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Coordinated Land Use and Transportation Planning – A Sketch Modelling Approach
by
Marcus J. Williams
A thesis submitted in conformity with the requirements for the degree of Masters of Applied Science
Department of Civil Engineering University of Toronto
© Copyright by Marcus J. Williams 2010
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Coordinated Land Use and Transportation Planning –
A Sketch Modelling Approach
Marcus J. Williams
Masters of Applied Science
Department of Civil Engineering
University of Toronto
2010
Abstract
A regional planning model is designed to facilitate coordinated land use and transportation
planning, yet have a sufficiently simple structure to enable quick scenario turnaround. The
model, TransPLUM, is built on two existing commercial software products: the Population and
Land Use Model (PLUM); and a four-stage travel model implemented in a standard software
package. Upon creating scenarios users are able to examine a host of results (zonal densities,
origin-destination trip flows and travel times by mode, network link flows, etc) which may
prompt modification of a reference land use plan and/or network plan. A zonal density-
accessibility ratio is described: an index which identifies the relative utilization of a zone and
which could serve as a coordinating feedback mechanism. The model was implemented for a
pilot study area – the Winnipeg Capital Region. Development of a baseline scenario is
discussed.
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Acknowledgments
There are many people who helped make this project happen.
First, I would like to acknowledge Pille Bunell (Royal Roads University) and Arne Elias (Centre
for Sustainable Transportation) for connecting me with staff at the City of Winnipeg. Everyone I
worked with at the City went out of their way to contribute: Dianne Himbeault, Michelle
Richard, David Houle, Bill Menzies, Bjorn Radstrom, Phil Wiwchar, Doug Hurl, Brett Shenback
and of course Susanne Dewey-Povoledo. Susanne saw the value in this project from the onset,
became the internal champion and assembled the necessary cross-departmental team for buy-in
and implementation.
Virgil Martin, a PLUM user extraordinaire at the Region of Waterloo, has always been
supportive of PLUM‘s use in other municipalities and provided valuable advice during this
project.
whatIf? Technologies, through NSERC‘s Industrial Postgraduate Scholarship, provided not only
financial support but also software and expertise. I have Robert Hoffman, Bert McInnis (my
industry supervisor), Michael Hoffman and Shona Weldon to thank for this support.
The friends I have made at the University of Toronto are too numerous to name. They have
helped me through courses and thesis roadblocks. I have shared many meals and drinks with
them and look forward to sharing many more.
It is my good fortune that Eric J. Miller has trouble saying no, and therefore agreed to take me on
as a graduate student. Despite the great demands on his time, Eric always finds time for his
students. His guidance, support, wisdom and patience throughout this project were invaluable.
His instigation of Friday afternoon visits to O‘Grady‘s is appreciated; his knowledge of Star Trek
episode plots is most impressive.
Finally, my entire family has been extremely supportive of my decision to return to school and
for that I am eternally grateful. To my wife Mary – thank you – and now I can return to being a
full-time husband. To my daughter Emma – thank you for waiting until the completion of my
coursework to be born!
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Table of Contents
Acknowledgments ..................................................................................................................... iii
Table of Contents ...................................................................................................................... iv
List of Tables ........................................................................................................................... vii
List of Figures ......................................................................................................................... viii
Chapter 1 Introduction ................................................................................................................ 1
1 Introduction ........................................................................................................................... 1
Chapter 2 Literature Review ....................................................................................................... 3
2 Literature Review .................................................................................................................. 3
2.1 Introduction .................................................................................................................... 3
2.2 The Urban Transportation Modelling System (UTMS).................................................... 4
2.3 Integrated Urban Models / Land Use Models .................................................................. 5
2.3.1 Spatial Interaction / Lowry-type Models .............................................................. 6
2.3.2 Spatial Input-Output Models ............................................................................... 7
2.3.3 Microeconomic-based Urban Models .................................................................. 8
2.3.4 Other ―Sketch‖ Models: Rule-based, GIS and Public Engagement ....................... 9
Chapter 3 Problem Statement ................................................................................................... 11
3 Problem Statement and Approach ........................................................................................ 11
3.1 Problem Statement ........................................................................................................ 11
3.2 Modelling and Implementation Approach ..................................................................... 12
Chapter 4 Pilot Study Area – Winnipeg, Manitoba ................................................................... 15
4 Pilot Study Area – Winnipeg, Manitoba ............................................................................... 15
4.1 Overview ...................................................................................................................... 15
4.2 Data Sources ................................................................................................................. 17
Chapter 5 TransPLUM Description .......................................................................................... 21
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5 TransPLUM Description ...................................................................................................... 21
5.1 Overview ...................................................................................................................... 21
5.2 Overview of the whatIf? Modelling Platform ................................................................ 23
5.3 PLUM Description ........................................................................................................ 26
5.3.1 Population and New Dwelling Demand ............................................................. 27
5.3.2 Land Use Plan and Allocation Mechanism ........................................................ 29
5.3.3 Employment ...................................................................................................... 31
5.3.4 PLUM‘s Suitability for Sketch Planning ............................................................ 32
5.4 Travel Model Description ............................................................................................. 35
5.4.1 Travel Model Platform - TransCAD .................................................................. 35
5.4.2 General Travel Model Information .................................................................... 36
5.4.3 Trip Generation ................................................................................................. 38
5.4.4 Trip Distribution ............................................................................................... 40
5.4.5 Mode Split ........................................................................................................ 43
5.4.6 Trip Assignment ................................................................................................ 49
5.4.7 Travel Model Outputs Returned to whatIf? Platform ......................................... 53
5.4.8 Travel Model‘s Suitability for Sketch Planning ................................................. 53
5.5 TransPLUM run-time performance ............................................................................... 53
Chapter 6 Baseline Scenario ..................................................................................................... 55
6 Baseline Scenario ................................................................................................................ 55
6.1 Population, Dwellings and Employment ....................................................................... 56
6.2 Land Use Plan and Allocation ....................................................................................... 58
6.3 Travel ........................................................................................................................... 65
Chapter 7 Coordination Approaches ......................................................................................... 69
7 Coordination Approaches .................................................................................................... 69
7.1 Feedback Paradigms ..................................................................................................... 69
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7.2 Land Utilization – the Density-Accessibility Ratio ........................................................ 71
7.2.1 Concepts ........................................................................................................... 71
7.2.2 Provisional Results ............................................................................................ 73
Chapter 8 Conclusion ............................................................................................................... 79
8 Conclusion ........................................................................................................................... 79
8.1 Summary of Contributions ............................................................................................ 79
8.2 Evaluation..................................................................................................................... 79
8.3 Future Work and Improvements .................................................................................... 80
8.3.1 Generic Model .................................................................................................. 80
8.3.2 Specific Winnipeg Implementation .................................................................... 81
References ................................................................................................................................ 83
Appendix A: Survey Trip Purpose to Model Trip Purpose Mapping ......................................... 87
Appendix B: Trip Distribution Validation Scatterplots.............................................................. 89
Appendix C: Trip Mode Classification Rules ............................................................................ 90
Appendix D: Mode Choice Model Estimation Results .............................................................. 92
Appendix E: Availability Restrictions on Transit and Walk-Bike Modes .................................. 96
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List of Tables
Table 5-1: Key time and geographic model informants ............................................................. 27
Table 5-2: Key population and dwelling demand model informants .......................................... 28
Table 5-3: Key employment informant ..................................................................................... 32
Table 5-4: Trip generation driver definitions. The unit of population is persons; the unit of
employment is jobs. .................................................................................................................. 38
Table 5-5: Base-year AM peak trips made by Winnipeg residents. ........................................... 39
Table 5-6: Base-year AM peak-hour trip generation rates, in trips per driver unit. Drivers are
defined in Table 5-4. ................................................................................................................ 39
Table 5-7: Calibrated inverse function gravity parameters by trip purpose ................................ 41
Table 6-1: Summary of inputs to baseline capacities calculation. .............................................. 61
Table 6-2: Total baseline scenario capacities for the entire Winnipeg Capital Region. .............. 62
Table A-1: Survey trip purpose to model purpose mapping where zone of trip origin is the home
zone of the trip maker. .............................................................................................................. 87
Table A-2: Survey trip purpose to model purpose mapping where zone of trip origin is note the
home zone of the trip maker. .................................................................................................... 87
Table C-3: Modes recorded in 2007 Winnipeg Area Travel Survey .......................................... 90
Table C-4: Mapping from surveyed modes to modelled modes ................................................. 91
Table E-5: Modelled availability restrictions on the transit mode .............................................. 96
Table E-6: Modelled availability restrictions on the walk-bike mode ........................................ 96
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List of Figures
Figure 3-1: TransPLUM conceptual system diagram ................................................................ 13
Figure 4-1: Map showing location of Winnipeg, Manitoba. Source: openstreetmap.com. .......... 16
Figure 4-2: Map of the 327 traffic zones comprising the study area (Winnipeg Capital Region).
Zones within the City boundary are hatched; ―outer ring‖ zones are shaded. Source: City of
Winnipeg, Public Works Department. ...................................................................................... 17
Figure 4-3: 2008 Winnipeg Capital Region road network ......................................................... 20
Figure 5-1: TransPLUM detailed system diagram ..................................................................... 22
Figure 5-2: Top-level model diagram in the whatIf? platform ................................................... 23
Figure 5-3: Example of the whatIf? standardized model logic diagram – the Population calculator
(sub-model) .............................................................................................................................. 24
Figure 5-4: Native data visualization tools within the whatIf? Platform .................................... 25
Figure 5-5: Total person-trips by time of day. 8-9AM peak hour is shaded in red. Source: 2007
Winnipeg Area Travel Survey. ................................................................................................. 37
Figure 5-6: Observed and predicted trip length distributions for AM peak home-to-work trips. 42
Figure 5-7: Observed AM peak-hour mode shares by trip purpose. Source: 2007 Winnipeg Area
Travel Survey. .......................................................................................................................... 43
Figure 5-8: Mode share vs. trip distance for AM peak hour home-to-work trips. Source: 2007
Winnipeg Area Travel Survey. ................................................................................................. 44
Figure 5-9: Predicted AM peak-hour mode shares by trip purpose. ........................................... 48
Figure 5-10: Base-year scaled-symbol auto flow map ............................................................... 51
Figure 6-1: Comparison of Winnipeg TransPLUM baseline scenario to the Conference Board‘s
demographic and economic forecasts. ....................................................................................... 57
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Figure 6-2: Example stand-alone capacity calculation, shown for the major redevelopment
component of residential reurbanization. pz is the geographic index PLUM zone; dt is the index
for dwelling type. ..................................................................................................................... 60
Figure 6-3: Thematic density maps of Winnipeg TransPLUM baseline scenario. All densities are
calculated using gross zonal areas. ............................................................................................ 63
Figure 6-4: Projected capacity deficits for the baseline scenario. ............................................... 64
Figure 6-5: Baseline mode share projection, AM peak hour. ..................................................... 65
Figure 6-6: Baseline total person travel time over time by mode, AM peak hour. ...................... 66
Figure 6-7: Baseline auto travel times from various zones to zone 201 (Winnipeg CBD), AM
peak. ........................................................................................................................................ 67
Figure 6-8: Thematic employment accessibility maps of Winnipeg TransPLUM baseline
scenario. Accessibility is measured in number of jobs accessible within 30 minutes during the
AM peak hour. ......................................................................................................................... 68
Figure 7-1: Planner feedback scheme based on zonal utilization. .............................................. 71
Figure 7-2: Thematic map of utilization indicator from Winnipeg TransPLUM 2006 base year.
AM peak hour accessibilities used. ........................................................................................... 74
Figure 7-3: Example of two zones with median utilization values. ............................................ 75
Figure 7-4: Example of downtown zone with low utilization value. .......................................... 76
Figure 7-5: Example of a low-density suburban zone near City boundary. ................................ 77
Figure 7-6: Zonal utilization indicator values for the baseline scenario, projected over time. .... 78
Figure A-1: 3D barplot of trip frequency by survey trip purpose. AM peak hour trips only. ...... 88
Figure B-2: Predicted vs. observed trip flows for super-zone (17 x 17) interchanges. ................ 89
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Chapter 1 Introduction
1 Introduction
There is a relationship between urban land use and transportation, two of many ―layers‖ in an
urban system. Land use patterns – where people live, work, shop and play – influence travel
patterns and the evolution of transportation infrastructure. At the same time, transportation
systems provide accessibility and influence where people engage in activities, and also how
urban form changes. This circular relationship occurs in a complex, dynamic manner.
Land use and transportation planning in North America have in many respects operated
independently of each another, ignoring their natural link. The reasons for this are complex and
historically rooted. There are institutional and professional dichotomies (Meyer and Miller,
2001) which ―silo‖ what should be an integrated urban analysis into separate, weakly-linked
agencies. Perhaps, more fundamentally, this disjointed approach is a result of the dominant
paradigm in which ―near-ubiquitous automobile-based mobility has ‗loosened the bonds‘‖ of the
relationship (Miller et al. 1998, 6). The last few decades have begun to show major cracks in the
automobile-based planning paradigm as metropolitan areas grapple with issues of congestion,
energy, emissions, etc. Therefore, there is a pressing need for a return to a coordinated planning
approach.
Computable models have long had a role as planning support tools in the urban domain. Urban
travel models are standard fare in transportation planning departments and, to a much lesser
degree, formal land use models are employed by regional planning organizations. Much
criticism has been levied against the practice of urban modelling – arguably the most influential
is Lee‘s Requiem for Large-Scale Models (1973). While this project does not directly adopt
Lee‘s framing of the problem, it identifies and addresses two broad concerns regarding the
operational state of urban modelling tools. The first concern is the poor support for coordinated
(or integrated) land use and transportation planning offered by many of the tools in common use.
The second concern is that many operational models (integrated or not) are highly complex. The
result is a large effort required to generate and evaluate scenarios, which restricts the feasible
breath of scenario analysis a planning organization can cover within real-world time and
resource constraints.
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The purpose of this thesis is to develop an operational model to facilitate coordinated land use
and transportation planning, yet have a sufficiently simple structure to enable quick scenario
turnaround.
This project uses the Winnipeg Capital Region in Manitoba, Canada, as a pilot study area.
Winnipeg was deemed a suitable pilot area due to its medium-sized population and its relative
isolation from other large urban centres, simplifying the needed representation of external
factors.
The project received support through an NSERC Industrial Postgraduate Scholarship (IPS1)
along with the participation of an industry partner, whatIf? Technologies Inc.
The report is organized as follows. Chapter 2 provides a review of past and current land use and
transportation modelling tools. Chapter 3 establishes a role for this research in the context of
existing tools and their rate of adoption. Chapter 4 offers background information on the pilot
area, the Winnipeg Capital Region, and the sources of available data for the model. Chapter 5
describes the model developed in detail, while Chapter 6 describes outlines the construction of a
baseline scenario. Chapter 7 discusses an approach to coordinating land use and transportation
planning within the context of the developed model. Chapter 8 summarizes this project‘s
research contribution, evaluates its success and identifies areas for future work and improvement.
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Chapter 2 Literature Review
2 Literature Review
2.1 Introduction
Soon after the birth of electronic computers, starting in the 1950‘s, urban systems analysts began
harnessing the new information processing capabilities to create projections of alternate future
states – via computable mathematical models – as long-range planning aids. This modelling
approach was first applied to the urban transportation sub-system1, most notably by the Chicago
Area Transportation Study which assembled and developed the methods which became the basis
for the standardized and enduring framework known as the urban transportation modelling
system (UTMS) or the four-stage model (Black, 1990; Johnston, 2004).
Urban transportation planning was and continues to be the most active application area of
modelling within the urban systems analysis domain. Yet on the heels of the early transportation
modelling work there was a significant research effort occurring in models of urban land use,
starting in the 1960‘s, which forecast future configurations of urban form and the corresponding
spatial distributions of population and economic activity. Many of these land use models were
designed to interact with transportation models in that their spatial allocation processes were
influenced by transportation measures (e.g., zonal accessibilities) and their outputs could serve as
drivers for transportation models. This can be seen as early recognition of the strong land use -
transportation relationship on the part of professionals, along with a desire to formally
incorporate the link in the planners‘ toolkit. Despite these efforts land use models never achieved
the prominence of UTMS but rather experienced a decline and ―near-total abandonment‖ in the
1970‘s (Meyer and Miller, 2001), which may be attributed to the following factors:
1 This report considers only the land use and transportation components of urban systems. Other sub-systems exist
(e.g., water distribution, hydrological, etc.) and are subject to their own modelling disciplines.
4
U.S. federal transportation funding and legislation which required formal transportation
analysis, but no such requirement for land use (Meyer and Miller, 2001; Miller et al.,
1998; Johnston, 2004)
A dominant laissez faire market-driven development environment in North America, with
no perceived need to plan land use (Miller et al., 1998)
A general disillusionment with the rational model of planning and the style of models
built on that premise, along with the perceived failure of these models to address policy
questions (Lee, 1973).
Therefore, until quite recently few regions employed formal models to project land use inputs to
their transportation models.
There has been a revival in the field and a 2009 survey suggests that half of large- and mid-sized
U.S. metropolitan planning organizations (MPOs) are engaged in land use modelling (Lee,
2010). Yet there is still a sense among practitioners that land use models are immature with
respect to institutional integration and operational policy decision support (Kockelman, 2009).
In Canada, operational land use models are rarities; the preparation of land use projections is
most often an ad-hoc, judgment-based process which produces a single, fixed forecast. This
inhibits feedback of projected transportation conditions to land use plans, and is prone to
producing disjoint land use and transportation plans.
2.2 The Urban Transportation Modelling System (UTMS)
For transportation planning professionals the urban transportation modelling system (UTMS) or
the four-stage model is a universally-understood framework. Generally, it accounts for: person
trip flows within a region by origin, destination, purpose and mode; vehicle or passenger
volumes by network link; and travel times by network link and origin-destination (O-D) pair (or
interchange). Although it has undergone significant advancement over the past 50 years and
there are variations in its application, its structure remains largely unchanged.
The four stages of the UTMS are:
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1. Trip generation, in which the number of trip ends (productions and attractions) by zone
and trip purpose are projected, driven by some unit(s) of zonal activity (e.g., households,
population, employment) and their characteristics.
2. Trip distribution, in which trips by zone of origin are distributed to destination zones.
The standard approach employs a gravity model in which the trip flow for a given O-D
pair is positively influenced by levels of activity contained in the two zones, and
negatively influenced by the zone-to-zone impedance (travel time and/or cost).
