development of a travel demand model for transborder
TRANSCRIPT
The Dissertation Committee for Salvador Arturo González-Ayala
certifies that this is the approved version of the following dissertation:
Development of a Travel Demand Model
for Transborder Commuter Activity
Committee:
________________________________ Randy B. Machemehl, Supervisor
________________________________ B. Frank McCullough
________________________________ Susan L. Handy
________________________________ Rob Harrison
________________________________ Leigh Boske
________________________________ Zhanmin Zhang
Development of a Travel Demand Model
for Transborder Commuter Activity
by
Salvador Arturo González-Ayala, B.S.C.E., M.S.E.
Dissertation
Presented to the Faculty of the Graduate School of
the University of Texas at Austin
in Partial Fulfillment
of the Requirements
for the Degree of
Doctor of Philosophy
The University of Texas at Austin
December 2005
Dedicado a
Anna Paola, Alejandra Renée y Rodrigo Andrés.
Acknowledgments
My special thanks and appreciation to Dr. Randy Machemehl for his dedicated guidance
and support over my last years as a UT student, as well as to Mr. Rob Harrison for his insightful
assistance and interest in the final preparation of this document.
iv
Development of a Travel Demand Model
for Transborder Commuter Activity
Publication No._________
Salvador Arturo González-Ayala, Ph.D.
The University of Texas at Austin, 2005
Supervisor: Randy B. Machemehl
The southern US border is a region of great economic activity. Key port-of-entry
locations on this region usually link twin cities and thus have become facilities were substantial
traffic cross on a daily basis. Seeking to improve the forecast of such flows, the present research
effort focused on development of a new procedure for disaggregate travel modeling of persons in
a bi-national conurbation. This procedure steps away from the conventional approach of studying
separately each side of the international boundary, and thus from modeling ports-of-entry through
the simplistic use of external zones. The new approach extends the model boundaries beyond
international limits, covering the urban areas on both sides, and thus joining the two systems
through the ports-of-entry, which eliminates the need for external zones at these locations.
Developing and validating an international crossing model with mode choice capability is
nevertheless more complex than simply joining together two existing travel demand models
(TDMs). These issues have been considered herein and an initial set of modeling methodologies
have been researched and tested with encouraging findings. The 9/11 events complicated the
border processing element of the commuter trip, however, as the study will show, the model
produced reasonable estimations for 2005, even when originally calibrated with pre-9/11 data.
This study thus represents an unprecedented effort for any border urban area in the
United States or Mexico.
v
Table of Contents
Chapter I. Introduction Background……………………………………………………………………………….….. 1
General objectives of research……………………………………………………..….…. 2
Literature review……………………………………………………………........................ 3
Outline of report……………………………………………………………..………...…...... 6
Chapter II. Model structure Background: the regional model……………………………………………………......... 7
Arrangement of external trips…………………………………………………........ 8
General operation of the regional model………………………………………...... 9
The bi-national OD matrix……………………………………………….………..… 9
Structure of the TTDM…………………….………………………………………...…...…. 10
TTDM output……………………………………………………...………………..... 13
Chapter summary………………….………………………………………...…..............…. 13
Chapter III. Data organization Sampled field data: travel surveys…………………………………………...…………... 14
Ports-of-entry surveyed……………………………………………………………... 16
Database management……………………………………………………………... 17
Transborder travel characterization: overall volumes and modes…..….……… 17
Transborder travel characterization: trip purpose…..….………………………… 20
Sampled field data: POE queue measurements………………………...…………..…. 22
Census data: zone level demographics……………………………………………....…. 24
US sources………………………………………………………...………….……... 24
Mexican sources…...……………………………………………...………….……... 25
Database adjustment…………………………………………...………...….……... 28
Chapter summary………………………………………………………………………....…. 28
vi
Chapter IV. Transborder transportation networks The roadway system………………………………...………………………………….…... 29
Fields and attributes…………………………………………………………….…... 30
Considerations at the POEs………………………………………………………... 33
The transit system……………………...…………………………………………….…..…. 33
Fields and attributes…………………………………………………………….…... 33
