Urban Travel Forecasting:What Was Learned in the Past 50 Years?How Should We Proceed in the Future?

Professor David BoyceDepartment of Civil and Environmental Engineering

Northwestern University, Evanston, Illinois, USA

Computational Transportation Science SeminarUniversity of Illinois at Chicago

April 25, 2007

Overview

• Origins of urban travel forecasting models in practice and in research;

• Model design choices facing the travel forecaster;

• History and constraints of travel forecasting software systems;

• Prospects for the future.

Origins of travel forecasting models

• Travel forecasting, as we know it today, began in the early 1950s:– In practice, to provide a basis for designing post-war

freeway systems, as an outgrowth of earlier surveys of urban travel patterns;

– In research, as an ingenious idea suggested by a new theory of optimization, in the context of basic research on allocation of scarce resources in a post-war civil defense project.

• The former took hold, and was disseminated; the latter was lost for 15 years, and has had little impact on the field, despite its far-sighted implications.

Travel Forecasting Procedure Based on Detroit Study Experience(Carroll and Bevis, Papers of the Regional Science Ass’n, 1957)

The description of the transportation planning process, as found on page 9 of the Final Report of the Chicago Area Transportation Study, Volume I, 1959.

Fig. 20. Recommended Expressway Plan for the Chicago Study Area

Source: CATS (1962, Map 13, p. 64)

I-355

Crosstown Expressway

This plan was recommended by the Chicago Area Transportation Study in 1962. The solid red lines were agreedupon before the Study began in 1955. The dashed lines were proposed additionsto that system. Only one facility on this map, I-355, was built; an extension is nowunder construction. The Crosstown Expressway became highly controversialin the early 1970s, although funding wasavailable to build this facility. Those fundswere later used for arterial roads and transit lines. This plan was the first and last attempt to utilize optimizing methods to design a plan.

Martin Beckmann’s user-equilibrium travel model with variable demand formulated as a constrained, nonlinear optimization problem1

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1. Beckmann, M., C. B. McGuire and C. B. Winsten (1956) Studies in the Economics of Transportation, Yale University Press.

Urban Location and Transportation Systems • Urban activities may be viewed as a spatial system:

– land area, floor area and layout requirements of households, firms and public agencies and services

– desire for spatial separation, light, clean air and environmental amenities

– land availability and suitability for location requirements• Transportation provides connections among activities:

– high density activities require higher capacity systems (e.g. rail transit)

– low density, extensive activities require lower capacity, more flexible systems (e.g. cars on an arterial network)

• Travel times and costs partially determine the relative spacing of activities:– households and workplaces– households and retail firms and services– employment and business services and package delivery

• Relation of travel times/costs to spatial interactions:– unit travel costs increasing with network flows (roads)– unit travel costs decreasing with network flows (some transit)– ability of different modes to serve spatially intensive vs. spatially

decentralized patterns• Land market and regulations: an imperfect mechanism for

coordinating land development, density and thereby travel.• Major cohesive forces that causes large cities to grow:

– transportation services– skilled labor supply– agglomeration and localization economies (availability of specialized

services at one location)– business and public services (why did Boeing move to Chicago?)

• Major forces that cause large cities to disperse:– need to satisfy space requirements at lower cost– desire to move closer to skilled labor force or to employ labor with different

attributes (why did Sears move to the outlying suburbs?)– reluctance and lack of incentives to recycle previously used land

(reuse of brownfields)

One attempt to represent the relationships among urban activities and transportation modes.

PTV America, Inc.

Let’s examine from first principles the attributes of these phenomena, as might be the situation in a place with no prior modeling experience.

• Unconstrained by prior research and practice;• Unconstrained by computational requirements;• Unconstrained by theory and data requirements.

Note: This may be dangerous! But it may offer us some new insights into the phenomena.

A. Basic model primitives

Location of households, employment, urban activities, land use

Travel activities

Traveler classes

Clock time

Transportation technologies (modes)

Networks

C. Network characteristics

Vehicles (single-packet-flow)

Relation of travel delay to:

> flows on links

> clock time

B. Basic dimensions of travel choices

Frequency of travel

Departure time

Origin-destination flow

Mode choice structures

Route choice structures and travel time perceptions

Structure of travel choices

Traveler market segmentation

Framework for the design of a travel forecasting model as a three-dimensional matrix of model attributes.