3. Mode split. Here, the total trip flow between each O-D pair is split among the various
modelled modes (e.g., auto-drive, auto-passenger, transit, walk, bike, etc.) based on some
combination of modal and trip maker attributes.
4. Trip assignment, in which the modal O-D demands are loaded onto their respective
networks, traverse actual routes and yield flow rates on individual network links.
Some variation on the ordering of the above stages exists – specifically trip distribution and
mode split – as well as varying methodological sophistication of individual stages and inter-stage
feedback (i.e., equilibration). Further discussion of the UTMS is found in Ortuzar and Willumsen
(2001) and Meyer and Miller (2001).
It should be noted that UTMS is: static, as it represents travel over a particular time period with a
single state; and trip-based, as its primitive unit of travel demand is a point-to-point trip. The
limitations these features impose on travel analysis have spurred much research in dynamic and
activity/tour-based methods (Jones, 1990). At present, however, the four-stage model remains
the dominant framework for operational transportation policy analysis and planning.
2.3 Integrated Urban Models / Land Use Models
The term integrated urban model describes a model which brings together urban form and travel
analysis, and is sometimes used interchangeably with land use model. This is potentially
confusing because within the group of land use models the degree of integration with
transportation varies considerably. Some include, or connect to, fully-blown transportation
models; others incorporate transportation-related measures in a much more indirect, static
manner. The following Sections 2.3.1 - 2.3.4 present models which range from deep-integration
6
to stand-alone land use projection. The sections are: spatial interaction or Lowry-type; spatial
input-output; microeconomic-based; and various other ―sketch‖ models employing rule-based
methods and/or GIS platforms, several of which are oriented towards public engagement.
The following sections draw from various reviews of integrated urban models: Hunt et al.
(2005), Kosterman and Petit (2005), Miller et al. (1998), Miller (2009), Southworth (1995) and
Wegener (1995).
2.3.1 Spatial Interaction / Lowry-type Models
From a historical perspective the Lowry model (Lowry, 1964) is generally considered the most
influential land use model – the causal logic and spatial interaction concepts it employs are
widely used in subsequent generations of land use models (Horowitz, 2004).
The Lowry model is premised on the notion that a region‘s basic employment – employment that
serves markets outside the region – acts as an ―anchor‖ which determines the distribution of
regional population and service-based (i.e., local) employment. The nature of the distribution is
such that: population is concentrated in areas with high accessibility to employment, and service-
based employment is concentrated in areas with high accessibility to population and
employment. Spatial interaction of this type is described as gravity distribution, similar to the
gravity-based trip distribution procedure used in four-stage travel models but working with
population and employment rather than trip ends. The original Lowry model was specified as a
sequence of equations to be solved through an iterative procedure; however, it was later
reformulated by Garin (1966) as a matrix-based procedure which allows a direct solution (Meyer
and Miller, 2001).
The Lowry model can be run stand-alone but it is also well-suited to being connected to a travel
forecasting model (Horowitz, 2004). In this configuration The Lowry model provides population
and employment distributions – based on assumed travel impedances – to the travel model,
which calculates updated impedances to be fed back into the Lowry model. This loop is iterated
until equilibration.
A widely used Lowry-type integrated urban model is the Integrated Transportation and Land-Use
Package (ITLUP), which contains the Disaggregate Residential Allocation Model (DRAM) and
the Employment Allocation Model (EMPAL), developed by Putman (1995).
7
Lowry-type models are inherently static equilibrium-based, although they can be made quasi-
dynamic by adding increments of basic employment at successive points over a planning horizon
(Meyer and Miller, 2001). Due to the fact that Lowry-type models ―re-construct‖ a city based on
projections of basic employment, they are poor at taking base-year development into
consideration. However, relative to subsequent generations of urban models they have relatively
modest data requirements.
2.3.2 Spatial Input-Output Models
Based on the legacy of the Lowry model is a family of integrated models which further
articulates interactions among employment sectors and households, giving rise to activity
location and transportation demand.
Of this family the MEPLAN package – developed by Echenique (1990) – appears to have had
the most extensive regional application. MEPLAN employs a spatial input-output structure
which accounts for producers and consumers (called factors) of goods and services, their
interactions, and intensities (or technical coefficients). Households are included in this structure
as both producers and consumers. As producers they supply labour to employers (resulting in
work trips); as consumers they require goods and services (resulting in shopping, service,
delivery, etc. type trips). Land and floorspace are considered non-transportable producer-type
factors, serving households and employers.
Exogenous consumption and production – similar to the basic employment of the Lowry model –
serve as the starting point for expanding intermediate economic activity according to the input-
output coefficients. Production factors are allocated to zones using discrete choice models which
take into account zonal production costs (including land prices) and travel impedances to zones
of consumption. Land prices are determined endogenously through an iterative procedure which
aligns land demand (elastic to price) with land supply, specified by zonal capacity constraints.
Although each MEPLAN state is fundamentally a static equilibrium, the model provides a
simulated dynamic through the variation of exogenous consumption and land constraints over a
sequence of time periods. Furthermore, delayed behavioural responses are represented though
selected time lags – for example, activity location at time period t is influenced by travel model
impedances from the previous period, t-1.
8
A characteristic of MEPLAN which is telling of its fundamentally integrated nature is the fact
that a distinct trip distribution stage is not required for its travel model component – trip
distribution is a direct result of its core input-output social accounting structure.
There are two models which are direct descendents of MEPLAN: TRANUS (Modelistica, 2007);
and PECAS (Hunt and Abraham, 2003) which, according to a recent survey of U.S. MPOs (Lee,
2010), has an estimated market share of 9% (of the MPOs with land use models).
2.3.3 Microeconomic-based Urban Models
Much research in integrated urban model over the last two decades has been directed towards an
increasingly detailed representation of urban land markets, the relevant actors and the application
of microeconomic theory governing their behaviour. In addition, some of the resulting models
present dynamic, non-equilibrium based frameworks for evolving urban form. This section
discusses a selection of these models.
MUSSA (―Modelo de Uso de Suelo de Santiago‖) developed by Martinez (1996) is based on
bid-choice theory (Alonso, 1964; Ellickson, 1981) in which individual firms or households bid
for space up to a maximum value, or willingness to pay. Firms and households try to maximize
the difference between their willingness to pay and the rent they actually pay (consumer surplus);
and landlords rent to the highest bidder. The model assumes a static equilibrium in which supply
equals demand, all households are assigned dwellings and geographically located, and capacity
constraints are not exceeded. Households are finely disaggregated. The land use model
equilibrates in conjunction with a connected four-stage travel model. Another static equilibrium
model with a strong microeconomic orientation is METROSIM (Anas, 1995), which has been
applied to Chicago and New York City.
UrbanSim (Waddell et al., 2003) is an integrated model which has an estimated market share of
15% of U.S. MPOs (Lee, 2010), representing the urban model with the single largest installation
base. In many respects UrbanSim is influenced by MUSSA: buyers bid based on their
willingness to pay and attempt to maximize their surplus; sellers attempt to maximize price paid.
However, the equilibrium assumption is relaxed and the building stock is evolved through a
dynamic disequilibrium. While many of the actors in UrbanSim are highly disaggregated (e.g.,
9
households), workplace choice is made in a connected travel model. In other words, place-of-
residence to place-of-work linkages are not integrated across the land use and travel sub-models.
There have been several major research efforts into true agent-based microsimulation
frameworks in which individual persons, households, firms, buildings, dwellings, vehicles, etc.
evolve and interact explicitly in a dynamic, non-equilibrium, integrated framework. Examples of
such models are ILUTE (Salvini and Miller, 2005), PUMA (Ettema et al., 2007) and ILUMASS
(Strauch et al., 2003). These models offer the potential to explore and simulate the behaviour of
urban socio-economic systems at an extremely fine level of detail and fidelity; to date they have
been exercised in academic, rather than operational planning environments.
2.3.4 Other “Sketch” Models: Rule-based, GIS and Public Engagement
There are many examples of ―lightweight‖ or ―sketch‖ urban planning support tools – at least
relative to the model classes presented in the preceding Sections 2.3.1 - 2.3.3 – which employ
less data-intensive and/or less theory-rich approaches in favour of some combination of: rapid
scenario turnaround, impact analysis, visualization and community engagement / consensus
building.
The California Urban Futures (CUF) land use model (Landis, 2001) is an example of a rule-
based approach. A detailed spatial database consisting of environmental, market and policy
layers is processed to define a collection of irregular developable land units. These units are
scored and sorted according to the potential profitability attributed to their development.
Regression-based projections of population growth, at the municipal level, are allocated to the
developable land units in sequential order according to their profitability ranking. A subsequent
generation of the model, CUF II, replaces the profitability-driven allocation process with
statistical state-change models applied to 1-hectare grid cells.
The Ohio-based What if? software package (Klosterman, 2001) – not to be confused with the
whatIf? Modelling Platform used in this project2 – is similar to the CUF model in that it adopts a
2 The What if? urban planning support system (www.whatifinc.biz) is developed by Richard E. Klosterman,
Professor Emeritus at the University of Akron. whatIf? Technologies Inc. (www.whatiftechnologies.com) is an
Ottawa-based consulting firm and developer of the whatIf? Modelling Platform used in this project. The two firms
10
rule-based allocation method, but is oriented towards a user-friendly GIS interface for
determining the relative suitability of locations for development.
UPlan (Walker et al., 2007) is another GIS-based land use allocation model which operates at a
highly resolved geographic scale – 50 x 50 m grid cells. Each cell is assigned a composite
development attractiveness value based on proximity to transportation and other infrastructure.
Exogenous population and employment growth projections drive the demand for new land
development which is allocated to cells based on their attractiveness.
There are several software tools geared towards visualization of land use scenarios and impact
assessment for public engagement: Index (Allen, 2001), Community Viz (Kwartler and Bernard,
2001), PLACE3S (Hanson and McKeever, 2009), and MetroQuest (2010). Community Viz is
noted for its ability to generate 3D bird‘s-eye views of potential land use scenarios. PLACE3S
and MetroQuest offer web-based access through which members of the public can directly
explore scenarios and impacts. These packages are generally built on GIS platforms.
The tools listed in this section are generally not tightly integrated with travel models; rather they
are used as stand-alone packages which accept travel-related measures from or output land use
results to travel models, but do not explicitly close the land use-transportation loop. One
exception is UPlan, whose design allows (but does not require) a direct interface to travel
models.
and their platforms are not related; the similar product names were independently trademarked in the U.S. and
Canada in the 1980-90s.
11
Chapter 3 Problem Statement
3 Problem Statement and Approach
3.1 Problem Statement
Chapter 2 briefly describes the history and current state of integrated land use and transportation
modelling tools. While significant research and development effort has been invested in these
tools – large-scale integrated models (Sections 2.3.2 - 2.3.2), and also land use-oriented sketch
models (Section 2.3.4) – there appears to be a dearth of contemporary tools with both an
integrated and sketch orientation. This observation matters because it identifies a largely
underserved segment of model offerings, which, if filled could provide better planning support to
regions.
Large-scale integrated models by definition provide an integrated view of the land use -
transportation planning problem but require large volumes of data and significant human
resources to operate, often making them ineffective for multi-scenario analysis within the
budgets and time constraints of real-world planning initiatives. In a recent survey of Canadian
planning agencies, Hatzopoulou and Miller (2009) cite a lack of resources as one of the major
challenges facing institutions with respect to urban modelling. Sketch-type land use models, on
the other hand, are suited to quicker scenario turnaround and are less resource intensive but
generally provide a partial analysis, failing to adequately address the land use - transportation
link.
Therefore, this project attempts to develop a model to enable quick-turnaround, yet coordinated
land use and transportation scenario analysis at the regional scale. It aims to combine a judgment
and scenario-based process with the rigour of a dynamic, quantitative accounting framework.
A point of note regarding word choice in the above statement: coordinated is used here, and also
in the title of this report rather the more common qualifier integrated found in the literature
within this context. Integrated is sometimes used in a specific model-structure sense to describe
models in which location choice (e.g., residential) and trip destination (e.g., for home-to-work
trips) are generated from the same underlying relationships (e.g., place-of-work to place-of-
12
residence linkages). While this specific, technical meaning of integrated does not describe this
project‘s chosen sketch approach, outlined in the following Section 3.2, the broader meaning of
the term is certainly applicable to this project‘s goals. Ultimately it was felt that coordinated
conveys much of the same holistic intent as integrated without the specific model architecture
implications.
3.2 Modelling and Implementation Approach
Several premises guided the development of the solution:
1. The model can be constructed largely based on existing methods and software
technology; as such, much of the project can be viewed as an exercise in design and
integration, as opposed to more basic research into model sub-components.
2. Interface matters. As far as possible the model interface should be transparent with
respect to model structure, data and assumptions. Scenarios should be readily created,
debriefed, compared to each other and modified. These attributes enhance the credibility
of any planning model, and also the level of productivity offered.
3. The core should be largely ―agnostic‖ with respect to behaviour – in essence it should be
an accounting framework upon which users can construct interchangeable, alternate
future scenarios (Gault et al., 1987).
Therefore, the chosen approach builds on a pre-existing land use model and platform – the
Population and Land Use Model (PLUM) developed by whatIf? Technologies Inc. – and
connects to a conventional 4-stage travel model. The combined solution is called TransPLUM.
13
Strategic Policy Controls
Population and
Land Use Model
(PLUM)
4-stage travel
model
Land Use Plan
Multi-modal
Network Plan
population &
employment
allocations
Planner Feedback
Figure 3-1: TransPLUM conceptual system diagram
Figure 3-1 presents a conceptual diagram of the approach, which exposes two main classes of
policy control variables to the user: the Land Use Plan and the Multi-modal Network Plan.
The Land Use Plan is a geographically-explicit set of parameters which reflects a zoning (type
and intensity of development) and phasing (relative sequencing of development) scheme. When
the plan is applied to a projected stream of regional development in PLUM, the result is a time-
varying land use projection and an associated population and employment allocation. The
allocation is passed to the travel model, which – in combination with an evolving Multi-modal
Network Plan – projects a sequence of future system travel states.
The configuration described above constitutes a core, a-cyclical framework for projecting future
land use and travel states. The dotted-line connection labeled Planner Feedback in Figure 3-1
represents the discretionary capability of the user to adjust a reference land use - transportation
plan combination in response to its expected outcome. This connection offers a means of
coordinating land use and transportation plans, but intrinsically it neither enforces nor prescribes
a coordination scheme. In this sense, the core approach can be thought of as descriptive rather
than normative.
14
PLUM is implemented on the whatIf?® Modelling Platform which also serves as the integrating
platform and primary user interface for TransPLUM, due to its model structure diagrammatics,
multi-dimensional array language, data visualization and scenario management capabilities.
15
Chapter 4 Pilot Study Area – Winnipeg, Manitoba
4 Pilot Study Area – Winnipeg, Manitoba
4.1 Overview
One of the project‘s goals is that the model structure is applicable to an arbitrary region,
consistent with the commercialization guidelines of the NSERC Industrial Postgraduate
Scholarship. It was determined that the participation of a pilot region would be beneficial – a key
factor in the market-readiness of the product – with respect to data availability but also with
respect to the credibility gained from working with a real ―client‖. As a result the City of
Winnipeg, Manitoba (shown on a map in Figure 4-1) was engaged as a project partner and pilot
municipality. Winnipeg was deemed to be a suitable choice due to its medium size – it is the 7th
largest Canadian municipality by population (Statistics Canada, 2010). Also, its relative isolation
from other large urban centres means that it approximates a ―closed system‖ with respect to
commuter travel, simplifying the representation of externally generated travel demand.
The 2006 Census of Canada population count for the City of Winnipeg is 633,000. The larger
Winnipeg Capital Region – the City plus 15 surrounding towns and rural municipalities – has a
count of 732,000 (Statistics Canada, 2007). It is this Capital Region which defines the study area.
The rationale for this choice is: the Capital Region represents most of the catchment area for trips
to and from the City; and much of the land expected to absorb future regional development falls
outside City boundaries but within the Capital Region. A map of traffic zones comprising the
study area is shown in Figure 4-2.
During the 1990‘s the region experienced low and even negative population growth rates. The
last decade has shown modest population growth, and from 2009 to 2031 approximately 220,000
additional people are projected for the region by the City of Winnipeg - Office of the CFO
(2009). The City and surrounding municipal governments face the challenge of managing this
growth with respect to built form, but also with respect to sustainable transportation
infrastructure. Currently, private automobile is the dominant mode of travel, and public transit is
provided by a conventional bus transit system. Construction is underway on a bus rapid transit
16
corridor in the Southwest quadrant of the city; however, there is active debate as to the extent
and type of rapid transit coverage which should be built for the rest of the city.
Figure 4-1: Map showing location of Winnipeg, Manitoba. Source: openstreetmap.com.
Winnipeg, Manitoba
Map image:
openstreetmap.com
17
Figure 4-2: Map of the 327 traffic zones comprising the study area (Winnipeg Capital
Region). Zones within the City boundary are hatched; “outer ring” zones are shaded.
Source: City of Winnipeg, Public Works Department.
4.2 Data Sources
The major data sources relevant to the Winnipeg model are described as follows:
2006 Census – custom tabulations in Winnipeg traffic zones. The City of Winnipeg
obtained from Statistics Canada a variety of population, dwelling, household and
employment data from the 2006 Census, custom-tabulated to the City‘s traffic zone
system (zones shown in Figure 4-2). Some zones are consolidated in order to minimize
data suppression for cross-tabulated datasets, but population and employment count totals
18
are provided in the full non-consolidated traffic zone geography. These data provide key
base-year distributions for the model.
CANSIM and historical Census data – standard Census geographies. Statistics
Canada‘s CANSIM is a key source of historical time-series data. In particular it contains
age-profiled population, migration, fertility, mortality and employment data. Typical
CANSIM dissemination geographies are relatively coarse – Census Metropolitan Areas
(CMAs) and Provinces – therefore CANSIM datasets are subject to scaling and
adjustment in preparing estimates for the study area. Historical Census data, available at
5-year intervals, provide periodic control points in the assembly of a historical
demographic database. Used at the Census Subdivision (CSD) geography the Census data
can be aggregated to directly match the study area. These data are important for historical
analysis or calibration of the regional population model.
2006 City of Winnipeg Assessment Parcel Database. For this project the City made
available its GIS-based parcel database of approximately 207,000 records. Key attributes
are predominant parcel use (of which there are 119 codes) and number of dwelling units.