Elements for modeling………………………………………………………..………....…. 35
The zone structure…………………………………………………...………….…... 35
Development of base skim matrices………………………………………..……... 36
Database update with combined skims………………………………………….... 39
Chapter summary………………………………………………………………………....…. 41
Chapter V. Model development Person trip generation……………………...…………………………………………..…... 42
Trip rate estimation……………………………………………………………...…... 42
Treatment of special generators………………………………………….………... 45
Model application……………………………………………………………..……... 47
Person trip distribution…………………...……………………...…………………….…... 48
Brief review of the gravity model………………………...…………………….…... 49
Model estimation…………………………………………………………...………... 51
Model application………………………………………………………..…………... 53
Crossing mode choice…………...…………………………………………………….…... 55
Brief review of the MNL and the nested-logit models……………………….…... 55
Model specification and estimation………………………………………………... 57
Model application……………………………………………………………..……... 59
Travel assignment and POE flow validation…………..……………………….…....…. 61
AUTO crossing validation……………………...……………………………….…... 62
PEDESTRIAN crossing validation…………………………….…...………….…... 63
Chapter summary………………………………………………………………………....…. 65
Chapter VI. Scenario evaluation Proposed transborder transit improvements..……………………………………..…... 67
Alternative transborder transit routes……………………………….………...…... 67
General assumptions…………………………………….……………………..…... 70
vii
GIS update..……………………………………………………………………..………..…... 71
TTDM application..……………………………………………………..………………..…... 73
Trip estimation……………………………………………..………….………...…... 73
Crossing mode choice………………………..………….……………………..…... 73
Assignment for validation………………………………………..…………..……... 75
Assignment for alternative scenarios………………………………..……..……... 77
Chapter summary………………………………………………………………………....…. 78
Chapter VII. Final analysis Preliminary conclusions..……………………………………………………….……..…... 79
Recommendation on further work..……………...…………………………………..…... 82
Appendix A………………………………..……………..…………………...……………………... 84 POE survey questionnaire
Appendix B…………………………..……………..………………………...……………………... 87 VB code to attach skims to survey trip records
Appendix C…………………………..……………..………………………...……………………... 95 Friction Factor tables
Acronyms………………………………………………………………………………………..…... 97
References………………………………………………………………………..……………..…... 98
Vita………………………………….………………………………………………..……………..…...100
viii
Chapter I. Introduction
Background
When defining the boundaries of a regional travel demand model (TDM), transportation
networks and the related traffic analysis zone structure are set to fully cover the corresponding
urban area. Under this framework the more detailed disaggregate behavioral models are
commonly used to characterize and forecast internal travel within the defined boundaries, while
external travel coming in or going out of the defined boundaries (external-external and external-
internal travel) is modeled through more simplistic and less precise growth-factor techniques that
make use of special external zones. Since external travel usually represents a small percentage
of the total trip activity taking place in an urban area (generally less than 5%), the error introduced
to the TDM by the simplistic approach is usually considered negligible.
For urban areas located adjacent to a neighboring country though, under current practice
the TDM boundaries are usually set to stop and coincide with the international border, regardless
of the existence of an urban area immediately across the international limits. Such conditions of
bi-national conurbation frequently translates into intense activity and commuter travel between
the two sides of the border, often yielding significantly more traffic at international ports-of-entry
(POEs) compared to other urban area access paths; this is illustrated in Figure 1 for the El Paso
and Laredo urban areas (Ref 1), which form bi-national conurbations with the cities of Juarez and
Nuevo Laredo respectively in Mexico.
58%
57%
42%
43%
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
200,000
El Paso Laredo
inland access roads
international ports-of-entry
AA
DT
for y
ear 2
000
Figure 1. Comparison of AADT between ports-of-entry and inland access roads
1
Moreover, crossing volumes at POEs as well as mode choice of transborder commuters
are highly correlated to inspection delay, as suggested in Figure 2 (Ref 2), a condition usually not
considered in travel forecasting of external stations. The drop in motor-vehicle trips and increase
in pedestrian crossings in year 2001 and 2002 coincides with an increase in US inspection times
for motor-vehicles in the same period.