A. Basic model primitives

Location of households, employment, urban activities, land use

Travel activities

Traveler classes

Clock time

Transportation technologies (modes)

Networks

• Location of households, employment, urban activities and land use– Locations and land development defined by small areas– Locations and land development defined by land parcel– Locations and land development defined on a small grid

• Travel activities– Trips from origins to destinations– Tours, or sequences of trips– Connections between activities (activity-based model)

• Traveler classes– Socio-economic classes (households classified by number of

persons, number of workers, income, number of cars)– Trip purposes, for trip-based and tour-based models

• Clock time– Daily (24 hour)– Period, such as peak-hour (static)– Instantaneous or short interval (dynamic)

• Transportation technologies (modes)– Vehicles only– All movements of persons by mode, including walk, cycle– Freight and persons

• Networks– Nodes (intersections, zone centroids)– Links (directed connections between two nodes)– Travel time and cost/fare (links or origin to destination)

B. Basic dimensions of travel choices

Frequency of travel

Departure time

Origin-destination flow

Mode choice structures

Route choice structures and travel time perceptions

Structure of travel choices

Traveler market segmentation

• Frequency of travel– Trips or tours per time period

• Departure time– Uniform rate during modeling period– Dependent on desired arrival time, or congested travel time– Dependent upon avoiding congested travel conditions

• Origin-destination flow– Demand function for each OD pair (Beckmann’s formulation)– Constrained by total number of departures or arrivals (known as a

doubly-constrained gravity model)– Destination choice function determined by variables describing

destination, and segmented by classes

• Route choice structures and assumptions about perceptions of travel time– Cost minimizing based on perfect information

(deterministic user-equilibrium)– Cost minimizing based on perfect information with random

perception errors (stochastic user-equilibrium)– Cost minimizing based on stochastic link/intersection travel times

with assumption about attitude towards risk• Structure of travel choices (e.g. mode choice)

– Simultaneous (all choices decided at once)– Sequential (sequence of choices, each dependent on the previous)– Hierarchical (choices conditional on other information)

• Traveler market segmentation– Tour type, designating the trip chain in which an individual trip

occurs: work tour, at-work tour, and non-work tour– Chauffeured tours and non-chauffeured tours

C. Network characteristics

Vehicles (single-packet-flow)

Relation of travel delay to:

> flows on links

> clock time

• Vehicles– Discrete or Packets (individual or groups of vehicles)– Continuous (flows of vehicles)– Scheduled (headways or timetable)

• Relation of traffic congestion to flows on network links– Delay depends on each link’s own flow (separable)– Delay depends on each link’s own flow plus traffic controls that

depend on other flows– Delay depends directly or indirectly on all flows (non-separable)

• Relation of traffic congestion to clock time– Delay depends on current flow only, or on current and future flow– Delay depends on current and future flow, and on unknown

incidents– Delay depends on boarding and alighting passengers, and number

of persons in vehicle

Attributes of traditional travel forecasting models• Basic primitives

– Activity locations defined by traffic analysis zones– Trip-based, origin to destination– Classes defined by trip purposes, with socio-economic segmentation– Daily (24 hour) or Period, such as peak-period– Sometimes vehicular travel only, including trucks– Networks defined by nodes, links with travel time/cost

• Basic dimensions: four models solved sequentially/feedback– Trips per time period with implied uniform departure rate– Origin-destination flow constrained by number of departures and

arrivals (doubly-constrained gravity model)– Nested logit mode choice model– Cost minimizing route choice (deterministic user-equilibrium)

• Basic network characteristics– Continuous flows of vehicles– Delay depends on each link’s own flow (separable)– Delay depends on current flow only

Attributes of integrated travel forecasting models

• Basic primitives– Activity locations defined by traffic analysis zones– Trip-based or tour-based, origin to destination– Classes defined by trip purposes, with socio-economic segmentation– Multiple periods, such as peak and shoulder periods– Person and vehicular travel, including trucks– Networks defined by nodes, links with travel time/cost

• Basic dimensions: one integrated model of defined choices– Trips per time period, exogenous or endogenous– Origin-destination, mode and time period choices defined as flows

and constrained by number of departures and arrivals – Cost minimizing route choice by period (deterministic user-equilibrium)– Solved by an iterative algorithm to precise convergence

• Basic network characteristics– Continuous flows of vehicles– Delay depends on each link’s own flow (separable or non-separable)– Delay depends on current flow only

Dest Choice / Mode Choice / Period Choice / Route Choice

Activity Frequency(Trip Generation)

Destination Choice(Trip Distribution)

Mode Choice

Route Choice(Traffic Assignment)

Activity Frequency(Trip Generation)

Sequential Procedure

Feedback

Integrated Model

Consistent levels of service with a precise

user-equilibrium solution

Input data: iO and jD by trip purpose

Road network

Compute the initial solution for 1:k Initialize travel costs 1ijc

Solve Trip Distribution 1)1( ijij de

Assign 1ijd to road network 1af

Compute the solution for 1: kk Compute average OD cost kcij

Solve Trip Distribution keij

Check convergence of keij to 1kd ij :

TMF = E1 ij

ijij kekd , or

RSE = E1

2/1

2

ijijij kekd

If converged, then STOP; if not, continue.