Also available is the related Commercial and Industrial Buildings database which
includes attributes for building footprint area, number of stories, year built and
construction type. However, as its name suggests, it excludes several types of place-of-
employment buildings such as schools, hospitals, libraries and (surprisingly) hotels. Both
these datasets are confined to properties and buildings within City boundaries, leaving a
data gap for the ―outer ring‖ rural municipality zones. The parcel dataset offers an
alternate or supplementary source of housing stock data to the traffic-zone tabulated 2006
Census data. The Commercial and Industrial Building database provides a partial source
of employment floorspace data. The fact that individual parcels are provided as discrete
geo-referenced objects offers great flexibility in tabulating this data to an arbitrary zone
system. However, the assessment database was not used in the final model presented in
this report due to discrepancies with Census data3 and insufficient time in which to
address them.
3 A common challenge for land use analysts. Also noted by Martin (2010), another PLUM user.
19
2007 Winnipeg Area Travel Survey. The City commissioned an ―origin-destination‖
travel survey which was conducted in the Fall of 2007. It sampled over 15,000
households in Winnipeg and within a 100 km radius, representing approximately 4.4% of
the City‘s households. It provides complete coverage of the Capital Region study area,
but with one caveat: it represents trips made within, to or from Winnipeg but not those
occurring exclusively within the ―outer ring‖. Results of the survey are described by
iTRANS Consulting Inc. (2009).
2008 road network. Winnipeg‘s Public Works Department maintains a detailed GIS-for-
Transportation road network within the TransCAD®
software environment, shown in
Figure 4-3. It includes highway, arterial and local road classifications; and also link
attributes such as speed limit, free-flow speed, vehicle capacity and volume counts (on
selected links). The network extends beyond City boundaries to cover the study area. In
addition, Public Works maintains a database of proposed future road improvements in the
same format.
2007 transit data. Winnipeg Transit, operator of the City‘s bus-only public transit
service, maintains a detailed database of geo-coded stops, routes and schedules. In
addition, its bus fleet is equipped with automatic vehicle locator (AVL) and automatic
passenger counter (APC) technology which enables the collection of detailed ridership
and utilization records. Transit data from the Fall 2007 booking was selected for use in
this project due to its coincidence with the 2007 Winnipeg Area Travel Survey. The
database is described by Winnipeg Transit (2006).
20
Figure 4-3: 2008 Winnipeg Capital Region road network
21
Chapter 5 TransPLUM Description
5 TransPLUM Description
5.1 Overview
Section 3.2 introduced the modelling and implementation approach of TransPLUM. This section
provides a richer description of the platform and the model.
Figure 5-1 below presents a further articulated system diagram than the conceptual Figure 3-1,
showing the main sub-components of PLUM, the 4-stage travel model and the primary
information flows among the components. Items in the upper half of the diagram constitute
PLUM (with the exception of the Multi-modal Network Plan policy control); the lower half
represents a 4-stage travel model. This diagram serves as an important reference throughout
chapter 5.
22
Strategic Policy
Controls
Households
New Dwellings
Required
Allocate New
Dwellings
Residential
Greenfield &
Reurbanization
Development
Base Dwellings
Stock
Geographically
Distributed
Population
Regional
Population
Projection
Residential Land
Use Plan
Employment
New
Employment
Space Required
Allocate New
Employment
Space
Employment
Greenfield &
Reurbanization
Development
Base
Employment
Stock
Geographically
Distributed
Employment
Employment
Land Use Plan
Trip Generation
Multi-modal
Network Plan
Trip Distribution
Mode Split
Trip AssignmentBase Multi-
Modal Network
PLUM
4-stage travel model
Planner
Feedback
Figure 5-1: TransPLUM detailed system diagram
23
5.2 Overview of the whatIf? Modelling Platform
Before delving into the specifics of TransPLUM it is worth providing some description of the
whatIf? Modelling Platform, PLUM‘s native modelling environment and the integrating platform
selected for the implementation of TransPLUM.
Figure 5-2 shows a partial view of TransPLUM‘s top-level model organization diagram in the
whatIf? software platform. Shaded boxes represent sub-models, or calculators. The white boxes,
the oval-shaped node and connecting arrows simply provide a hierarchical organizational
structure for the calculators and have no bearing on the model‘s logical content. Many elements
in the implementation-level Figure 5-2 map to blocks in the system diagram, Figure 5-1.
Figure 5-2: Top-level model diagram in the whatIf? platform
Figure 5-3 shows the internal structure of the Population calculator, an example of the
standardized whatIf? model logic diagrams. Note:
Vertical cylinders represent stock variables, horizontal cylinders represent flow variables
and hexagons represent parameter variables.
24
Rectangles represent procedures and contain readily-viewable code for transforming
input variables into outputs. The code employed is a multi-dimensional array
manipulation language called TOOL.
The names of the data objects are followed by square brackets which contain a list of
codes. These codes, termed informants, identify classifying dimensions across which a
variable is defined and can be used across multiple variables. For example, the informant
a is a classifying age sequence, in this case from 0 to 100+ in single years of age. The
stock-type variable population indexed with [s,ts,a] is a 3-dimensional array object
defined across sex, time and age.
Figure 5-3: Example of the whatIf? standardized model logic diagram – the Population
calculator (sub-model)
The population variable in Figure 5-3 has several associated shaded tags. These indicate that
population is a shared variable, used in other calculators within TransPLUM, and these tags can
be used to navigate directly to those calculators.
25
Figure 5-4 (a), (b) and (c) show the data visualization options built into the platform – graph,
table and geographic displays – instantly accessible by clicking on variables in the model
diagram. Figure 5-4 (d) shows a comparison of two scenarios within a graph display. In addition
to these native display tools the platform provides data interchange capability with other standard
analytical tools such as spreadsheets and GIS software.
(a) Graph display
(b) Table display
(c) Geomap display
(d) Scenario comparison display within graph
Figure 5-4: Native data visualization tools within the whatIf? Platform
The platform natively supports scenario management. Each variable can be assigned multiple
instances (or assumptions); therefore, a scenario is the specification of a particular instance for
each variable in a model.
26
The platform offers an integrated scripting environment for calculations which occur outside the
―hard coded‖ model logic in the diagrams, useful for pre- and post-processing tasks. These
scripts, known as views, are written in the same TOOL language contained in the diagram‘s
procedure boxes.
The nature of developing and modifying models in the whatIf? Modelling Platform is one of
―drag and drop‖ diagram operations, coding and informant specification. This flexibility was
used to customize the pre-existing PLUM structure to the Winnipeg application, as well as
extend the logic to ―wrap around‖ a travel model.
5.3 PLUM Description
The Population and Land Use Model (PLUM)4 is an operational model developed by whatIf?
Technologies Inc.5 in close cooperation with the Region of Waterloo, Ontario, where the model
actively supports growth management policy analysis. PLUM was commissioned in 2000 and
has since evolved through numerous versions (Martin, 2009); it has also been applied to the
Region of Peel (Ontario, Canada) and the State of Victoria, Australia (Baynes et al., 2009), in
modified form. Much of PLUM‘s structure is adapted from the firm‘s earlier work on the
broader Waterloo Region Planning Framework (Bish and Hoffman, 1993). In the following
description, where project- and Winnipeg-specific requirements resulted in notable variations
from other PLUM implementations the model will be referred to as Winnipeg PLUM.
PLUM generates regional population and employment projections and in conjunction with user-
specified land use policies it produces land use projections and associated population and
employment allocations.
The fundamental time and geographic informants (or dimensions) used by Winnipeg PLUM are
listed in Table 5-1. Simulation time is the time horizon over which the model projects urban
states. Historical time is the period over which the model captures internally-consistent
historical demographic data. Both the simulation and historical time periods have respective
4 This is different from the legacy Projective Land Use Model (PLUM) by Goldner (1968).
5 whatIf? Technologies Inc. is an Ottawa, Ontario based modelling consultancy and developer of the whatIf?
Modelling Platform. www.whatiftechnologies.com.
27
starting points, or base years. PLUM Zone is the geographic zone system in which the land use
model operates. These informants are shared with the travel model – a convenient design
decision for bridging the data connection.
Table 5-1: Key time and geographic model informants
Informant Name Description
Simulation time 2007 to 2056 in one-year steps
Historical time 1992 to 2006 in one-year steps
Base year 2006
Historical base year 1991
PLUM Zone The primary geographic system; the 327-zone traffic zone
system shown in Figure 4-2
The following Sections 5.3.1 - 5.3.3 describe the flow of model logic presented in the PLUM
portion of the system diagram in Figure 5-1. In these sections italicized text generally refers to
specific boxes in the diagram.
5.3.1 Population and New Dwelling Demand
PLUM‘s sequence of calculations starts with Regional Population Projection. A population
cohort-survival model generates a population forecast for the entire regional study area (i.e., no
geographic disaggregation) over the model‘s 50-year simulation time horizon. Variables are
stratified by age and sex, and the model accounts for the standard components of change:
immigration, emigration, births and deaths. The cohort-survival method used by PLUM evolves
the population – one year at a time – from a known starting point (the base year) by shifting the
population of each age-and-sex cohort forward by a year, subject to assumed age-and-sex
specific mortality rates (hence the ―survival‖ label). Births are calculated using assumed
mothers‘ age-specific fertility rates. Assumed age-and-sex stratified immigration and emigration
flows are added to and subtracted from the regional population. Note that the cohort-survival
model diagram is shown as the example in Figure 5-3. Next, the population projection is split
between population in collectives (e.g., nursing and retirement homes) and population in
dwellings, using exogenous age-and-sex related propensities.
Within Households, a household formation rate is applied to the population in dwellings to yield
a projection of households by household size. In Winnipeg PLUM, households are treated as
28
equivalent to dwellings6 and so a projection of total regional dwellings required (i.e., total
demand) is available.
The calculation of New Dwellings Required incorporates the projected total demand for
dwellings and the Base Dwellings Stock by dwelling type. Required assumptions include the mix
of new dwelling types and base stock removal rates (i.e., demolition rates). An accounting
procedure determines the stream of new dwellings needed to keep the total stock supplied
commensurate with the total stock demanded, and the composition of that stream is set by an
assumed mix of new dwelling types.
Table 5-2: Key population and dwelling demand model informants
Informant Name Description
Age 0 to 100+ in single-year-of-age increments
Household size 1 to 6+ in single-persons-per-household increments
Dwelling type Set:
- Single detached
- Semi-detached or duplex
- Row house or townhouse
- Apartment, up to 4 storeys
- Apartment, 5 or more storeys
The population portion of PLUM is ―calibrated‖ such that the same model structure used for
simulation is run over historical time to generate an internally-consistent historical time series.
This historical series is useful for trend analysis (e.g., shifting fertility by mothers‘ age,
increasing retirement age). In the case of Winnipeg PLUM the ―closure error‖ method of
population calibration is used. In this method the population cohort-survival model is
sequentially run on 5-year historical segments, and the resulting year-5 age-sex stratified
population is compared to the observed Census count for the same year. The difference – termed
the closure error – is reduced to zero (within a specified tolerance) by iteratively adjusting inputs
to the population model. The available Statistics Canada data on births and deaths were taken to
be more reliable than the available immigration and emigration data for the study area.
Therefore, net immigration was treated as the free variable and adjusted so that the error
converged to zero.
6 The PLUM structure allows for a non-unity household-to-dwelling formation rate (e.g., recreational homes, multi-
household dwellings) but in practice this is set to one.
29
While PLUM also allows for calibration of the dwelling demand model structure, there were
challenges in reconciling various CANSIM and historical Census datasets for the Winnipeg
Capital Region against the 2006 Census custom-tabulated data. This led to the decision to
override the model‘s base year dwelling stock with the custom-tabulated data, rather than use the
evolved stock from the historical model.
Key population and dwelling informants are presented in Table 5-2.
5.3.2 Land Use Plan and Allocation Mechanism
At the heart of PLUM is an allocation mechanism which takes a regional projected stream of
New Dwellings Required and distributes it to the model‘s geographic zone system, over the
simulation time horizon, according to a user-specified Residential Land Use Plan.
For each zone in the study area the land use plan specifies two main variables:
Capacity, also called mature state, is a measure of a zone‘s potential for development. It
is stated in number of dwelling units and answers the question ―How many dwelling units
could this zone contain if fully built out?‖ On its own, capacity does not determine when
or even if a zone will experience development.
Priority is a ranking parameter which controls when a zone accepts development,
relative to other zones. Zones with higher priority receive development before those with
lower priority. Multiple zones can be assigned the same priority level, in which case they
receive development simultaneously in proportion to their available capacity.
Both of the above variables are judgement-based policy controls which can be informed by
alternate zoning schemes, density targets and phasing assumptions. The allocation mechanism
also provides additional ―tweaking‖ parameters for finer control of the process, such as a zonal
fill speed regulator, if desired.
The reader will recall from Section 5.3.1 that the New Dwellings Required demand is stratified
by dwelling type (see Table 5-2 for dwelling type categories). Before this demand is
geographically allocated an additional classification is applied – a split between greenfield and
reurbanization type development. Greenfield development is that which occurs on previously
30
un-serviced land; reurbanization occurs in already-developed areas, typically through infill or
redevelopment. The split is applied as an exogenous share variable, by dwelling type.
In Winnipeg PLUM, the resulting new dwellings demand stream is classified 10 ways (5
dwelling types by 2 development types) and in fact there are 10 corresponding independent
allocation processes and land use plans. There are two distinctions between the greenfield and
reurbanization allocation processes worth noting:
Reurbanization dwelling stocks are pre-filled with base-year dwelling counts; at the first
simulation time period their available capacities equal the difference between their
capacities and their base year levels. In other words, reurbanization capacity includes the
base stock level. By definition, no greenfield dwellings exist in the base year – greenfield
development is a future-only model concept – and as such greenfield dwelling stocks
begin the simulation period empty.
The reurbanization allocation process accepts projected dwelling removals from the base
stock (by zone and by dwelling type); this means that new available capacity may
become available due to removals. The greenfield allocation process does not allow for
stock removals.
Should the allocation process not have sufficient capacity to meet demand, this condition is
reported via a deficit output variable. A non-zero deficit implies an infeasible scenario, to which
the user may respond by adjusting the demand stream and/or the planned zonal capacities.
In the final step of the allocation process the Geographically Distributed Population is
calculated. This is achieved by applying an estimate of persons per dwelling unit (by dwelling
type, by zone) to the already-allocated dwelling units to yield estimated population by zone. This
estimated population is then uniformly scaled so that its total matches the control total from the
Regional Population Projection. Note that the allocated population is not stratified by age and
sex; it is provided as total population by zone.
PLUM can also account for ―recently-built‖ stock – i.e., development which occurs since the
most recent census count, monitored through municipal building records – although this was not
utilized in the pilot version of Winnipeg PLUM.
31
5.3.3 Employment
The preceding Sections 5.3.1 and 5.3.2 describe the population (top) and residential (left side)
components of the PLUM system diagram in Figure 5-1. The right side of the diagram represents
employment projection and allocation which essentially parallels the residential process, as
suggested by the symmetrical diagram layout. Where the residential process allocates dwellings
and population, the employment process allocates employment-related floorspace and jobs.
The Employment box takes a projection of population in dwellings and applies an age-and-sex-
based employment participation rate to yield a projected regional labour force. Two subsequent
share variables are used to project employment (i.e., jobs within the Capital Region), taking out
―live in, work out‖ workers and adding in ―live out, work in‖ workers.
The same accounting procedure used to determine New Dwellings Required on the residential
side is used for New Employment Space Required. In this case the variable being determined is
the stream of new employment needed to keep the total employment ―supplied‖ (i.e., base
employment stock plus net employment flow) commensurate with the total employment
projected. The new employment required is converted to new employment-related floorspace
required using an assumed average space per new employee, by employment type. Other
assumptions include the mix of new employment types and stock removal rates for base
employment.
New employment space is geographically allocated using the same mechanism as the residential
side, using the same land use plan controls: capacity (stated in square feet of floorspace) and
priority. Again, the greenfield-reurbanization distinction is made.
PLUM can explicitly allocate ―population related‖ employment – i.e., retail and service
employment which serves local communities, and therefore ―follows‖ residential development –
although this was not utilized in the pilot version of Winnipeg PLUM.
The final step of the employment allocation process is the calculation of employment (jobs) by
zone. The key employment sector informant is presented in Table 5-3.
32
Table 5-3: Key employment informant
Informant Name Description
Employment sector
Note: Italicized sectors are
assumed not to require built
space.
Set:
- Industrial
- Warehouse and logistics
- Retail
- Office
- Education
- Service
- Primary
- Work at home
- No fixed place of work
5.3.4 PLUM’s Suitability for Sketch Planning
Every model, by definition, embodies some combination of abstraction, simplification and
aggregation. These design decisions represent limitations of which model users should be aware,
but these may also be seen as features which make a particular model appropriate for certain
types of analysis. This sentiment is elegantly captured in the widely-quoted statement, ―All
models are wrong; some models are useful.‖ (Box, 1979). This section outlines the main features
of PLUM which may be viewed as appropriate for regional long-term sketch planning.
5.3.4.1 Treatment of markets
PLUM does not include a formal representation of markets and prices in the land development
process, thereby side-stepping significant model complexity. Rather, it employs a ―command-
and-control‖ land use plan to allocate development in space and time. This approach may be
justified by the view that a regional planning authority (ostensibly) shapes urban form via official
plans, policies, zoning by-laws, secondary plans, etc., and that it ultimately grants or denies
approval to individual development proposals. The major caveat here is that a land use plan and
projected urban state from PLUM may not be supported by actual market conditions (e.g.,
consumer preferences, developer incentives) – and hence may be infeasible. In practice, the land
use plans provided to PLUM are judgement-based and are implicitly informed by expert
knowledge of a regional economy and market conditions.
33
Another perspective on the market-agnostic, physical-accounting orientation of PLUM – versus
market-driven land development models – is a complimentary one. PLUM offers a means to
quickly explore alternate physical trajectories of an urban system, unconstrained by econometric
behavioural models. These alternate paths can be screened with respect to physical impacts (e.g.,
land consumption, transportation energy and emissions) and serve as references for subsequent
behavioural analysis concerned with incentivizing towards or away from paths identified as more
or less physically desirable.
5.3.4.2 Treatment of time
PLUM is a dynamic framework; in the case of Winnipeg PLUM it operates at a single-year time
step. This temporal resolution adds more data richness and complexity compared to a static
equilibrium modelling approach. However, the benefit of a time-explicit approach may be
considered to outweigh the data-management overhead cost, especially as it is handed by the
platform‘s underlying stock-flow tools. Whereas Lowry-type models forecast a future state at
some indeterminate point, without using base or earlier-than-forecast land use patterns
(Horowitz, 2004), PLUM evolves the system starting from a known base state.