0
10,000,000
20,000,000
30,000,000
40,000,000
50,000,000
60,000,000
1997 1998 1999 2000 2001 2002
year
pers
on-c
ross
ings
,
0%
10%
20%30%
40%
50%
60%
70%80%
90%
100%
1997 1998 1999 2000 2001 2002
mod
e sh
are
PEDESTRIAN
AUTO
year
Figure 2. Northbound travel patterns at El Paso POEs for different years
Finally, due to customs inspection operations and resulting vehicle idling at POEs, these
access links generate considerably more emissions per trip than those connecting to
conventional non-border external zones. This set of special conditions intuitively calls for more
careful travel forecasting.
The main focus of this research effort is therefore the development of a behavioral-based
approach that substitutes the use of external zones for modeling transborder commuter travel;
such an approach requires extending the TDM boundaries beyond the international border. The
El Paso (USA) – Juarez (MEX) bi-national conurbation will be used as a reference and case
study for this research project.
General objectives of research
The objectives of the present research project are:
2
• Estimate and validate a travel demand model that characterizes and forecasts
transborder commuter activity in a bi-national setting: the El Paso-Juarez region will
be used as reference. Such model should be sensitive to inspection policy and
multimodal infrastructure changes at the POEs. In order for other US-Mexico border
regions to emulate this effort, the model should be able to use data conventionally
available and maintained along the Mexican side.
• Use the resulting TDM to evaluate the impact of alternative transborder transportation
scenarios (case study for Do-nothing versus improved transit connectivity evaluation).
Under the current research effort, the impact will be quantified in terms of total daily
crossings by mode. Compare forecasts of the proposed approach to conventional
state-of-practice.
Literature review
Literature review revealed that transborder traffic, particularly that between the US and
Mexico, has been studied quite extensively over recent years. Most attention though has been
placed at the national and/or state levels, looking at long segments of the international border,
and mainly concentrating on commercial vehicle flows: For example, Fang, Harrison and
Mahmassani (Ref 3) developed predictive models for freight movement between US regions and
Mexico, while Strong, Harrison, and Mahmassani (Ref 4) presented a methodology to forecast
the effects of NAFTA on the demand of freight transportation at the Texas-Mexico border; on a
related report, Harrison et. al. (Ref 5) recommend improvements to currently available datasets to
enhance performance of these freight models. Weissmann and Harrison have also analyzed
transborder freight traffic growth under NAFTA, that use Texas highway and rail infrastructure,
but with origins and destinations outside of Texas, and have evaluated planning-level needs
along the Texas-Mexico border (Refs 6, 7 and 8). Towards policy issues, Boske and Harrison
(Ref 9), have studied strategies to overcome difficulties of cross-border freight transportation.
The fewer studies that actually had focused at the local level over bi-national
conurbations, mostly concentrate on commercial vehicles as well, such as the research by Said,
Harrison, and Hudson (Refs 10, 11, and 12) that examined the effect of transborder truck traffic in
the City of Laredo, Texas, as well as that by Sassin et. al. (Ref 13) that looked into the efficiency
of moving freight at the Laredo ports-of-entry. Moreover on port-of-entry evaluation, Stockton et.
3
al. (Refs 14, 15, and 16) had studied design issues to improve northbound truck processing
efficiency.
There are a couple of studies, such as those by McCullough et. al (Ref 17), and
Weissmann et. al. (Refs 18 and 19) that have included passenger transborder travel in their
scope. These efforts though, use an aggregate approach and are non-behavior based, thus
lacking the transportation supply-demand detailed interrelation that disaggregate TDMs are
designed to provide.
Specifically in the area of disaggregate TDMs for transborder passenger travel there is
very limited research that explore bi-national conurbations, and thus, the use of external stations
at international borders is common practice to model transborder commuter travel. Jurisdictional
constraints in the selection process and implementation of transportation infrastructure as well as
different data formats are usually practical factors limiting boundaries of travel demand models
beyond international borders. Such factors are not an issue in conurbations within the US, due to
the structuring of Council of Governments (COGs) and Metropolitan Planning Organizations
(MPOs); such institutional framework facilitates inter-agency coordination and data sharing
between Municipal, County and even State jurisdictions within the US.