Assign kd ij to road network to desired level

of convergence of excess route costs kfa

Average trip matrices 1kd ij and keij :

CW: kekdkd ijij W11W ,

or

MSA: kek

kdk

kkd ijij

1

11

Legend: k – Loop index W – Weight for averaging matrices E – Feedback convergence target CW – Constant Weights MSA – Method of Successive Averages TMF – Total Misplaced Flow RSE – Root Squared Error

Feedback by Averaging of OD Matrices

Problems requiring travel forecastsfor transportation systems planning

• Systems or network planning:– Determine system layout or configuration– Determine spacing of facilities by type (e.g., freeway, arterial,

collector; rail, bus, shuttle)– Determine overall capacities of facilities (vehicles, persons per

hour)

• Subsystem or modal planning:– Determine intersection lane capacities, signal system design– Coordinate signal system design– Determine transit frequencies (headways), vehicle size– Coordinate transit services among submodes

• Staging of facility and service improvements:– Determine annual and multi-year improvement programs– Find optimal staging of project implementation

• Assessment of environmental, energy and social consequences of transportation systems– Determine total emissions (NOx, CO2, SO2) and energy

consumption by year, facility type, and subregions– Determine equity and fairness measures (termed environmental

justice in USA)– Determine which travel classes, trips, time periods are impacted by

a given system improvement

Relationship to Location and Land Use Planning

• Extent and scale of transportation systems is determined by location, density and scale of land use pattern and the associated pattern of urban activities;

• Effectiveness and efficiency (cost) of alternative transportation technologies (modes) depends on the extent, density and layout (clustering or dispersion) of urban activities;

• To be most effective, land use and transportation systems planning must be coordinated and undertaken jointly.

History of travel forecasting software systems• The origins of travel forecasting software may be traced to

the first use of main frame computers in this field in 1958;• Software systems called “batteries” were developed by the

Federal Government and its consultants in the 1960s;• These were reorganized and extended during the 1970s

as the Urban Transportation Planning System (UTPS);• Consultants to the Federal Government and transportation

studies also developed software systems, in part with the support of Control Data Corporation, which sought service contracts (TranPlan, and later MinUTP);

• During the 1980s new software systems were developed from the findings of academic research based on the PC (EMME/2, TransCAD, SATURN, VISION System);

• Since the 1990s, a consolidation of software systems has occurred, resulting in four principal systems (CUBE, EMME, TransCAD, VISION) and a few systems found in selected global regions (ESTRAUS, SATURN, TRACKS, TRANUS).

Constraints imposed by software systems

• Nearly all software is based on the traditional sequential procedure view of travel forecasting;

• As a result, the capabilities offered are basically toolkits for implementing and solving specific models, and sequences of models, as found in practice;

• These capabilities are linked together by menus, scripts and other ad hoc methods;

• Only one software vendor offers a specially designed solution procedure based on the integrated model concept (MCT’s ESTRAUS);

• General purpose solvers for integrated models, formulated as optimization problems, are not efficient for the large-scale implementations found in this field; micro-simulation remains impractical and may omit important relationships.

• Professional practice and training of practitioners is increasingly related to one or more of these software systems, which are often seen as “black boxes” by users.

Prospects for the Future

• Need for better informed decisions is increasing (global warming, resource shortages, equity around the world);

• Implications of bad decisions are not confined to wasted resources, since system equilibria will adjust to the realities, and the least efficient urban cities will decline (St. Louis, Detroit in the US; Russia, Britain in the world economy);

• Opportunities to create more livable and productive urban environments may be lost, if decisions are not improved;

• Progress in advancing travel and location models is slow and evolutionary, but capability to apply accumulated knowledge through improving computer hardware and software appears to expand at an increasing rate;

• Progress will ultimately depend upon improved training of professionals and researchers, which is relatively slow;

• Therefore, investment in education and research is the key to exploiting the technological advances that computer engineering and science is providing to us.