5.3.4.3 Treatment of uncertainty
PLUM‘s core structure is deterministic. There are no probability distributions associated with
assumptions and land use control variables. In principle this could be achieved through the
creation of a ―stochastic layer‖, but would entail significant additional operational complexity. In
practice, uncertainty is addressed through user judgement and scenarios, assisted by the scenario
management capability of the platform.
5.3.4.4 Other notable abstractions
PLUM involves other abstractions germane to a sketch planning approach, including:
Vacant dwellings and employment space are not explicitly modelled. All built space is
considered occupied at every point in time. Also, time lags between demand and supply
response are not modelled – the creation of new supply is instantaneous and coincident
with demand. The rationale is that issues of shorter-term market dynamics and cycles are
34
not crucial for a model with a long-term strategic orientation (i.e., 30-50 years), and may
therefore be abstracted over.
Dwelling types and employment sectors (Table 5-1 and Table 5-2 respectively) represent
aggregations which span Winnipeg PLUM‘s demand and supply processes. The 5-group
dwelling type categorization captures distinct types quite well, and also maps nicely to
Statistics Canada‘s Census dwelling types. The 9-group employment sector
categorization classifies both employment (in jobs) and employment space (in square feet
of floorspace). This classification bridge between employment and built space is a
convenient structural simplification for the model, but presents both conceptual and
practical challenges7.
While PLUM‘s stock-flow-based allocation provides ―stickiness‖ for dwellings and
employment space in zones, at each time period population and employment are assigned
to zones de novo. In practice this is ameliorated by projecting the relative zonal attractors
– estimated persons per dwellings, and estimated space per employee – so that they do
not vary rapidly.
5.3.4.5 Familiarity of model concepts to professionals
A final point regarding PLUM‘s suitability for sketch planning and broader adoption speaks to
the familiarity of the model‘s concepts to practising land use planners. PLUM was developed in
consultation with regional land use planners (Bish and Hoffman, 1993; Martin, 2009) and as a
result it embodies many concepts and procedures that are well understood by the planning
profession (e.g., cohort-survival population models, land capacity analysis, development
priorities and phasing). The combination of structural model transparency, data transparency and
conceptual familiarity may be viewed as mitigating common ―black box‖ resistance to model
adoption.
7 An example of a conceptual challenge is accounting for a worker classified as industrial who actually works in an
office position. A practical challenge is mapping to the employment sector classification from both the North
American Industry Classification System (NAICS) used in the Census and also municipal building assessment
codes.
35
5.4 Travel Model Description
The lower half of Figure 5-1 represents a four-stage travel model which accepts population and
employment time-series projections from PLUM, and also a user-specified evolving Multi-modal
Network Plan.
The following Section 5.4.1 discusses considerations for the choice of transportation modelling
platform, TransCAD. Section 5.4.2 provides general information about the travel model.
Sections 5.4.3 - 5.4.6 describe the individual stages of TransPLUM‘s four-stage travel model,
intertwining model structure and methods with a description of the base-year travel context
within the Winnipeg area.
5.4.1 Travel Model Platform - TransCAD
In the case of Winnipeg a pre-existing operational travel model was not available; therefore a
new 4-stage model was developed for TransPLUM. Caliper Corporation‘s TransCAD®
transportation modelling package was selected as the implementation platform for two main
reasons:
1. TransCAD is a widely used, modern transportation modelling package which provides
the various 4-stage procedures in a customizable, scriptable environment. TransCAD
also includes a native GIS interface for creation and editing of multi-modal networks.
2. A detailed base road network covering the study area is maintained by the Winnipeg
Public Works department in the TransCAD environment and was made available to this
project (see Section 4.2).
The decision to implement the travel model on a separate platform, and not natively in the
whatIf? platform, has drawbacks. The first is a loss of transparency: calling a single compiled
TransCAD script from the whatIf? platform hides the internal logic of the 4-stage model (unless
the user is willing and able to work directly with the TransCAD script). The second drawback is
36
a partial loss of scenario management as the tracking of variable instances and scenarios does not
automatically extend from the whatIf? platform into the TransCAD script8. Variables are tracked
if they are explicitly exported to and imported back from TransCAD but implementing this on
every variable internal to the 4-stage model represents significant development and operational
overhead.
The option of implementing a 4-stage travel model directly in the whatIf? platform was feasible,
and in fact a simple whatIf?-based travel model does exist9. However, it was ultimately decided
that for the pilot version of TransPLUM the benefits of using a mature and feature-rich third-
party transportation modeling environment outweighed the costs of developing data interface
logic and the partial loss of transparency and scenario management within the travel model.
Future development on TransPLUM could include various degrees of ―cracking open‖ the travel
model within the whatIf? platform.
There is another perspective on the hard interface between the whatIf? platform and TransCAD.
In the pilot version of TransPLUM, the data ―bridge‖ is quite ―narrow‖ and comprises:
population and employment zonal totals to TransCAD; and origin-destination trip flows and
travel times back to the whatIf? platform. These are standard 4-stage model inputs and outputs
and so it is conceivable that another common transportation modelling package could be
swapped in for TransCAD.
5.4.2 General Travel Model Information
As stated in Section 5.3, the travel model operates at the same geographic zone system used by
PLUM – the 327-zone traffic zone system covering the Winnipeg Capital Region study area
shown in Figure 4-2. The decision to have the land use and travel model use a common
8 TransCAD provides some diagrammatic and scenario management functionality; however, a cursory review of the
product documentation suggests a less natural implementation than that of the whatIf? platform. Furthermore, the
proposition of implementing scenario management on two independent platforms for the same model seems
cumbersome.
9 Bish and Hoffman (1993) describe the Waterloo Regional Planning Framework, developed in the whatIf?
Modelling Platform, which includes a transportation module. However, the readily-available transportation
functionality is limited. For example, the only traffic assignment routine currently available is all-or-nothing, and
there is little built-in network editing capability.
37
geographic dimension is a major convenience which precludes the need for tedious mapping
procedures.
The model represents passenger travel in the Capital Region during the 8:00-8:59 AM peak hour
of a typical Fall weekday, consistent with the 2007 Winnipeg Area Travel Survey dates.
Modelling only the AM peak hour was done for simplicity, but travel models for additional
periods could be developed. The surveyed number of person-trips by time-of-day Winnipeg
residents is shown in Figure 5-5, with the AM peak hour highlighted.
0
20000
40000
60000
80000
100000
120000
140000
160000
180000
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Time of day
Nu
mb
er
of
trip
s
Figure 5-5: Total person-trips by time of day. 8-9AM peak hour is shaded in red. Source:
2007 Winnipeg Area Travel Survey.
The other informant shared by the travel model and PLUM is the simulation time dimension
(Table 5-1). This 50-year time horizon in one-year steps is the temporal frame in which
Winnipeg PLUM operates; it also defines the sequence of travel model runs. To be clear, PLUM
is a dynamic model in which the system state at a given year partially depends on the previous
year. In contrast, the 4-stage travel model is a static-equilibrium model representing a particular
38
time period, in this case the AM peak hour. A travel model run for a given year is independent of
runs for all other years. Strictly speaking, the simulation time dimension is not a property of the
travel model; but it is convenient that the sequence of independent travel model runs is made
temporally coincident with the land use model‘s output stream.
Finally, it should be noted that there is a one-year gap between the 2007 travel survey and the
2006 Census – the Winnipeg TransPLUM base year – but the survey is treated as representative
of the 2006 base.
5.4.3 Trip Generation
The trip generation procedure calculates the number of trip ends (productions and attractions) by
zone and trip purpose, for the AM peak. In TransPLUM this is achieved by multiplying: trip
generation rates, where a rate is the number of trips generated per unit driver; and zonal driver
variables, where the definition of a driver varies by trip purpose and whether it is for production
or attraction. The driver variables are defined in Table 5-4.
Table 5-4: Trip generation driver definitions. The unit of population is persons; the unit of
employment is jobs.
Trip end type
Production Attraction
Trip
Purpose
Home to work Population Employment
Home to school Population Education Employment
Home to other Population Population + Employment
Non-home based Population + Employment Population + Employment
Trip generation rates for the base year were calculated using: trip data from the 2007 Winnipeg
Area Travel Survey; and zonal population and employment levels from the custom-tabulated
2006 Census data (see Section 4.2 for a description of the datasets). The mapping from the
survey‘s trip purposes to modelled purposes in provided in Appendix A. The reader will recall
that the survey represents trips made within, to and from the City of Winnipeg – but not those
made exclusively in the ―outer ring‖ of the Capital Region – thereby excluding a portion of the
travel activity for outer-ring residents. Therefore, several considerations were made in the
selection and tabulation of records for base year generation rates:
39
Only AM peak trips made by residents of the City were included in trip rate numerators.
Only population and employment within City boundaries were used in trip rate
denominators10
.
This effectively applies the dominant city-based generation rates to the entire study area, but this
was determined to be preferable to including only partial travel activity from outer-ring residents.
The number of AM peak trips made by Winnipeg residents, broken down by trip purpose, is
presented in Table 5-5. Based on a total population of 632,965 people and employment of
311,824 jobs (of which 24,835 are in the education sector), the estimated base-year trip rates are
provided in Table 5-6 below.
Table 5-5: Base-year AM peak trips made by Winnipeg residents.
Trip end type
Number of Trips Percent of Trips
Trip
Purpose
Home to work 55,641 36.7%
Home to school 46,233 30.5%
Home to other 31,781 21.0%
Non-home based 17,802 11.8%
Total 151,457 100.0%
Table 5-6: Base-year AM peak-hour trip generation rates, in trips per driver unit. Drivers
are defined in Table 5-4.
Trip end type
Production Attraction
Trip
Purpose
Home to work 0.087905 0.178437
Home to school 0.073042 1.861608
Home to other 0.050210 0.033638
Non-home based 0.018842 0.018842
10 This likely under-estimates the absolute attraction rates where City-resident-only trip totals are divided by City-
based employment totals, and some portion of the City-based jobs are filled by non-City residents. However,
productions and attractions are subsequently balanced – with productions held fixed – and so attraction rates are
relative.
40
The initial trip generation procedure results in zonal production and attraction vectors whose
totals do not match; this is followed by a balancing procedure in which the productions are held
fixed and the attractions are uniformly scaled.
For reasons of technical convenience, the first part of TransPLUM‘s trip generation procedure is
implemented on the whatIf? Modelling Platform. An unbalanced production-attraction table is
then passed to the TransCAD travel model script which performs trip balancing. The whatIf?-
based trip generation rates are exogenous and may be varied over time.
5.4.4 Trip Distribution
In this step the balanced production and attraction trip ends serve as the origin (row) and
destination (column) totals for origin-destination (O-D) trip matrices – one for each of the
model‘s four trip purposes – populated by a doubly-constrained gravity distribution procedure.
Individual O-D matrix cells representing trip flows from one zone to another are commonly
referred to as interchanges.
For a given trip purpose, the number of trips predicted from origin zone i to destination zone j is
given by:
)( ijjjiiij cfDBOAT ( 5.1 )
where Oi and Dj are origin and destination trip end totals respectively; f(cij) is an impedance
function of travel cost, cij; and Ai and Bj are balancing factors solved through a standard iterative
procedure described by Ortúzar and Willumsen (2001).
TransPLUM uses auto zone-to-zone travel time in minutes as the measure of travel cost. The
functional form selected for impedance is the inverse power function:
b
ijij ccf
)( ( 5.2 )
where b is a parameter whose value is found to produce the closest match between predicted trip
length distribution and the observed trip length distribution from observed base-year O-D
matrices. The inverse power function was chosen due to its simple functional form but also due
41
to its performance, based on visual inspection of observed and predicted trip length distribution
charts, relative to other impedance functions such as the exponential.
Table 5-7: Calibrated inverse function gravity parameters by trip purpose
Trip Purpose Calibrated parameter
value, b
Home to work 1.32
Home to school 2.56
Home to other 2.09
Non-home based 1.95
TransCAD includes standard procedures for applying gravity distributions, and also for
calibrating their parameters. Table 5-7 presents the calibrated inverse function parameter values
for Winnipeg TransPLUM; Figure 5-6 shows the observed11
and predicted trip length
distributions for the Home-to-Work trips.
Validation was performed on distribution procedure by defining 17 superzones and tabulating the
interchange trip flows from the observed survey data and from the predicted gravity distribution,
for each trip purpose. The observed and predicted interchange flows were used in a linear
regression, yielding goodness-of-fit R2 values of 0.64, 0.69, 0.77 and 0.67 for home to work,
home to school, home to other and non-home based trips respectively. Scatterplots of the
predicted vs. observed super-zone interchange flows are provided in Appendix B.
It should also be noted that trip distribution is the first step in the so-called ―outer loop‖ of the 4-
stage model in which the Trip Distribution → Mode Split → Trip Assignment sequence is
iterated, updating link travel times until convergence. The ―inner loop‖ refers to a standard
iterative procedure used in trip assignment, described in Section 5.4.6. Neither loop is shown in
the TransPLUM system diagram Figure 5-1 for simplicity.
11 These distributions are based on modelled base-year zone-to-zone travel times (as trip durations are not recorded
in the travel survey). Travel times are modelled by assigning base-year O-D matrices from the survey to base-year
networks.
42
Observed Trip Length Distribution
0
500
1000
1500
2000
2500
3000
3500
4000
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101
105
109
113
117
Auto Travel Time (min)
Fre
qu
en
cy
(a)
Predicted Trip Length Distribution
0
500
1000
1500
2000
2500
3000
3500
4000
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101
105
109
113
117
Auto Travel Time (min)
Fre
qu
en
cy
(b)
Figure 5-6: Observed and predicted trip length distributions for AM peak home-to-work
trips.
43
5.4.5 Mode Split
The mode split step takes the four O-D trip matrices – one for each trip purpose – from the
preceding distribution step and splits each into three separate matrices for the modelled modes of
travel: auto, transit and walk-bike. In total twelve O-D matrices are created: four trip purposes by
three modes.
Home to Work
auto, 84%
other, 0%
transit, 9%
w alkBike, 7%
Home to Other
auto, 89%
other, 1%
transit, 4%
w alkBike, 6%
Home to School
auto, 44%
other, 2%transit, 23%
w alkBike, 31%
Non Home Based
auto, 90%
other, 0%
transit, 1%
w alkBike, 9%
Figure 5-7: Observed AM peak-hour mode shares by trip purpose. Source: 2007 Winnipeg
Area Travel Survey.
44
Figure 5-7 shows the AM-peak mode shares by trip purpose from the 2007 travel survey12
. With
the exception of home-to-school trips, auto is the dominant mode with 84-90% share.
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Trip Distance (km)
Mo
de S
hare
auto
bike
other
transit
walk
Figure 5-8: Mode share vs. trip distance for AM peak hour home-to-work trips. Source:
2007 Winnipeg Area Travel Survey.
In formulating a mode-split model it can be informative to plot mode share against trip distance
from survey data13
, as shown in Figure 5-8 for home-to-work trips. The plot reveals that the walk
mode is sizable for trips less than 2 km. Auto share rises to around 90% approaching the 6 km
trip distance. Transit share appears to peak around 15-20%, starting at 2 km, and gradually
declines with increasing distance. The bike mode is a ―trace‖ element, rarely having more than a
few percent share at any distance.
12 The rules used to classify the survey‘s individual trip records as one of the three modelled modes are provided in
Appendix C.
13 Survey trip records include a point-to-point straight-line distance field.
45
A standard random utility multinomial logit model is used to perform the mode split in
TransPLUM. This model assumes that a trip maker t selects the available mode i which offers
the greatest utility. Utility, Uit is defined as
ititit VU ( 5.3 )
where Vit is the ―systematic‖ or observable utility and εit is random utility, an ―error‖ term. Using
the assumption that the error terms for all trip makers are identically and independently
distributed with the Type I Extreme Value distribution, then the probability of a trip maker t
selecting a mode i is given by
j
V
V
itjt
it
e
eP
( 5.4 )
In TransPLUM the mode choice models are estimated using micro trip record data but applied at
an aggregate zone-to-zone, or trip interchange level. Excellent treatments of discrete choice
modelling are provided by Ben-Akiva and Lerman (1985) and Train (2009).
The set of equations ( 5.5 ) represents the systematic utilities of the three modes, for Winnipeg
TransPLUM‘s home-to-work mode split model.
bikeDistbikeablewalkDistwalkableV
ipDisttTimePerTrwalkAndWaiTTV
kmdistIfGTkmisDistGTTTV
walkBike
transittransittransit
autoauto
389.0610.1297.1545.0
157.0012.0640.0
6538.06991.2012.0
( 5.5 )
Estimated parameter values are included in the equations, all of which are significant at the 95%
confidence level. The detailed estimation results are provided in Appendix D. Key points
regarding this model are:
A generic total travel time variable TT in minutes is included in the auto and transit
modes with a negative parameter, suggesting that the greater a mode‘s travel time for a
given trip interchange, the less attractive the mode becomes. In practice travel time
variables are almost universally included in mode choice models and their parameter
signs are expected to be negative to be considered valid.
46
The utility equation for the combined walk-bike mode is based entirely on trip distance.
The dummy variable walkable takes a value of one if a trip has a straight-line distance of
less then 2 km; otherwise it is zero. The variable walkDist is the straight-line trip distance
in kilometres if walkable is one; otherwise it is zero. Along with its positive parameter,
walkable acts as an alternative specific constant for the walk component of the walk-bike
mode. The negative walkDist parameter decreases the attractiveness of walking with
increasing distance, in much the same manner as a negative travel time parameter does
with increasing time. For the bike component of the walk-bike mode the dummy
bikeable and distance bikeDist variables act in the same way as their walk-mode
counterparts. However, bikeable takes a value of one for trip distances greater than or
equal to 2 km and less than 10 km.
The variable walkAndWaitTimePerTripDistance in the transit utility equation is a
measure of walking and waiting intensity, in minutes per kilometre. It reflects the
unattractiveness of a short-distance transit trip with a relatively large walk-and-wait time
component. By the same token a traveller would be more amenable to the same walk-
and-wait time if it were associated with a longer-distance trip.