The most progressive transborder travel demand model in operation so far appears to be
that of the San Diego Association of Governments (SANDAG). A related effort currently under
development is that of the Whatcom Council of Governments (WCOG) to model the transborder
corridor between Seattle, Washington and Vancouver, British Columbia (Ref 21). In its initial
phase the model will focus on freight transborder travel using a destination-choice approach.
The SANDAG TTDM
As part of their overall travel demand modeling process, SANDAG has included a
component for border vehicle-crossing forecast between the San Diego Region and Northern
Baja California, Mexico, that includes the city of Tijuana, and in less detail the cities of Tecate,
Rosarito, and Ensenada (Ref 20). The SANDAG TTDM required the development of a simplified
roadway network and zonal structure for the urban areas on the Mexican side, thus extending the
boundaries of the model beyond the international limits.
As represented in Figure 3, the SANDAG TTDM was originally developed from travel
surveys conducted at the POEs, through a 3-step process.
4
Figure 3. Flow chart of SANDAG TTDM development
In a first step that merges trip generation and distribution concepts, transborder origin-
destination (OD) matrices for vehicle-trips were developed by expanding survey responses to
counted daily volumes, and then directly allocating the resulting trips to the study area’s zones
according to the proportions observed in the travel survey. The OD matrices were obtained for
generic home-based-work (HBW), home-based-other (HBO), and non-home-based (NHB)
transborder trip purposes, for separate northbound and southbound direction of travel.
Secondly, the transborder OD matrices were converted from daily to hourly periods,
using time-of-day distributions of vehicle-traffic volumes obtained at POEs. Parallel to this
process and in preparation of the traffic assignment step, the existing computer roadway
networks on both sides of the border, pre-loaded with corresponding internal traffic, were joined
through links at the POEs.
On a final step, the hourly transborder OD matrices were assigned to the bi-national
roadway network under user-equilibrium criteria, including POE delay. The SANDAG TTDM
validation is based on POE traffic, and selected link traffic on the U.S. side of the network.
The POE surveys collected data for different modes of transborder travel, but no mode
split component was developed.
For future year traffic volume forecasts, trip growth factors are applied to the full
transborder OD matrices, therefore allowing no differential trip growth between zones. Such trip
growth factors were obtained as aggregate values for the whole bi-national region, averaging
population and employment forecasted growth.
5
The developers of the SANDAG TTDM acknowledge limitations of their model, mainly
due to the aggregate nature of socioeconomic data used on the Mexican side, as well as the
simplistic trip distribution approach. Added to these limitations, is the rigid formulation of the trip
generation step and lack of a mode split component.
Outline of report
The present document has been structured in seven chapters, of which the current
Chapter I provides an introduction to the research.
Chapter II (model structure) describes the overall model strategy, depicting the TTDM as
one of three components in the overall regional travel model; includes general guidelines and
data flow, as well as an overview of development approaches for the different model steps.
Chapter III (data organization) describes the field data used to develop the TTDM, its
organization in databases, and resulting travel pattern characterization. This chapter also
describes the demographic data available on both sides of the US-Mexico border, and its
conditions to replicate the TTDM approach at other bi-national conurbations.
Chapter IV (transborder transportation network) describes the general guidelines followed
for the configuration of the transborder transportation network in the model, including spatial
organization, travel modes represented, and attributes coded. The corresponding transborder
zone structure is described as well.
Chapter V (model development) describes the methodology followed in the formulation
and mathematical calibration of each of the model components, as well as the model validation
procedure and results.
Chapter VI (scenario evaluation) presents an example of TTDM use to forecast
transborder travel demand and impacts under specific scenarios of improved transborder transit
conditions. Impacts are established and compared between the improved transit scenarios and
prevailing (Do-nothing) conditions.
Chapter VII (final analysis) presents a summary set of conclusions, as well as future
research recommendations.
6
Chapter II. Model structure
Background: the regional model
The TTDM has been conceived as one of three components of the overall regional travel
demand model, as depicted in Figure 4; in this conception, the TTDM is designed to forecast
transborder travel only, while the other two components, the EPTDM (El Paso, USA) and the
JZTDM (Juarez, MX) have been designed to forecast travel within each corresponding side of the
international border.
EPTDM component 1
component 3
JZTDM component 2
Figure 4. Overall organization of the regional travel demand model for the border conurbation
7
The El Paso side (component 1) was the first piece to be developed, originally as a conventional
stand-alone TDM with external zones, including the ports-of-entry; assignment is performed on a
24-hour basis. Initially the base year was 1994 (Ref 22) since travel surveys and counts were
available only for that year; more recently in 1997 new counts were made available, and thus a
1997 model was re-calibrated and validated, using TransCAD as the model platform.
The Juarez side (component 2) was calibrated and validated with 1996 data, also as a
conventional stand-alone TDM; however, in preparation for the planned merging of the models,
the JZTDM was organized under the same flow structure and data format in TransCAD as the
EPTDM, and a 1996 version of the EPTDM was prepared, making both fully compatible. The
base year designated for the TTDM was therefore 1996.
On each side of the border, the TransCAD transportation networks have been developed
to characterize urban travel for three generic modes: highway, transit, and pedestrian. Originally,
the networks and corresponding zonal structure were coded completely independently for each
side, although by design both use the same definitions, categories, and units for network
attributes. With the introduction of the TTDM (component 3), the networks on each side were
joined into one bi-national network.
Each side has been developed to characterize internal travel through 4 main trip
purposes: home-based-work (HBW), home-based-non-work (HBNW), non-home-based (NHB),
and truck-taxi (TRTX) trips. The original stand-alone versions also considered two external trip
purposes that included all international border-crossing travel; under the new bi-national region
frame, the original characterization of external trips has been re-arranged to accommodate the
TTDM component and its resulting transborder (TRBR) trips as a separate group.
Arrangement of external trips
As shown in Figure 4, all external trips under component 1 have been divided into US-only and
TRBR categories, while under component 2 the external trips have been divided into MX-only and
TRBR categories.
US-only are those external trips that stay within the US side; similarly, MX-only are those external
trips that stay within the Mexican side. Both US-only and MX-only categories include external-
local (exlo) and through (thru) trip types.
TRBR trips are obtained from the TTDM (component 3), and are fed back into model components
1 and 2. TRBR trips have been classified as one of the following three types:
1) TRBR local: These are transborder trips with both origins and destinations within the
region (Juarez origin and El Paso destination, or El Paso origin and Juarez destination);
8
the main effort of the TTDM is concentrated with this type, since it represents most of the
transborder trips (roughly 90%). Modeling of component 3, requires further trip purpose
disaggregating of TRBR local trips, as will be shown in the following chapters.
2) TRBR exlo: These are transborder trips with one end outside of the bi-national model
region. Control totals are obtained exogenous to the model.
3) TRBR thru: These are transborder trips with both origins and destinations outside of the
bi-national model region. Control totals are also obtained exogenous to the model.
General operation of the regional model
Overall, components 1 and 2 have been designed as conventional behavior-based
disaggregate travel models, with a sequence of steps that account for trip generation, distribution,
mode split and trip assignment (transit and traffic). Additionally a feedback loop to roadway
network speed and capacity attributes provides the opportunity to adjust the models to closely
reproduce observed vehicle and transit passenger volumes.
The regional model process starts simultaneously at components 1 and 2, requiring an
initial set of roadway speed and capacity link attributes for each side of the bi-national network.
One model sequence is then completed independently for both components, all the way to the
roadway traffic assignment stage, without external trips. Pre-loaded roadway networks then enter
as input to the TTDM, providing revised roadway link travel costs (i.e. travel times, capacities)
that will impact transborder shortest paths and port-of-entry assignment.
The TTDM processes the information and returns multi-modal transborder (TRBR) trips
to components 1 and 2, to be joined with other external and internal trips, for a second set of
multi-modal trip assignments on each side.
This all-inclusive assignment is then validated at the regional level (VMT and transit passengers
boardings), and at the corridor screenline level, using observed data.
The bi-national OD matrix
All the trips being considered under the regional TDM have been organized in one bi-
national OD matrix, as conceptualized in Figure 5. Such matrix keeps track of trips from all three
components in the regional model and can be used for assignment on the entire bi-national
network, or on individual sub-areas by model component.