The auto utility equation contains a pair of distance-based variables – isDistGT6km and
distIfGT6km – similar to those in the walk-bike equation but with a greater-than 6 km
threshold. Interestingly, the distIfGT6km parameter has a positive sign, correlating
increasing trip distance with greater auto attractiveness. One possible behavioural
interpretation of such a correlation is that the further travellers venture away from home,
the less comfortable they are relying on transit, as proposed by Marshall and Grady
(2006) to explain positive distance parameters in a mode choice model developed for the
Washington DC region. In Winnipeg TransPLUM the inclusion of this pair of distance-
based variables in the auto utility was found to be a factor in the estimation of a negative
travel time parameter (see the first point in this list regarding the importance of negative
travel time parameters); without the distance-based variables, positive travel time
parameters resulted. Furthermore, inclusion of other common mode-choice model
variables (e.g., costs, origin and destination zone densities) resulted in positive travel time
parameters. This experience appears consistent with earlier research in Winnipeg area
47
mode split models (Hurl, 1996) in which mode share showed low sensitivity to modal
travel time.
Project time constraints prevented the development of individual mode split modes for the other
three trip purposes – home to school, home to other, and non home based – and so the home-to-
work model specification was reused and estimated using survey data for the other purposes. The
resulting travel time parameters either had positive signs, or were statistically insignificant; for
model application these parameters were set to zero. Estimation results for all the trip purposes
are provided in Appendix D.
The predicted base year mode shares are shown in Figure 5-9. Compared to the observed mode
shares shown in Figure 5-7 for home-to-work trips, the dominant auto mode is over-predicted by
about 3%. This variance is not surprising given the relatively coarse interchange variables
available and the lumping together of auto-drive and auto-passenger. A further step – not
performed in this project – would be a mode split model calibration step in which alternative
specific constants are adjusted for closer matching of observed and predicted aggregate shares.
Model performance for the other trip purposes is not very good; as such there is room for
improved specification of these models.
The availability of the transit and walk-bike modes are restricted to interchanges which meet
certain maximum time and distance criteria. These criteria are provided in Appendix E.
It is also worth noting that zone system definition is an extremely important factor in the
accuracy of the projected mode shares. Standard transportation modelling practice involves
grouping, or abstracting, all the activity points in a zone into a single point, or centroid.
Centroids are then connected to various transportation networks via virtual links called centroid
connectors. Large zones imply coarse spatial aggregation insofar as they group large areas of
activity into single representations of network accessibility. Modelled walk times or distances
along centroid connectors are especially sensitive to zone size: naturally these affect the walk
mode, but also the walk-time component of transit, which in turn impact projected mode shares.
As can be seen in Figure 4-2, zone sizes in the Winnipeg TransPLUM zone system generally
increase moving outwards from the city centre. Incidentally, much of the Winnipeg Capital
Region‘s growth is anticipated to occur in larger zones near the city boundary. One of the
suggested improvements in Section 8.3 is to define smaller zones in these growth-prone areas.
48
Home to Work
aut o, 87%
t ransit , 6%
walkBike, 7%
Home to Other
aut o, 94%
t ransit , 2%
walkBike, 4%
Home to School
aut o, 58%
t ransit , 16%
walkBike, 26%
Non Home Based
aut o, 94%
t ransit , 2%
walkBike, 4%
Figure 5-9: Predicted AM peak-hour mode shares by trip purpose.
A further cautionary note is offered with respect to the mode split model presented in this
section, along with a vision for a looser coupling between the TransPLUM framework and mode
split models. The mode split model presented here was developed using base-year survey data,
and included variables from the limited TransPLUM outputs available. The resulting model is
strongly distance-based; it embeds a rigid dependence on observed historical correlations
between mode share and trip distance. The model is not without merit in the TransPLUM
context: one would expect it to pick up some modal shifting associated with land use change,
such as intensification, through changing trip length distributions. However, the model is not
well equipped to reflect substantial mode share change which may result from transportation
level-of-service changes, such as investment in a regional rapid transit network. Projecting mode
shares and developing mode split models is as much an art as it is a science and alternate plans
49
may be served by different mode split models. As such, the mode split step in the context of the
overall TransPLUM framework may be seen not as a hard-wired model with its associated
parameters but rather as a placeholder for exogenous mode shares (by interchange, by trip
purpose). In this approach mode shares could be generated by different formal models, such as
the one presented in this section; or they could be judgment-based, much like PLUM‘s land use
plan described in Section 5.3.2. The key point is that the mode split step could move to being
managed at the scenario level – enabling easier swapping in and out of mode split assumptions or
models – rather than being hard-coded into the underlying TransPLUM framework. The practical
implementation of such an approach relates to issues of ―cracking open‖ the travel model within
the whatIf? platform, discussed in Section 5.4.1. In the current pilot implementation of Winnipeg
TransPLUM the mode split model is executed by fixed logic within a TransCAD script.
5.4.6 Trip Assignment
In the trip assignment stage the O-D matrices from the preceding mode split stage are
consolidated across trip purposes to give total O-D trip demand matrices by mode. The modal
demands are loaded onto their respective networks; they traverse actual routes and ultimately
yield flow rates on individual network links. Trip assignment is performed to predict usage on
specific network segments and in the case of networks modelled with capacity constraints, to
predict the impact of travel demand on network performance. In Winnipeg TransPLUM the auto
road network is capacitated, the transit network is un-capacitated and the walk-bike trips are not
assigned (they are assumed to follow the road network but not suffer congestion effects). The
reader will recall from Section 5.4.2 that the four-stage model represents a static-equilibrium
state and so the projected flow rates are indicative of the entire trip assignment period – in this
case the AM peak hour.
5.4.6.1 Auto assignment
Prior to auto trip assignment, the consolidated auto O-D trip matrix, in person-trips, is converted
to vehicle-trips with a single factor calculated from the travel survey: 0.897 vehicle-trips per
person-trip. The matrix is assigned to a capacity constrained road network assuming
deterministic user equilibrium conditions (Wardrop, 1953), an industry-standard procedure
supported by all major transportation modelling software packages including TransCAD (Caliper
Corporation, 2008). User equilibrium is the network condition in which all routes connecting an
50
O-D pair offer the same travel time, and a user is not able to select one route over another to gain
travel time savings. The assignment requires specification of a volume delay function which
relates individual link travel times to the vehicular traffic volumes serviced by those links.
Winnipeg TransPLUM uses the common Bureau of Public Roads volume delay function with the
recommended default parameters.
The base-year road network used is that described in Section 4.2, the 2008 network provided by
Winnipeg‘s Public Works Department. Although the model network represents a 2008 road
configuration it was selected as the best available representation of the 2006 base-year network
as modifications were minimal during the intervening period. It contains highway and arterial
links, but also local roads. It has approximately 35,000 links and 11,000 nodes – more detail than
ideal for a regional sketch model. There are several reasons why a detailed network
representation is not desirable in this context:
The greater a network‘s detail, the more effort required for its creation. Defining future
networks down to the local-road level presents a significant obstacle to generating
multiple alternative future network plans.
Detailed plans of subdivision and local street layouts are generally not known for long-
term future development.
Trip assignment through centroid connectors does not necessarily benefit from the use of
local road networks.
For these reasons it would have been preferable to define a more aggregate representation of the
base-year road network and strip out local roads, but project time constraints precluded such an
exercise. A further complicating factor was the use of the detailed road network as the basis for
transit route definitions – some of which occur on local roads – and is discussed in the following
section. As a result, network definition detail is listed as an area for improvement in Section 8.3.
Figure 5-10 shows the base year auto flow map resulting from trip assignment, including
volume-to-capacity link colouring.
51
Figure 5-10: Base-year scaled-symbol auto flow map
Trip assignment is sometimes referred to as the ―inner loop‖ of the four-stage model due to a
standard iterative procedure used to solve user equilibrium trip assignment. In TransPLUM, auto
trip assignment is also the final step of the ―outer loop‖ introduced in Section 5.4.4. In the outer
loop the auto assignment results are used to recalculate zone-to-zone travel times (an impedance
matrix) which is fed back into the trip distribution procedure, followed by mode split and trip
assignment. TransCAD‘s implementation of the Method of Successive Averages is used here –
as opposed to direct feedback – in which assignment results are averaged with those of previous
outer loop iterations and fed back to trip distribution until convergence (Caliper Corporation,
2008).
5.4.6.2 Transit assignment
TransPLUM employs a non-capacity constrained transit network – a common approach for
transit assignment. This is justified by the assumption that public transit systems offer large
52
passenger capacities, and can be scaled up to meet demand as required (e.g., by adding more
vehicles to a route).
As Winnipeg‘s existing transit system is bus oriented, the base-year network is comprised of bus
routes defined over the underlying road network used for auto assignment. The operational route
definitions from Winnipeg Transit are available at the stop-by-stop level; however, this is seen as
too detailed for a sketch model for reasons similar to those discussed regarding auto network
detail in Section 5.4.6.1. Therefore, transit route points coinciding with nodes in the road
network are used to define stops, or access nodes in the transit route system (TransCAD
terminology).
The actual transit assignment procedure used is the TransCAD-specific Pathfinder method,
similar to the assignment procedures found in other transportation modelling packages such as
EMME/2 and TRANPLAN. The key features of the Pathfinder method in the context of
TransPLUM are: it consolidates overlapping routes into ―trunks‖ to reflect concentrated service
corridors; and it selects multiple transit paths between O-D pairs, and allocates trips to alternate
paths based on their levels-of-service (Caliper Corporation, 2008).
With few exceptions, Winnipeg transit base-year bus routes run in mixed traffic and are therefore
susceptible to delays due to auto congestion. In practice, modelling transit vehicle delay due to
auto congestion is a relatively advanced four-stage model feature; and there are other factors at
play: route schedules, vehicle dwell times due to boardings and alightings, etc. Based on
anecdotal knowledge and a cursory analysis of average route speed statistics from Winnipeg
Transit, the transit network link travel times were set to twice those of the auto link free-flow
speeds.
A final note on the transit assignment step in TransPLUM is that it is optional. In a non-capacity
constrained network, determining O-D impedance matrices (travel time, in the case of
TransPLUM) can be independent from assigning trip volumes to routes and links. Therefore,
unless ridership projections by route are specifically required, only transit impedances need be
53
calculated for the mode split step. Furthermore, if transit assignment is required it can be
performed outside the ―outer loop‖, as transit impedances are fixed14
.
5.4.7 Travel Model Outputs Returned to whatIf? Platform
Following the completion of every TransCAD travel model run for a given year, O-D trip flow
and travel matrices (by mode, by trip purpose) are passed back to the whatIf? platform for
scenario management and analysis alongside the corresponding PLUM data.
5.4.8 Travel Model’s Suitability for Sketch Planning
While there are several valid criticisms of the four-stage model it remains the dominant travel
modelling approach, well understood by transportation planning professions. There is a body of
evolving methods designed to address the shortcomings of four-stage travel models (e.g.,
activity-based models, microsimulation) but it was decided that the complexity and data
requirements of these approaches would be excessive for the sketch orientation of this project.
Furthermore, the design of a four-stage model is sufficiently flexible to accept relatively
aggregate population and employment distributions, as is the case with TransPLUM. The PLUM
outputs and travel model inputs are aligned – mapping and disaggregation procedures are not
required for the data transfer.
5.5 TransPLUM run-time performance
Comprehensive, rigorous performance testing was not carried out on Winnipeg TransPLUM.
However, a full run of the connected PLUM and TransCAD travel model for the baseline
scenario (described in Section 6) took approximately 1 hour and 40 minutes on the reference
system15
. The PLUM portion of the run time is very small – less than 5%. A significant portion
(~25%) is spent on dis- and re-assembly of large multi-dimensional data arrays across the
14 Should the transit network link speeds be made dependent on the auto network link speeds, this would no longer
be the case and transit assignment would have to occur within the outer loop.
15 The test system was a mid-range 2006-era laptop PC with: a dual-core Intel T2400 CPU @ 1.83GHz, 1GB RAM,
and Windows XP. The whatIf?-based PLUM ran on a virtualized (VMware) Linux server. TransCAD 5.0 ran
natively on Windows.
54
whatIf-TransCAD interface via flat files. There is significant potential for run time reduction in
re-engineering the data interface, but also in the use of more modern and powerful hardware.
However, even though a 1 hour and 40 minute run time does not represent ―real time‖ analysis, it
does offer an advantage over more complex integrated urban models whose run times are often
measured in days. In this regard TransPLUM‘s performance is consistent with goal of a sketch-
type model capable of rapid scenario analysis and turnaround.
55
Chapter 6 Baseline Scenario
6 Baseline Scenario
The previous chapter describes: development of Winnipeg TransPLUM‘s structure and its
constituent sub-models; preparation of historical demographic time-series data, and
geographically distributed base-year stocks for the PLUM component; and calibration of the
travel model using base-year survey data. With these tasks completed, TransPLUM is able to
accept future assumptions and policy controls in order to produce scenarios – projections of
future urban states. This section describes the creation of a first ―baseline‖ TransPLUM
scenario.
Before proceeding there are two important caveats to be stated:
1. The baseline scenario presented here does not represent an official forecast from the
City of Winnipeg. It has not been reviewed or vetted by City staff; rather, it is a
preliminary synthesis of assumptions and interpretations of several consultant reports. It
is not intended to serve as a basis for policy and planning decisions without further
collaborative review.
2. Due to project time constraints, an evolving multi-modal network plan was not prepared
for the baseline scenario. Instead, the fixed base-year networks were used for the entire
simulation time horizon. Thus, while the baseline scenario projects population growth,
economic growth and land use change, transportation infrastructure is not expanded.
This was a project resource limitation – not a model limitation. TransPLUM does
include the logical structure to accept evolving multi-modal network, in one-year steps.
In spite of these caveats and limitations, constructing the baseline scenario is an important step in
model ―shake down‖ and testing. Also, as the name implies, it provides a baseline or reference
from which to construct and compare new scenarios.
While it is common to attribute labels or themes to scenarios (e.g., ―business as usual‖, ―smart
growth‖) it is difficult to assign such a label to this baseline scenario. It incorporates projections
56
and assumptions from several recent consultant reports prepared for the City of Winnipeg. These
reports include:
Long-Term Demographic and Economic Forecast for Winnipeg‘s Census Metropolitan
Area (Conference Board of Canada, 2007)
City of Winnipeg Residential Land and Infill Strategy – Draft (Office for Urbanism,
2009)
City of Winnipeg Comprehensive Employment Lands Strategy (Altus Clayton, 2008)
City of Winnipeg Commercial Land Strategy (Altus Group Economic Consulting, 2009)
Downtown Winnipeg Employment Study (Altus Clayton, 2009)
The forecast time horizons used by these reports generally extend 25 years, using the 2006
Census year as a base and projecting out to 2031. Therefore many of the results which follow
also use this timeframe. The reader will recall, however, that TransPLUM‘s simulation time
horizon extends 50 years, ending at 2056.
6.1 Population, Dwellings and Employment
In constructing the baseline scenario, PLUM‘s net immigration was approximately matched to
that of the Conference Board‘s population forecast, resulting in the total population projection
shown in the graph in Figure 6-1 (a). Note that the baseline projection is slightly greater than the
Conference Board‘s – between 2-4% larger over the period shown. This is expected as the
Conference Board‘s projection covers the Winnipeg Census Metropolitan Area (CMA), whereas
TransPLUM covers the larger Winnipeg Capital Region, which had 3.5% more population than
the CMA in the 2006 base year. Other population variables are held constant at their base-year
values16
.
16 The projections for these other population variables (fertility- and mortality- related) could certainly be adjusted
to reflect historical trend analysis. However, holding these variables fixed is not an unreasonable approximation
given their slow rate of change and the relative insensitivity of the total population to them, versus projected
immigration levels.
57
Figure 6-1 (b) compares household projections, which appear consistent with the difference
observed from the population comparison. Figure 6-1 (c) and (e) compare housing starts and
new jobs respectively. These two projections are of particular interest because they are drivers of
urban land development in TransPLUM.
Population
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
2006
2008
2010
2012
2014
2016
2018
2020
2022
2024
2026
2028
2030
pers
on
s
Conference Board CMA TransPLUM baseline
(a)
Households
-
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
450,000
2006
2008
2010
2012
2014
2016
2018
2020
2022
2024
2026
2028
2030
ho
useh
old
s
Conference Board CMA TransPLUM baseline
(b)
Housing Starts
0
1,000
2,000
3,000
4,000
5,000
6,000
2006
2008
2010
2012
2014
2016
2018
2020
2022
2024
2026
2028
2030
dw
ellin
g u
nit
s
Conference Board CMA TransPLUM baseline
(c)
New Jobs
0
1,000
2,000
3,000
4,000
5,000
6,000
2007
2009
2011
2013
2015
2017
2019
2021
2023
2025
2027
2029
job
s
Conference Board City Wpg TransPLUM baseline
(d)
Figure 6-1: Comparison of Winnipeg TransPLUM baseline scenario to the Conference
Board’s demographic and economic forecasts.
Over the full forecast period the Conference Board‘s total projection of housing starts is
approximately 14% greater than the TransPLUM baseline. Two likely sources of difference are:
58
The baseline scenario assumes no dwelling unit removals (demolitions) and therefore
does not create new replacement housing stock, which may be present in the Conference
Board‘s projection.
TransPLUM does not model vacant dwellings (see Section 5.3.4.4) – it makes the
dwelling stock exactly commensurate with regional households – and the Conference
Board‘s projection likely accounts for vacant units. This also seems to explain the
baseline projection‘s drastic dip in 2007, where it appears that the household level is
―catching up‖ to the built dwelling stock.
These issues deserve further investigation. However, TransPLUM‘s projection of housing starts
is sufficiently close to a third-party forecast to be considered adequate for the baseline scenario.
The difference in projections for new jobs is similar to that of housing starts, but more
pronounced. Not only is the Conference Board‘s total projection almost 17% larger than the
TransPLUM baseline, but the Board‘s projection covers just the City of Winnipeg, rather than
the CMA. As was the case with dwellings, the baseline scenario assumes no regional job losses
and so replacement jobs are not added into the flow of new jobs.
Further investigation into these differences is sure to improve upon TransPLUM‘s baseline
scenario but may also call into question some of the assumptions used by third-party forecasts,
and highlight the need to perform more sensitivity and scenario analysis.
The baseline scenario includes projections of the shares of new dwellings and employment space
by type, from consultant reports listed above. The portion of these demands directed to
redevelopment (verses greenfield) was determined through a judgment-based iterative process in
which deficits are largely balanced, out until 2031. The resulting portions are in the 25-50%
range, depending on dwelling/employment type.