9
El P
aso
inte
rnal
zon
es
Juar
ezin
tern
al z
ones
US
exte
rnal
zon
es
MX
exte
rnal
zon
es
El Paso EP TRBR US-only TRBRinternal zones local local exlo exlo
Juarez TRBR JZ TRBR MX-onlyinternal zones local local exlo exlo
US US-only TRBR US-only TRBRexternal zones exlo exlo thru thru
TRBR MX-only TRBR MX-onlyexternal zones exlo exlo thru thru
component 1 component 2 component 3EP TDM JZ TDM TTDM
MX
Figure 5. Schematic OD matrix for the bi-national model region
The conceptual scheme of the matrix in Figure 5 is helpful in identifying the different trips
considered as well as their model component source.
Structure of the TTDM
Under its current version presented here, the TTDM has been designed to estimate daily
person-trips, as automobile, transit, and pedestrian flows between El Paso and Juarez, and
through their transportation networks, including international ports-of-entry.
10
Although components 1 and 2 of the regional model include freight, currently the TTDM
does not. Added to the complexities of developing a behavior-based model for freight in an
international context, since freight represents only 4% of all TRBR vehicle-trips (Ref 23), and less
than 0.3% of the overall vehicle-trips taking place in the entire El Paso-Juarez conurbation, a
decision was made to concentrate the initial efforts of the TTDM development on person travel
exclusively. The effect of this limitation on modeled port-of-entry choice and traffic allocation is
further minimized since the ports-of-entry in the El Paso-Juarez area have separate facilities for
freight, including approach roadways, thus practically eliminating any impact in processing
capacity or congestion for other modes. The pattern of increasing freight activity nevertheless
suggests the inclusion of a freight complement in subsequent research phases.
As such, the present version of the TTDM has been organized in a sequential 4-step
structure, although the specific approaches on each of these steps have special transborder
treatments. Figure 6 presents the overall organization and data flow of the TTDM.
Figure 6. Transborder travel demand model flow chart
Step 1
This initial section of the TTDM deals with the procedure for person trip generation. It
has been developed as a cross-classification table of transborder trip rates, for up to ten trip
11
purposes, subsets of those managed by components 1 and 2. It includes development of trip
rates for special generators on both sides of the border. Total daily trips generated by each zone
in the bi-national area are characterized as regionally balanced productions and attractions.
Step 2
Information on transborder person trip generation is fed into a second section, where the
trips are distributed among the zones in the bi-national region. This has been achieved through
the calibration of gravity type models by trip purpose, using roadway travel times as impedance.
Step 3
In the third section, a model has been developed to establish the port-of-entry crossing
mode, as well as the access modes to and from the ports-of-entry of all transborder trips; the
latter will impact the loading of the bi-national transportation networks. There are only two port-
of-entry crossing modes for persons: automobile and pedestrian; furthermore, the base access
modes for pedestrian crossings are automobile, transit, and walk. The model has been calibrated
using a nested-logit formulation, and validated against reported crossing mode shares.
Step 4
The final section of the TTDM deals with trip assignment. This is done under four
different categories, according to both crossing mode and mode of access to port-of-entry:
1) AUTO crossing w/AUTO access
2) PEDESTRIAN crossing w/WALK access
3) PEDESTRIAN crossing w/TRANSIT access
4) PEDESTRIAN crossing w/AUTO access
Such organization allows the loading of the appropriate transportation networks, and
model validation according to crossing mode at specific port-of-entry locations. The first category
deals with automobile mode for both port-of-entry crossing and access, while the rest of the
categories deal with pedestrian crossing mode under different access modes.
Under the first category (AUTO crossing w/AUTO access), the corresponding person-
trips fed from the mode split step, together with external person-trips (TRBR exlo and TRBR thru),
are converted to vehicle-trips (p to v) and assigned to the pre-loaded roadway network obtained
from model components 1 and 2; assignment is performed through a user-equilibrium procedure.
The selected path for each trip will include a specific port-of-entry, and thus, at the end of the
12
procedure, each port-of-entry will have vehicle volumes allocated. Crossing delay at each port-
of-entry is adjusted and the assignment procedure repeated until automobile crossing volumes in
the model replicate observed values.