6.2 Land Use Plan and Allocation
The main land use plan control variables – capacity and priority, described in Section 5.3.2 – are
specified for the baseline scenario, guided by the land strategy documents listed in the
59
introduction to this section. In addition, GIS-based layers from a draft urban structure map17
are
used to overlay development areas with individual TransPLUM zones. A stand-alone sequence
of calculations was developed to prepare zonal capacities by type, outside the formal
TransPLUM structure, as a separate whatIf?-based model. Figure 6-2 is an example of one such
calculation; the resulting capacity is specified in dwelling units by zone by dwelling type.
17 Part of the OurWinnipeg official plan update.
60
Table 6-1 summarizes all the land development types, factors and studies used in the preparation
of the baseline capacities.
Figure 6-2: Example stand-alone capacity calculation, shown for the major redevelopment
component of residential reurbanization. pz is the geographic index PLUM zone; dt is the
index for dwelling type.
61
Table 6-1: Summary of inputs to baseline capacities calculation.
Greenfield Reurbanization
Residential
Studies used:
Residential Land and Infill Strategy
OurWinnipeg Urban Structure (draft)
Development Types:
New communities
Factors:
Gross areas
Land conversion factors
Net area shares to dwelling types
Net densities by dwelling type
Studies used:
Residential Land and Infill Strategy
OurWinnipeg Urban Structure (draft)
Development Types:
Infill
Major redevelopment
Downtown
Factors:
Gross areas
Land conversion factors
Net area shares to dwelling types
Net densities by dwelling type
Employment
Studies used:
Employment Lands Strategy (ELS)
Commercial Land Strategy (CLS)
Development Types:
Unserviced large parcels, ELS
Potential commercial inventory, CLS
Factors:
Gross areas
Land conversion factors
Lot coverage ratios
Floorspace shares to employment sectors
Studies used:
Employment Lands Strategy (ELS)
Commercial Land Strategy (CLS)
Downtown Employment Study (DES)
Development Types:
Vacant/underutilized serviced parcels, ELS
Existing commercial inventory, CLS
Major office job space, DES
Factors:
Gross areas
Land conversion factors
Lot coverage ratios
Floorspace shares to employment sectors
Table 6-2 presents the total baseline scenario capacities for the study area. The reader will recall
that the reurbanization capacities include the already-built base. Due to challenges in working
with the building assessment floorspace data – partial data, category mismatch issues with the
Census industrial classification – the employment floorspace base used is ―synthetic‖, calculated
from base jobs and space per employee assumptions. Future efforts could be directed to
reconciling assessment floorspace data with census employment data.
62
Table 6-2: Total baseline scenario capacities for the entire Winnipeg Capital Region.
Greenfield Reurbanization
Single 73,920 192,836
Semi 2,178 16,201
Row 9,073 10,736
Apartment Low 18,537 73,058
Apartment High 13,066 39,062
Industrial 7,163,227 52,436,297
Warehouse / Logistics 13,350,154 100,530,835
Retail 5,797,581 63,692,030
Office 5,732,014 57,840,876
Education 432,900 17,585,750
Service 8,947,617 57,078,031
Residential
(dwelling units)
Employment (sq.
ft. floorspace)
Development priorities for the baseline scenario are based on phasing assumptions gleaned and
interpreted from the listed consultant reports, supplemented by informal interviews with City
planning staff. Three distinct priority levels are specified for greenfield development, for both
residential and employment. Two levels are used for employment reurbanization. All residential
reurbanization is lumped together into a single priority level18
.
Allocation results are presented in Figure 6-3 in the form of thematic density maps for selected
horizon years: 2007, 2016 and 2031.
18 This is the same assumption used by PLUM users at the Region of Waterloo, Ontario. They note ―the nature of
re-urbanization, in practice, tends to be very spotty and sporadic…[PLUM assumes] re-urbanization will happen
everywhere in proportion to the identified potential.‖ (Martin, 2009)
63
Persons per acre
2007 2016 2031
Jobs per acre
2007 2016 2031
Persons and Jobs per acre
2007 2016 2031
Figure 6-3: Thematic density maps of Winnipeg TransPLUM baseline scenario. All
densities are calculated using gross zonal areas.
64
The baseline scenario projects deficits, shown in Figure 6-4, which represent insufficient planned
capacity to meet expected demand. During the development of the baseline scenario demand
and supply variables were adjusted – manually, over several iterations – to push the onset of
deficits back further in time. On the demand side this involved shifting some development from
reurbanization to greenfield; on the supply side, the planned densities of certain dwelling types
were increased.
LegendGFDUDef/20
scenario 36
1 single
2 semi
3 row
4 aptLo
5 aptHi
greenfield dwelling units deficitdwellUnit / year
time in years
2004 2010 2016 2022 2028 2034 2040 2046 2052 2058
X103
0.00
0.30
0.60
0.90
1.20
1.50
1.80
2.10
1
2
3
4
5
(a)
LegendRUDUDef/19
scenario 36
1 single
2 semi
3 row
4 aptLo
5 aptHi
reurbanization dwelling units deficitdwellUnit / year
time in years
2004 2010 2016 2022 2028 2034 2040 2046 2052 2058
X103
0.00
0.30
0.60
0.90
1.20
1.50
1.80
2.10
12345
(b)
Legend: Single – 1, Semi – 2, Row – 3, Apartment Low Density – 4, Apartment High Density – 5
LegendGFNPRESDef/29
scenario 36
1 ind
2 war
3 ret
4 off
5 edu
6 ser
7 Primary
8 WorkAtHome
9 NFPW
greenfield non population related employment space deficitsqft / year
time in years
2004 2010 2016 2022 2028 2034 2040 2046 2052 2058
X105
0.00
0.90
1.80
2.70
3.60
4.50
5.40
6.30
7.20
8.10
1
2
3
45
6
789
(c)
LegendRUNPRESDef/27
scenario 36
1 ind
2 war
3 ret
4 off
5 edu
6 ser
7 Primary
8 WorkAtHome
9 NFPW
reurbanizaition non population related employment space deficitsqft / year
time in years
2004 2010 2016 2022 2028 2034 2040 2046 2052 2058
X105
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1234
5
6789
(d)
Legend: Industrial – 1, Warehouse/Logistics – 2, Retail – 3, Office – 4, Education – 5, Service – 6
Figure 6-4: Projected capacity deficits for the baseline scenario.
65
With the exception of education-based employment space, deficits in the baseline scenario do not
occur until the year 2028 – towards the end of the 2007-2031 planning horizon used by the
various third-party studies which informed the baseline capacity assumptions. Growth capacity
for education-related employment was not provided in the baseline scenario due to a lack of
information available regarding expansion plans for educational facilities. This education-related
deficit is left as an open issue to be resolved in further scenario development.
6.3 Travel
This section presents key travel model results from the baseline scenario. Much of what follows
is based on O-D trip flow matrices (post mode split) and travel time matrices.
Figure 6-5 shows the baseline projected mode share for all trips. As discussed in Section 5.4.5,
the mode split model over-predicts auto trips in the base year. However, focusing on the rate and
direction of change, one observes only a small shift in shares over time – approximately 2%
increase in auto mode share, from 81% to 83% over 25 years, and matching total decreases in
transit and walk-bike shares. The provisional conclusion is that the baseline land use projection,
on its own, implies a gradual increase in auto share.
Mode Share
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
2007
2009
2011
2013
2015
2017
2019
2021
2023
2025
2027
2029
2031
year
sh
are
auto
transit
walkBike
Figure 6-5: Baseline mode share projection, AM peak hour.
66
Figure 6-6 shows total person travel time over time, by mode. Person travel time increases over
time for all modes, but the auto mode shows the greatest absolute and proportional increase.
LegendtravelInd/travelTimeTot/34
scenario 36
1 auto
2 transit
3 walkBike
total travel timetripPurp=total
minute * person
time in years
2004 2010 2016 2022 2028 2034 2040 2046 2052 2058
X107
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1
2
3
Figure 6-6: Baseline total person travel time over time by mode, AM peak hour.
Figure 6-7 shows baseline AM peak auto travel times from various zones to the Winnipeg central
business district (CBD), represented by zone 201. Graph (a) displays the travel times for all 327
zones to zone 201. The general trend is a gradual increase over time, exemplified by graph (b), a
typical zone. Graphs (c) and (d) are examples of zones showing marked increase in auto travel
time during the simulation horizon. In both these cases the zones of origin are not serviced by
major roads, yet are projected to experience significant growth – thus the projected demand
outstrips the existing road capacity. The reader will recall that the baseline scenario uses a static
base-year road network. Therefore, while these sharply increasing travel times are intuitively
consistent with the baseline assumptions, they should not be considered realistic projections.
67
LegendfourStageTravel/TT/32
scenario 36
1 201
2 202
3 211
4 212
5 213
6 214
7 241
8 242
9 243
10 251
11 252
12 253
13 254
14 255
15 261
16 262
17 461
18 462
19 463
20 464
21 465
22 466
23 471
24 472
25 473
26 671
27 672
28 673
29 674
30 681
31 682
32 683
33 684
34 685
35 811
36 812
37 813
38 1211
39 1212
40 1213
41 1214
42 1301
43 1302
44 1400
45 1410
46 1420
47 1431
48 1432
49 1433
50 1501
51 1502
52 1511
53 1512
54 1513
55 1514
56 1521
57 1522
58 1523
59 1530
60 1600
61 2101
62 2102
63 2200
64 2211
65 2301
66 2302
67 2310
68 2401
69 2402
70 2403
71 2501
72 2502
73 2503
74 2504
75 2511
76 2512
77 2600
78 2611
79 2612
80 2701
81 2702
82 2703
83 2704
84 2705
85 2706
86 2707
87 2710
88 3201
89 3202
90 3203
91 3210
92 3300
93 3310
94 3320
95 3401
96 3402
97 3403
98 3404
99 3405
100 3411
101 3412
102 3420
103 3431
104 3432
105 3441
106 3442
107 3501
108 3502
109 3503
110 3504
111 3511
112 3512
113 3513
114 3514
115 3515
116 3701
117 3702
118 3703
119 3711
120 3712
121 3713
122 3714
123 3715
124 3721
125 3722
126 3800
127 4101
128 4102
129 4103
130 4200
131 4210
132 4300
133 4310
134 4321
135 4322
136 4323
137 4324
138 4400
139 4410
140 4421
141 4422
142 4430
143 4501
144 4502
145 4511
146 4512
147 4513
148 4601
149 4602
150 4603
151 4604
152 4611
153 4612
154 4613
155 4621
156 4622
157 4623
158 4624
159 4625
160 4701
161 4702
162 4703
163 4711
164 4712
165 4713
166 4720
167 4801
168 4802
169 4810
170 4901
171 4902
172 4910
173 5201
174 5202
175 5301
176 5302
177 5310
178 5311
179 5320
180 5400
181 5410
182 5421
183 5422
184 5430
185 5501
186 5502
187 5711
188 5712
189 5713
190 5714
191 5801
192 5802
193 5900
194 5911
195 5912
196 6101
197 6102
198 6103
199 6201
200 6202
201 6203
202 6204
203 6211
204 6212
205 6301
206 6302
207 6311
208 6312
209 6321
210 6322
211 6323
212 6401
213 6402
214 6411
215 6412
216 6501
217 6502
218 6600
219 6611
220 6612
221 6621
222 6622
223 6701
224 6702
225 6703
226 6704
227 6705
228 6710
229 6801
230 6802
231 6803
232 6901
233 6902
234 6910
235 7100
236 7201
237 7202
238 7211
239 7212
240 7213
241 7214
242 7215
243 7220
244 7301
245 7302
246 7303
247 7304
248 7311
249 7312
250 7313
251 7320
252 7401
253 7402
254 7403
255 7404
256 7405
257 7411
258 7412
259 7413
260 7500
261 7510
262 7601
263 7602
264 7603
265 7604
266 7610
267 8101
268 8102
269 8201
270 8202
271 8210
272 8301
273 8302
274 8311
275 8312
276 8321
277 8322
278 8331
279 8332
280 8401
281 8402
282 8411
283 8412
284 8413
285 8421
286 8422
287 8431
288 8432
289 8441
290 8442
291 8443
292 8500
293 8510
294 8521
295 8522
296 8523
297 8524
298 8525
299 8526
300 8527
301 8600
302 8610
303 9011
304 9012
305 9013
306 9020
307 9031
308 9032
309 9033
310 9034
311 9040
312 9051
313 9052
314 9061
315 9062
316 9071
317 9072
318 9080
319 9090
320 9101
321 9102
322 9110
323 9131
324 9132
325 9133
326 9140
327 9150
328 9500
329 9510
330 9520
331 9530
332 9540
333 9550
334 9560
335 9570
travel timesmode=auto, time=2007
minute
time in years
2004 2010 2016 2022 2028 2034 2040 2046 2052 2058
X102
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960
6162636465666768697071727374
757677
78798081828384858687
888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126
127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162
163164165166167168169170171172
173174175176177178179180181182183184185186187188189190191192
193
194195
196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234
235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265
266
267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301
302
303304305
306
307308
309
310
311312313
314
315
316317
318
319
320
321
322
323324325
326
327
328
329
330
331
332
333
334
335
(a) All zones to 201
LegendfourStageTravel/TT/32
scenario 36
1 fourStageTravel/TT/32
travel timesmode=auto, time=2007
minute
time in years
2004 2010 2016 2022 2028 2034 2040 2046 2052 2058
X101
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00 1
(b) Zone 5302 to 201
LegendfourStageTravel/TT/32
scenario 36
1 fourStageTravel/TT/32
travel timesmode=auto, time=2007
minute
time in years
2004 2010 2016 2022 2028 2034 2040 2046 2052 2058
X101
0.00
0.30
0.60
0.90
1.20
1.50
1.80
2.10
2.40
2.70
1
(c) Zone 2710 to 201
LegendfourStageTravel/TT/32
scenario 36
1 fourStageTravel/TT/32
travel timesmode=auto, time=2007
minute
time in years
2004 2010 2016 2022 2028 2034 2040 2046 2052 2058
X102
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80 1
(d) Zone 5900 to 201
Figure 6-7: Baseline auto travel times from various zones to zone 201 (Winnipeg CBD), AM
peak.
Figure 6-8 shows accessibility plotted on the Winnipeg zone map for different modes, for three
projection years. The accessibility measure for a given zone is the number of jobs accessible
within a specified threshold time (30 minutes is used here). For all but a few zones, employment
accessibility increases over time. As the baseline scenario does not include network
improvements, the increasing zonal accessibilities are due to allocated employment growth.
68
Auto employment accessibility
2007 2016 2031
Transit employment accessibility
2007 2016 2031
Walk employment accessibility
2007 2016 2031
Figure 6-8: Thematic employment accessibility maps of Winnipeg TransPLUM baseline
scenario. Accessibility is measured in number of jobs accessible within 30 minutes during
the AM peak hour.
69
Chapter 7 Coordination Approaches
7 Coordination Approaches
This section returns to the notion of land use and transportation coordination introduced in
Section 3.2, represented graphically as the dotted lines labeled Planner Feedback in Figure 3-1
and Figure 5-1.
Section 7.1 defines the term feedback in the context of the TransPLUM framework. Section 7.2
describes the development of a land utilization indicator intended to assist TransPLUM users
with coordination.
7.1 Feedback Paradigms
It is worth making a distinction between the term feedback as often used in dynamic systems
modelling, and feedback used in the context of TransPLUM‘s planner feedback. Generically,
feedback describes a situation in which some aspect of a system‘s state is observed, and that
observation is subsequently used in the control of a process which ultimately feeds back to
impact said system‘s state.
Used in the dynamic systems modelling field, feedback usually refers to model structure which
formalizes and endogenizes a feedback process using algorithms, mathematical equations and
parameters. This is done to represent some aspect of system‘s behaviour – be it physical,
economic or social. In fact, this type of endogenous feedback is present throughout TransPLUM
and a good example is that of the regional population cohort-survival model. The absolute
number of births which the model projects for the time period t is calculated based on the
regional population of women of child-bearing age from the previous time period, t-1. Starting at
some future time period (e.g., t+15) the population of women of child-bearing age will have
been influenced by the births at period t, thus completing the population → births → population
feedback loop. This feedback structure is ―baked‖ into TransPLUM‘s population model and its
purpose can be characterized as one of prediction, at least within the context of a given scenario.
In contrast, planner feedback, while it adheres to the generic definition of feedback, does not
prescribe formal mathematical statements representing controller behaviour, although it does not
70
preclude such formality. Rather, the dotted-line planner feedback represented in Figure 3-1 and
Figure 5-1 represents the discretionary capability of the model user to adjust a reference land use
- transportation plan combination in response to their expected outcome. This feedback operates
at a layer above the core TransPLUM framework; its purpose can be characterized as one of
iterative expectation, control and learning19
. Possible planner feedback responses are:
Adding network capacity to a reference plan in order to mitigate projected delays on
specific links. This is a concept long-familiar to transportation planners in the context of
four-stage models and network design.
Removing planned network capacity increases of a particular mode, to areas well
serviced by other modes. This too is a concept familiar to transportation planners in
activities such as transit route rationalization.
Increasing planned densities of specific areas to take advantage of planned transportation
infrastructure and high levels of service.
Decreasing planned densities of specific areas, anticipating of poor levels of
transportation service.
The default planner feedback mechanism is judgment and trial-and-error based. In practice,
setting and adjusting the rich multi-dimensional land use controls manually, cell-by-cell, is time-
consuming and therefore ―helper‖ scripts (called views in the whatIf? platform) may be created
to partially or fully automate feedback operations. ―Broad brush‖ adjustments can be made and
scenarios created, through views, and subsequently refined manually if required. They key point
here is that planner feedback mechanisms are flexible, interchangeable, and no single feedback
method is prescribed by TransPLUM. The following Section 7.2 proposes a particular feedback
helper – a land use utilization indicator.
Due to time constraints, this project did not explore automated methods of adjusting the
properties and topologies of evolving multi-modal networks.
19 In the control theory literature this is sometimes referred to as a second-order cybernetic system.
71
7.2 Land Utilization – the Density-Accessibility Ratio
This section proposes an indicator to relate the outcomes of land use and transportation plans,
and to serve in their coordination.
7.2.1 Concepts
The indicator is premised on the following line of thinking. If a zone is endowed with a given
level of accessibility, is there an ―appropriate‖ corresponding density level (or range of density
levels) for that zone? If there is, let it be referred to as the normative zonal density. Then, if the
zone‘s actual density is greater than its normative density it may be considered over-utilized. The
converse also applies – if the zone‘s density is less than its normative density it may be
considered under-utilized.
This indicator sets the stage for coordination of land use and transportation plans via a planner
feedback scheme, shown in Figure 7-1.
If zone is over-utilized
Consider… Decreasing planned
zonal density and/or
Increasing transportation service
to/from the zone
If zone is under-utilized
Consider… Increasing planned
zonal density
and/or Decreasing transportation service
to/from the zone
Figure 7-1: Planner feedback scheme based on zonal utilization.
This conceptual foundation raises several practical, inter-related questions:
1. What should the measures of zonal density and accessibility be?
2. How are normative densities determined? What is the functional form that produces
normative density, given accessibility?
3. How should zonal densities and accessibilities be related to indicate the degree of
over/under- utilization. What is the functional form?
72
4. How exactly are policy controls (land use plans, network plans) adjusted in response to
over/under utilization? How is the nature and magnitude of a density and/or network
adjustment determined?
This project does not engage in a rigorous exploration of these questions, which poses a
significant research effort in its own right. However, questions 1, 2 and 3 are provisionally
addressed in the remainder of this section. Question 4 is not addressed further – beyond the
simple scheme laid out in Figure 7-1 – except to say that the starting point for policy control
feedback is purely judgment based. Some degree of planner feedback automation is plausible,
perhaps even to the extent that an iterative feedback view could be run to equilibration.
Returning to question 2 above, it would seem that the specification of an absolute normative
density as a function of accessibility should be backed by empirical multi-regional comparative
research, but also normative models of urban structure. The analysis would be subjective and the
results would almost inevitably be contentious. Therefore, for the purpose of this project, a
relative normative density is used, in which relative applies to zones within the study area, the
Winnipeg Capital Region. How relative normative densities are defined will soon become clear.
A provisional answer to questions 1 and 3 (units of measure, utilization function) is as follows.
For a zone i, let
i
iii
grossArea
employmentpopulationdensity
( 7.1 )
accessibilityi = number of accessible jobs from zone i within t minutes ( 7.2 )
and let
ktyBenchmaraccessbilityaccessibli
chmarkdensityBendensitynRatioutilizatio
i
ii
/
/
( 7.3 )
The equations above are considered provisional for several reasons. First, zonal density ( 7.1 ) is
defined as the sum of population and jobs divided by gross zonal area, a crude density measure
used in other growth management settings (Ministry of Public Infrastructure Renewal, 2006).
Population and jobs are weighted equally, but perhaps a non-equal weighting might be better
73
suited to this purpose. ( 7.2 ) offers a simple employment-accessibility measure, the same
described in Section 6.3 and used in Figure 6-8. It could be made more specific (e.g.,
accessibility to school enrollment) or more general (e.g., to include residential activity). There
are other more sophisticated accessibility indices which weight zonal activities using continuous
impedance functions, as opposed to the hard ―all or nothing‖ time threshold t; perhaps these are
worth experimentation in this context. Finally, accessibility can be defined over one or multiple
modes.
The proposed utilization indicator ( 7.3 ) is a ratio of scaled zonal density to scaled zonal
accessibility. Scaling is accomplished through density and accessibility benchmark constants
whose values are arbitrary, but for this project are chosen so that the median utilization value
equals 1 in the base year. The benchmark values imply the normative zonal densities, and the
benchmarks are set with respect to the Winnipeg base-year zonal utilizations – hence the relative
nature of the normative densities described above.
Ultimately, the open questions discussed above regarding the formulation of the utilization
indicator can only be addressed through experimentation and review with planning professionals
and experts, in order to best align the utilization outputs with professional judgment. After all,
the indicator is intended to be a professional judgment aid and so that is the standard against
which it should be calibrated.
7.2.2 Provisional Results
This section describes a first attempt at applying the utilization indicator proposed in the
preceding Section 7.2.1 to Winnipeg TransPLUM‘s 2006 base year, using AM peak
accessibilities. The accessibility metric used is that of ( 7.2 ), but transit-based, and the value of
the threshold time t is set at 30 minutes. The density and accessibility benchmarks are set at 10
persons and jobs per acre, and 16,330 transit-accessible jobs within 30 minutes, respectively.
These benchmark settings result in the median zonal utilization indicator having a value of 1.
Thus a zone with utilization value above 1 may be considered over-utilized with respect to transit
accessibility; and vice-versa, below 1 may be considered under-utilized.
74
The distribution of zonal utilizations is skewed. Naturally, half of the zones have utilization
values less than 1, but the mean value is 2.63 and the maximum is 42.52. Zones without transit
accessibility20
are excluded.
Figure 7-2: Thematic map of utilization indicator from Winnipeg TransPLUM 2006 base
year. AM peak hour accessibilities used.
The utilization results are mapped in Figure 7-2. Overall, the emergent pattern can be described
as over-utilized in the downtown area, under-utilized in the mature inner ring and over-utilized
near the City boundaries.
Examples of specific zones, their densities, accessibilities and utilization values are provided as
follows.
20 According to the modelled restrictions on the transit mode presented in Appendix E.
75
Zone 472den: 92.25
acc: 148,815
util: 1.01
Zone 3515den: 26.42
acc: 41,724
util: 1.03
Figure 7-3: Example of two zones with median utilization values.
Figure 7-3 shows two zones, both with utilization values close to one (the median utilization).
One zone is within the downtown area and contains primarily commercial-use buildings (zone
472); the other (zone 3515) is approximately 7 km outside the CBD and contains residential and
employment land uses. This comparison demonstrates that, according to this utilization metric,
zones with markedly different densities, land use types and locations can produce similar
utilization values due to varying zonal accessibilities. However, the fact that both these two
zones correspond to ―middle of the pack‖ utilization levels does not provide any intuitive
interpretation of the metric.
76
Zone 462den: 22.08
acc: 125,685
util: 0.28
Figure 7-4: Example of downtown zone with low utilization value.
The zone highlighted in Figure 7-4 does provide some intuitive confirmation of the metric. Here,
zone 462 is located within the downtown area and therefore has accessibility to a large number
of jobs via transit. It contains the Manitoba Legislative Building, surrounded by sprawling
grounds, and therefore shows a relatively low density. The result is a utilization indicator value
of 0.28 suggesting relative under-utilization.
Of course, it is the role of the planner to interpret such results in the context of existing zone-
specific uses. This example is not indented to suggest that the site of an important civic building
should be redeveloped to contain high-density office towers!
77
Zone 4802den: 13.41
acc: 1,310
util: 16.72
Figure 7-5: Example of a low-density suburban zone near City boundary.
A final example of the utilization indicator is shown in Figure 7-5. This zone, near the edge of
the City, contains mainly low-density residential development. In the context of poor transit-
based accessibility to jobs it yields a utilization value of 16.72, suggesting relative over-
utilization.
78
LegendtravelInd/denAccessRat/34
scenario 36
1 9090
2 9102
3 9110
4 9140
5 9020
6 9034
7 9040
8 9051
9 9052
10 9062
11 9080
12 9071
13 9072
14 6710
15 9101
16 9133
17 9132
18 9150
19 9012
20 9013
21 9033
22 9031
23 2705
24 2707
25 2704
26 2600
27 9032
28 3800
29 3722
30 4910
31 9061
32 4810
33 5900
34 5912
35 5911
36 6910
37 6902
38 6704
39 6701
40 6622
41 7510
42 7500
43 7601
44 7604
45 7610
46 8526
47 8610
48 9131
49 9011
50 1530
51 1600
52 2504
53 2503
54 2702
55 2701
56 2706
57 2703
58 2612
59 2512
60 2402
61 2710
62 2403
63 3702
64 3703
65 3701
66 3721
67 3715
68 3714
69 3713
70 3711
71 3512
72 3511
73 3441
74 3432
75 3320
76 3431
77 3310
78 3210
79 3201
80 2211
81 2102
82 2101
83 2310
84 1214
85 1213
86 1211
87 1301
88 1400
89 1501
90 1511
91 1513
92 8600
93 8510
94 8443
95 8442
96 8441
97 8332
98 8331
99 8210
100 8102
101 8101
102 811
103 812
104 202
105 201
106 262
107 261
108 241
109 243
110 461
111 462
112 463
113 464
114 6201
115 6203
116 6204
117 6301
118 6302
119 6401
120 6501
121 6611
122 6600
123 6801
124 6802
125 6901
126 6703
127 6702
128 6621
129 6612
130 6502
131 7403
132 6412
133 7401
134 7402
135 7405
136 7413
137 7412
138 7603
139 7602
140 8402
141 8524
142 8413
143 8525
144 8523
145 8522
146 8500
147 8432
148 8431
149 8322
150 8321
151 8202
152 8201
153 7100
154 813
155 214
156 211
157 252
158 251
159 242
160 253
161 465
162 466
163 6202
164 6103
165 6211
166 6311
167 6321
168 6411
169 6402
170 6705
171 6803
172 6323
173 7404
174 7303
175 7304
176 7411
177 7320
178 7313
179 8401
180 8412
181 8411
182 8527
183 8521
184 8422
185 8421
186 8312
187 8311
188 8302
189 8301
190 7220
191 7212
192 674
193 673
194 213
195 212
196 255
197 254
198 471
199 472
200 473
201 684
202 6102
203 6101
204 6212
205 6312
206 6322
207 7301
208 7302
209 7312
210 7311
211 7215
212 7214
213 7211
214 672
215 671
216 682
217 681
218 683
219 685
220 7213
221 7201
222 7202
223 1514
224 1522
225 1512
226 1521
227 1432
228 2501
229 2502
230 2511
231 2611
232 2401
233 3300
234 3405
235 3403
236 3404
237 3402
238 3412
239 3411
240 3420
241 3501
242 3502
243 3503
244 3712
245 3504
246 3514
247 3513
248 3515
249 3442
250 3401
251 3202
252 3203
253 2200
254 2302
255 2301
256 1302
257 1431
258 1212
259 1410
260 1420
261 1502
262 1523
263 1433
264 5801
265 5714
266 5713
267 5711
268 5501
269 5421
270 5430
271 5320
272 5202
273 5201
274 4321
275 4103
276 4102
277 4101
278 4210
279 4200
280 4300
281 4310
282 4410
283 4400
284 4501
285 4502
286 4601
287 4602
288 4603
289 4701
290 4703
291 4901
292 4902
293 4802
294 4801
295 4720
296 4612
297 4625
298 4624
299 5502
300 5712
301 5802
302 5422
303 5410
304 5310
305 5311
306 5301
307 4323
308 4322
309 4324
310 4421
311 4511
312 4513
313 4711
314 4712
315 4713
316 4604
317 4702
318 4611
319 4613
320 4623
321 4621
322 5400
323 5302
324 4430
325 4422
326 4512
327 4622
density-accessiblity ratiomode=transit, LUAct=popAndEmp
time in years
2004 2010 2016 2022 2028 2034 2040 2046 2052 2058
X101
0.00
0.50
1.00
1.50
2.00
2.50
3.00
3.50
4.00
4.50
5.00
12345678910111213141516171819202122
23
24
25
26
2728
29
30313233
34
35
36
37
38
39
40
414243
4445
46
47
48
49
5051
5253
54
55
5657
5859606162
63
64
6566
676869
707172
73
747576777879
80
81828384858687888990
91
92
93949596979899100101102
103
104105106107108109110111112113114115116117118119120121122123
124
125126127128129130131132133134135136137
138
139140141
142143
144
145
146147148149150151152153154
155156157158159160161162163164165166167168169
170171
172173174175176177178
179
180
181182
183
184
185
186187188189190191192193194195196
197198
199200201202203204205206207208209210211212213214215216217218219220221222
223224
225
226227228
229
230231232233234235
236
237238239240241
242243
244245246247
248249250251252253254255256257258
259
260
261
262
263
264
265
266
267
268269270271272273274275276277278279280
281
282
283
284
285286
287288289290
291
292
293294
295296297298
299
300301
302303304305306307308309
310
311312313314315316
317
318319
320321
322
323
324
325
326327
Figure 7-6: Zonal utilization indicator values for the baseline scenario, projected over time.
Up to this point the utilization indicator has been presented as a static concept, applicable to
snapshots of urban form. However, within the context of a dynamic integrated urban model such
as TransPLUM, the indicator can be applied to an evolving time-series projection of a City, as
shown in Figure 7-6. This adds another dimension to the indicator, extending its interpretation to
include the direction, magnitude and rate of change of zonal utilizations under specific
assumptions and policies.
It would appear that the concept of a utilization indicator based on the ratio of zonal density to
accessibility is fairly unique one, at least in the context of a planning support model such as
TransPLUM. An example of the ratio is found in the literature (Heikkila and Peiser, 1992) but in
this case the measured used was the inverse of utilization – accessibility over density – as a
means to generate land rents.
79
Chapter 8 Conclusion
8 Conclusion
8.1 Summary of Contributions
This project has resulted in the development of a sketch model, TransPLUM, to support
coordinated land use and transportation planning at the regional scale – a generally overlooked
but important segment of urban models offered. The model was implemented using the
Winnipeg Capital Regional as a pilot study area, and a baseline scenario was created.
Two areas of innovation are notable. First is the general application of commercially available
modelling software to design, configure and integrate a tool with a focus on rapid analysis,
model transparency and scenario management. Second, more specifically, is a proposed
utilization indicator – a density-accessibility ratio – which identifies the relative utilization of a
zone and might serve as a coordinating mechanism.
8.2 Evaluation
The tool was developed to enable coordination of regional land use - transportation plans, and to
enable rapid scenario analysis.
With respect to the coordination objective, the tool accepts independent land use and multi-
modal network plans, and uses a deterministic model structure to project outcomes. The
responsibility for coordination is left in the hands of the user, to interpret projected land use and
travel patterns, and to adjust the plans with a view towards increased efficiency, compatibility
and desirability. Fundamentally the tool does not ensure coordination but rather provides an
environment for assembling, managing and visualizing land use and transportation plans ―side-
by-side‖, thereby extending the perception of planners and increasing the likelihood of
coordinated plans. Compared to the disjoint manner in which many regional planning authorities
operate, this tool represents a significant advancement, both technically and from an institutional
integration perspective.
80
While it is premature to evaluate the effectiveness of the proposed utilization indicator, it looks
to be a promising means of helping planners balance land use and transportation plans.
With respect to the objective of enabling rapid scenario analysis, or ―sketch‖ modelling,
experience from this project is not sufficient to gauge the level of success. A baseline scenario
was constructed for this project, and in doing so several intermediate scenarios were created –
incrementally and rapidly. However, it is the ability to create significantly different scenarios
which is of greater interest. Alternate land use plans in TransPLUM can be specified in a quick
―broad brush‖ manner through judicious groupings of zones, development types and their
assigned policy controls. However, the ability to quickly sketch network plans is dependent on a
sufficiently aggregate representation of base and future networks – a criterion not satisfied in the
pilot Winnipeg TransPLUM due to time constraints. The reality is that while TransPLUM offers
much structure geared towards simplified, quick planning, it is not a ―silver bullet‖ – there is still
significant effort required in preparing even strategic-level network inputs.
8.3 Future Work and Improvements
This final section lists several areas for further research and development on TransPLUM. It is
divided into work related to the generic TransPLUM structure, and that related to the specific
Winnipeg TransPLUM implementation.
It is also worth noting that many of the design decisions made in this project revolve around
trade-offs between parsimonious structure versus disaggregation and comprehensiveness.
Proposed model improvements tend to be biased toward increased complexity, as is the case with
several items listed here. Nevertheless, the original sketch goals of the model should not be
forgotten in the consideration of these items.
8.3.1 Generic Model
Further research and development areas related to the generic TransPLUM model are:
More extensive exploration and testing of the utilization indicator. Cross-regional
comparative analysis could be particularly useful in determining standards for density-
accessibility benchmarks.
81
Building on a better-understood utilization indicator, automated feedback mechanisms to
TransPLUM‘s land use plum could be developed, which would be balance-seeking.
Tighter software integration between PLUM and the travel model (TransCAD) could be
developed, with respect to: data transfer efficiency; and also travel model transparency
(i.e., ―cracking open‖ the travel model logic within the whatIf? platform).
Consideration of urban freight movement model structure.
Building aspects of dynamics and inertia into the travel model such that trip distribution
for a given time point is influenced by prior distributions (i.e., lasting impacts of
established land use and travel patterns).
8.3.2 Specific Winnipeg Implementation
Further research and development areas related to the specific Winnipeg TransPLUM
implementation are:
A more comprehensive travel model validation exercise, and comparison to more detailed
travel models.
Mode split model calibration.
Specification of a refined zone system – in particular for larger zones near the City‘s
boundary.
A review of the model‘s data categories, and mappings from Census and municipal data
sources. In particular, a review of employment-related floorspace categorization in the
City‘s assessment database would be useful.
Research into technical best-practices for specifying evolving multi-modal networks
using TransCAD.
Preloading observed and estimated truck flows onto the road network.
Creation of several substantively-varying land use – transportation scenarios for the
Winnipeg Capital Region. In particular, assumptions and policies for the surrounding
82
rural municipalities should be developed in collaboration with the local governments.
Also, growth of educational facilities should be researched and incorporated in the
scenarios.
Developing mode split models for all model trip purposes, other than home-to-work.
83
References
Allen, E. 2001. INDEX: Software for community indicators. In Planning support systems:
Integrating geographic information systems, models and visualization tools, eds. Richard
K. Brail, Richard E. Klosterman, 229-262. Redlands, CA: ESRI Inc.
Alonso, W. 1964. Location and land use: Toward a general theory of land rent. Cambridge, MA:
Harvard University Press.
Altus Clayton. 2009. Downtown Winnipeg employment study.
———. 2008. City of Winnipeg comprehensive employment lands strategy.
Altus Group Economic Consulting. 2009. City of Winnipeg commercial land strategy.
Anas, A. 1995. METROSIM: A unified economic model of transportation and land-use. Paper
presented at Travel Model Improvement Program Land Use Modeling Conference.
Baynes, T., J. West, and G. M. Turner. 2009. Design approach frameworks, regional metabolism
and scenarios for sustainability. In The dynamics of regions and networks in industrial
ecosystems., eds. M. Ruth, B. Davidsdottir, 97. Gloucestershire: Edward Elgar
Publishing.
Ben-Akiva, Moshe E. 1985. Discrete choice analysis : Theory and application to travel demand,
ed. Steven R. Lerman. Cambridge, Mass.: MIT Press.
Bish, L., and R. Hoffman. 1993. Integrating land use and transportation planning: A strategic
approach. Plan Canada (January 1993).
Box, G. E. P. 1979. Robustness in the strategy of scientific model building. Wisconsin
University-Madison Mathematics Research Center.
Caliper Corporation. 2008. Travel demand modelling with TransCAD 5.0 user's guide. Caliper
Corporation.
City of Winnipeg – Office of the CFO. Adjusted population forecast 2009 to 2031. in City of
Winnipeg [database online]. Winnipeg, Manitoba, 2009 [cited 07/15 2010]. Available
from http://www.winnipeg.ca/cao/pdfs/AdjustedPopulationForecast2009To2031.pdf.
Echenique, M. H., A. D. J. Flowerdew, J. D. Hunt, T. R. Mayo, I. J. Skidmore, and D. C.
Simmonds. 1990. The MEPLAN models of Bilbao, Leeds and Dortmund. Transport
Reviews 10, (4): 309-22.
Ellickson, B. 1981. An alternative test of the hedonic theory of housing markets. Journal of
Urban Economics 9, (1): 56-79.
Ettema, D., K. Jong, H. Timmermans, and A. Bakema. 2007. PUMA: Multi-agent modelling of
urban systems. Modelling Land-use Change: 237-58.
Garin, R. A. 1966. A matrix formulation of the Lowry model for intrametropolitan activity
allocation. Journal of the American Planning Association 32, (6): 361-4.
Gault, F. D., K. E. Hamilton, R. B. Hoffman, and B. C. McInnis. 1987. The design approach to
socio-economic modelling. Futures 19, (1): 3-25.
84
Goldner, W. 1968. Projective land use model (PLUM). Berkeley, California: Bay Area
Transportation Study Commission.
Hanson, Nancy, and McKeever, Michael. San Diego, California: The PLACE3S analysis method
in Federal Highway Administration [database online]. 2009 [cited July 11, 2010 2010].
Available from http://www.fhwa.dot.gov/tcsp/sdcadflt.html.
Hatzopoulou, M., and E. J. Miller. 2009. Transport policy evaluation in metropolitan areas: The
role of modelling in decision-making. Transportation Research Part A: Policy and
Practice 43, (4): 323-38.
Heikkila, E. J., and R. B. Peiser. 1992. Urban sprawl, density, and accessibility. Papers in
Regional Science 71, (2): 127-38.
Horowitz, A. J. 2004. Lowry-type land use models. In Handbook of transport geography and
spatial systems., eds. David A. Hensher, Kenneth J. Button, Kingsley E. Haynes and
Peter R. Stopher. 1st ed., 167-183. Oxford: Elsevier.
Hunt, J. D., and J. E. Abraham. 2003. Design and application of the PECAS land use modeling
system. Paper presented at 8th Computers in Urban Planning and Urban Management
Conference, Sendai, Japan, .
Hunt, J. D., D. S. Kriger, and E. J. Miller. 2005. Current operational urban land-use–transport
modelling frameworks: A review. Transport Reviews 25, (3): 329-76.
Hurl, D. 1996. Internal report - transportation planning model improvements: Modal split model.
Winnipeg, Manitoba: City of Winnipeg, .
iTRANS Consulting Inc. 2009. 2007 Winnipeg area travel survey results - draft. Ottawa, Ont.:
iTRANS Consulting Inc., Project 4144.
Johnston, R. A. 2004. The urban transportation planning process. In The geography of urban
transportation., eds. S. Hanson, G. Giuliano. 3rd ed., 115. New York: Guilford Press.
Jones, P., ed. 1990. Developments in dynamic and activity-based approaches to travel analysis.
Avebury.
Klosterman, R. E. 2001. The whatIf? planning support system. In Planning support systems:
Integrating geographic information systems, models and visualization tools, eds. Richard
K. Brail, Richard E. Klosterman, 263-284. Redlands, CA: ESRI Inc.
Klosterman, R. E., and C. J. Pettit. 2005. An update on planning support systems. Environment
and Planning B: Planning and Design 32, (4): 477-84.
Kockelman, K., B. Zhou, and S. Tirumalachetty. 2009. NCTCOG‘s land use modeling
workshop: Meeting minutes.
Kwartler, M., and R. N. Bernard. 2001. CommunityViz: An integrated planning support system.
In Planning support systems: Integrating geographic information systems, models and
visualization tools., eds. Richard K. Brail, Richard E. Klosterman, 285-308. Redlands,
CA: ESRI Inc.
Landis, J. 2001. CUF, CUF II, and CURBA: A family of spatially explicit urban growth and
land-use policy simulation models'. In Planning support systems: Integrating geographic
information systems, models and visualization tools., eds. Richard K. Brail, Richard E.
Klosterman, 157-200. Redlands, CA: ESRI Inc.
85
Lee, David J-H. 2010. 2009 TMA/MPO modeling activity survey. Fredericksburg Area
Metropolitan Planning Organization.
Lee, Douglass B.,Jr. 1973. Requiem for large-scale models. Journal of the American Institute of
Planners 39, (May): 163-78.
Lowry, I. S. 1964. A model of metropolis. Santa Monica, CA: Rand Corp., RM-4035-RC.
Martin, V. 2010. Internal whitepaper: Population and land use model (PLUM). Kitchener,
Ontario: Region of Waterloo.
Martinez, F. 1996. MUSSA: Land use model for Santiago city. Transportation Research Record:
Journal of the Transportation Research Board 1552, (-1): 126-34.
MetroQuest. [cited July 11, 2010 2010]. Available from http://www.metroquest.com.
Meyer, M. D., and E. J. Miller. 2001. Urban transportation planning: A decision-oriented
approach. New York: McGraw-Hill.
Miller, E. J. 2009. Graduate course lecture slides: Transportation and development (CIV 1535F).
Toronto: University of Toronto.
Miller, E. J., D. S. Kriger, and J. D. Hunt. 1998. TCRP web document 9 integrated urban models
for simulation of transit and land-use policies final report. [S.l.]|b National Academy
Press: National Academy Press.
Ministry of Public Infrastructure Renewal. 2006. Places to grow: Growth plan for the greater
golden horseshoe. Toronto, Ontario: Queen's Printer for Ontario.
Modelistica. TRANUS: Integrated land use and transport model. in Modelistica [database
online]. 2007 [cited 07/11 2010]. Available from
http://www.modelistica.com/GeneralDescriptionTranus.pdf.
Office for Urbanism. 2009. City of winnipeg residential land and infill strategy (draft).
Ortuzar, J., and L. G. Willumsen. 2001. Modelling transport. 3rd ed. Chichester: Wiley.
Putman, S. H. 1995. EMPAL and DRAM location and land-use models: An overview. Paper
presented at the TMIP land-use modelling conference, Dallas, TX.
Salvini, P., and E. J. Miller. 2005. ILUTE: An operational prototype of a comprehensive
microsimulation model of urban systems. Networks and Spatial Economics 5, (2): 217-
34.
Southworth, F. 1995. A technical review of urban land use-transportation models as tools for
evaluating vehicle travel reduction strategies. Oak Ridge, TN: Oak Ridge National
Laboratory, ORNL-6881.
Statistics Canada. Population and dwelling counts, for Canada and census subdivisions
(municipalities), 2006 and 2001 censuses - 100% data. in Statistics Canada [database
online]. Ottawa, Ont., 2010 [cited 07/15 2010]. Available from http://www12.statcan.ca/census-recensement/2006/dp-pd/hlt/97-
550/Index.cfm?TPL=P1C&Page=RETR&LANG=Eng&T=301&S=3&O=D.
———. 2007. Age (123) and sex (3) for the population of canada, provinces, territories, census
divisions and census subdivisions, 2006 census - 100% data. Ottawa, Ont.: Statistics
Canada.
86
Strauch, D., R. Moeckel, M. Wegener, J. Gräfe, H. Mühlhans, G. Rindsfüser, and K. J.
Beckmann. 2003. Linking transport and land use planning: The microscopic dynamic
simulation model ILUMASS. Paper presented at 7th International Conference on
GeoComputation, Southampton, http://www.geocomputation.
org/2003/Papers/Strauch_Paper.pdf.
The Conference Board of Canada. 2007. Long-term demographic and economic forecast for
Winnipeg‘s census metropolitan area.
Train, Kenneth. 2009. Discrete choice methods with simulation. 2nd ed. New York: Cambridge
University Press.
Waddell, P., A. Borning, M. Noth, N. Freier, M. Becke, and G. Ulfarsson. 2003.
Microsimulation of urban development and location choices: Design and implementation
of UrbanSim. Networks and Spatial Economics 3, (1): 43-67.
Walker, W. T., S. Gao, and R. A. Johnston. 2007. UPlan: Geographic information system as
framework for integrated land use planning model. Transportation Research Record:
Journal of the Transportation Research Board 1994, (-1): 117-27.
Wardrop, J. G. 1953. Some theoretical aspects of road traffic research. OR 4, (4): 72-3.
Wegener, M. 1995. Current and future land use models. Paper presented at Travel Model
Improvement Program Land Use Model Conference, Dallas.
Winnipeg Transit. 2006. Data manual for transit service analysis system (TSAS). Winnipeg,
Manitoba: Winnipeg Transit.
87
Appendix A: Survey Trip Purpose to Model Trip Purpose Mapping
Table A-1: Survey trip purpose to model purpose mapping where zone of trip origin is the
home zone of the trip maker.
ORIGIN_TZ == HOME_TZ
HBW HBS HBO NHB
[1] Work (usual) 1
[2] Shopping 1
[3] Work-Related (other than usual) 1
[4] School 1
[5] Drive Someone Somewhere 1
[6] Other 1
[7] Return Home 1
[8] Social/Recreation 1
[9] Work on the Road / itinerant workplace / no fixed address 1
[10] Restaurant (Eat In) 1
[11] Pick Someone up 1
[12] Medical/Dental 1
[13] Restaurant (Take-Out) 1
[14] Refused
[15] Don't Know
Table A-2: Survey trip purpose to model purpose mapping where zone of trip origin is note
the home zone of the trip maker.
ORIGIN_TZ != HOME_TZ
HBW HBS HBO NHB
[1] Work (usual) 1
[2] Shopping 1
[3] Work-Related (other than usual) 1
[4] School 1
[5] Drive Someone Somewhere 1
[6] Other 1
[7] Return Home 1
[8] Social/Recreation 1
[9] Work on the Road / itinerant workplace / no fixed address 1
[10] Restaurant (Eat In) 1
[11] Pick Someone up 1
[12] Medical/Dental 1
[13] Restaurant (Take-Out) 1
[14] Refused
[15] Don't Know
88
Drive S
om
eone S
om
ew
here
Medic
al/D
enta
l
Oth
er
Pic
k S
om
eone u
p
Resta
ura
nt (E
at In
)
Resta
ura
nt (T
ake-O
ut)
Retu
rn H
om
e
School
Shoppin
g
Socia
l/R
ecre
ation
Work
-Rela
ted (
oth
er
than u
sual)
Work
(usual)
Work
on the R
oad / itinera
nt
work
pla
ce / n
o fix
ed a
ddre
ss
NHBHB
0
5000
10000
15000
20000
25000
30000
35000
40000
45000
NHB
HB
Figure A-1: 3D barplot of trip frequency by survey trip purpose. AM peak hour trips only.
89
Appendix B: Trip Distribution Validation Scatterplots
0 500 1000 1500
05
00
10
00
15
00
OD_Pred_SUP$HBW_obs
OD
_P
red
_S
UP
$H
BW
0 500 1500 2500
01
00
02
00
03
00
0OD_Pred_SUP$HBS_obs
OD
_P
red
_S
UP
$H
BS
0 500 1000 2000
05
00
15
00
OD_Pred_SUP$HBO_obs
OD
_P
red
_S
UP
$H
BO
0 200 600 1000
02
00
60
01
00
0
OD_Pred_SUP$NHB_obs
OD
_P
red
_S
UP
$N
HB
Figure B-2: Predicted vs. observed trip flows for super-zone (17 x 17) interchanges.
90
Appendix C: Trip Mode Classification Rules
This appendix describes the rules used to classify individual trip records from the Winnipeg Area
Travel Survey (WATS) into the TransPLUM‘s three modelled modes: auto, transit and
walkBike.
Table C-3 lists the WATS modes recorded and Table C-4 shows the mapping from WATS
modes to modelled modes. Due to the fact that each trip record includes up to five mode fields, a
simple mode precedence scheme is applied after the mapping:
1. If any of the five mode fields are of type transit then the trip is classified as transit, else
2. If any of the five mode fields are of type auto then the trip is classified as auto, else
3. If any of the five mode fields are of type walkBike then the trip is classified as walkBike,
else
4. The trip is classified as other, which is ignored
Table C-3: Modes recorded in 2007 Winnipeg Area Travel Survey
MODE
1 car driver
2 car passenger
3 Winnipeg Transit bus
4 intercity bus
5 other transit
6 private transportation service
7 school bus
8 water taxi / ferry
9 Taxi
10 Handi-Transit
11 Bicycle
12 Walk
13 motorcycle/moped
14 other mode
15 don't know
16 Refused
91
Table C-4: Mapping from surveyed modes to modelled modes
Modelled mode to auto transit walkBike
Surveyed mode
from
car driver Winnipeg Transit bus bicycle
car passenger school bus walk
taxi other transit
motorcycle/moped
92
Appendix D: Mode Choice Model Estimation Results
Model1: Home-to-work
Inputs
Total Cases 2664
Cases with bad or missing
choice value
12
Cases with missing
attribute values
5
Valid Cases 2647
Choice Distribution
transit : 201 7.6%
auto : 2272 85.8%
walkBike : 174 6.6%
Maximum likelihood reached at iteration 15
Parameter Estimate Std. Error T Test ASC_TRANSIT -0.639628 0.227695 -2.809139
travelTime -0.011607 0.006208 -1.869647
walkable 0.545438 0.234755 2.323434
walkDist -1.296603 0.213884 -6.062178
bikeable -1.610184 0.438851 -3.669094
bikeDist -0.388737 0.112270 -3.462521
isGT6km -2.990744 0.632532 -4.728210
distIfGT6km 0.538088 0.069382 7.755443
wwtPerDist -0.156734 0.028101 -5.577597
Log-likelihood at
zero
-2765.708475
Log-likelihood at end -1100.742193
-2 (LL(zero) -
LL(end))
3329.932563
Asymptotic rho
squared
0.602004
Adjusted rho
squared
0.598749
Model2: Home-to-school
93
Inputs
Total Cases 1916
Cases with bad or missing
choice value
31
Cases with missing
attribute values
24
Valid Cases 1861
Choice Distribution
transit : 402 21.6%
auto : 885 47.6%
walkBike : 574 30.8%
Maximum likelihood reached at iteration 13
Parameter Estimate Std. Error T Test ASC_TRANSIT -0.323962 0.180255 -1.797246
travelTime 0.011385 0.004245 2.682132
walkable 2.549689 0.170426 14.960698
walkDist -2.200810 0.165813 -13.272876
bikeable -3.376859 0.451361 -7.481506
bikeDist 0.203974 0.082592 2.469667
isGT6km -1.200468 0.434274 -2.764307
distIfGT6km 0.182808 0.040094 4.559485
wwtPerDist -0.069229 0.012071 -5.735013
Log-likelihood at
zero
-2000.321772
Log-likelihood at end -1496.807225
-2 (LL(zero) -
LL(end))
1007.029094
Asymptotic rho
squared
0.251717
Adjusted rho
squared
0.247217
Model3: Home-to-other
Inputs
Total Cases 1485
Cases with bad or missing
choice value
12
Cases with missing
attribute values
1
Valid Cases 1472
Choice Distribution
transit : 47 3.2%
auto : 1338 90.9%
walkBike : 87 5.9%
94
Maximum likelihood reached at iteration 23
Parameter Estimate Std. Error T Test ASC_TRANSIT -2.015738 0.442164 -4.558805
travelTime 0.006033 0.011132 0.541905
walkable 0.513589 0.251184 2.044674
walkDist -3.265314 0.402183 -8.118984
bikeable -4.599030 1.973850 -2.329979
bikeDist -0.270739 0.526239 -0.514478
isGT6km -3.541959 1.327957 -2.667224
distIfGT6km 0.543782 0.141115 3.853461
wwtPerDist -0.170683 0.045350 -3.763700
Log-likelihood at
zero
-1561.203104
Log-likelihood at end -410.945364
-2 (LL(zero) -
LL(end))
2300.515479
Asymptotic rho
squared
0.736776
Adjusted rho
squared
0.731012
Model4: Non-home based
Inputs
Total Cases 834
Cases with bad or missing
choice value
1
Cases with missing
attribute values
1
Valid Cases 832
Choice Distribution
transit : 7 0.8%
auto : 752 90.4%
walkBike : 73 8.8%
Maximum likelihood reached at iteration 21
Parameter Estimate Std. Error T Test ASC_TRANSIT -3.114880 1.112799 -2.799140
travelTime -0.042082 0.036253 -1.160774
walkable 0.968389 0.292471 3.311060
walkDist -3.779180 0.507603 -7.445148
bikeable -2.277350 1.065389 -2.137576
bikeDist -0.477239 0.312368 -1.527812
isGT6km -9.289475 3.732033 -2.489119
distIfGT6km 1.321451 0.467763 2.825044
wwtPerDist -0.036164 0.075315 -0.480164
Log-likelihood at
zero
-886.068332
95
Log-likelihood at end -188.864788
-2 (LL(zero) -
LL(end))
1394.407087
Asymptotic rho
squared
0.786851
Adjusted rho
squared
0.776694
96
Appendix E: Availability Restrictions on Transit and Walk-Bike Modes
Table E-5 presents the restrictions placed on the availability of the transit mode during model
estimation and application at the interchange level. Table E-6 provides the designed availability
restrictions for the walk-bike mode. It was discovered late in the project that an implementation
error led to these restrictions not being enforced for walk-bike shares, and that a small fraction of
walk-bike trips were being predicted for interchanges with distances greater than 10km. This
issue is flagged for correction in future model use, but was determined not to be of significant
concern for the baseline scenario results presented in this report.
Table E-5: Modelled availability restrictions on the transit mode
Criterion Value
Maximum total travel time 150 minutes
Maximum total transfer time 60 minutes
Maximum number of transfers 3
Minimum initial wait / transfer time 2 minutes
Maximum access walk time 20 minutes
Maximum egress walk time 20 minutes
Table E-6: Modelled availability restrictions on the walk-bike mode
Criterion Value
Allowable walk distance 0-2 km
Allowable bike distance 2-10 km