Under the second category, pedestrian crossings with walk access mode are assigned to
the bi-national pedestrian network through an all-or-nothing procedure.
Under the third category, pedestrian crossings with transit access mode are assigned to
the bi-national transit network through a pathfinder procedure.
Under the fourth and last category (PED crossing w/AUTO access), the corresponding
person-trips fed from the mode split step, together with external person-trips (TRBR exlo and
TRBR thru), are converted to vehicle-trips (p to v) and assigned to the pre-loaded roadway
network obtained from model components 1 and 2; assignment is performed through a user-
equilibrium procedure. Once vehicle volumes are assigned to the roadway network, vehicle-trips
allocated to the ports-of-entry are converted back to person-trips before proceeding to the
pedestrian crossing validation.
Pedestrian crossing volumes by port-of-entry are added from the second, third and fourth
categories, and entered into a feedback loop based on pedestrian crossing delay by port-of-entry,
until model volumes match reported volumes.
TTDM output
When all four categories have been validated, the overall assignment procedure of the
TTDM enables optimal characterization of transborder travel over the different transportation
networks. The end result of step 4 is then summarized in the appropriate cells of the bi-national
model region’s OD matrix, by mode of access. In addition, at this stage the ports-of-entry have
appropriate impedance attributes to allow correct crossing volume allocation. In this format, the
information can then be fed back into components 1 and 2.
Chapter summary
The modeling approaches briefly described herein for each of the steps in the TTDM, will
be revisited and presented with greater detail in Chapter 5. The next chapter will deal with the
development and organization of databases used to calibrate and validate the TTDM.
13
Chapter III. Data organization
The TTDM development required the gathering of information from conventional sources,
although in some instances special treatment of the data was needed due to the transborder
nature of the model. Three types of data were specifically sought:
• travel surveys
• POE queue measurements
• Zone level demographics
The first two types were obtained through field sampling. Travel surveys on the one
hand, represented the main information source required to understand prevailing travel behavior
and thus, to identify logic patterns and significant variables for demand modeling. POE queue
measurements were a TTDM-specific requirement designed to characterize POE delay, a
network attribute not currently available as part of the original EPTDM or JZTDM, but essential in
the development of transborder shortest paths and corresponding travel costs.
The third data type was obtained from different census sources. To expand the surveyed
travel patterns to the entire urban area, demographic information was required disaggregated at
the zone level.
Special data organization formats were devised in order to optimize resources and
prepare the information for the modeling tasks ahead.
Sampled field data: travel surveys
Two travel survey efforts were available as data sources for this effort. In 1994 a
comprehensive travel survey on the El Paso side, and in 1996 a similar effort was completed for
Juarez. Both of these had information that contributed to a description of conditions at the border
crossings. Although the 94 El Paso survey described well El Paso travel characteristics, it was
for several reasons inadequate to describe border crossing activity, therefore special efforts were
design into the 1996 Juarez survey to particularly describe person trips associated with border
crossings.
Locally conducted travel surveys are the preferred information source for characterizing
travel patterns in an urban area and developing travel models. In particular, the 1994 El Paso
14
travel survey household travel dairies, as well as external station (intercept) surveys at the ports-
of-entry. However, detailed examination of the information revealed that the household travel
dairies actually captured a very small sample of transborder trips (less than 5%); in addition, the
travel surveys at the ports-of-entry were only conducted for the northbound direction, and in
general, origins on the Juarez side were recorded under very coarse location definitions.
From this experience, the 1996 Juarez travel survey was specifically designed to
overcome such limitations. In particular, port-of-entry intercept surveys were done for both travel
directions and included detailed questions on access modes, trip purpose, and location of trip-
ends (address and/or nearest street-intersection) on both sides of the border. The Juarez travel
survey was conducted during October of 1996, and also included household surveys, and
external station (intercept) surveys at the ports-of-entry in the El Paso-Juarez region. Household
surveys, as in the El Paso case, did not yield a large enough sample size of transborder trips, so
eventually only the port-of-entry intercept surveys were used to develop the TTDM.
Zaragoza
Paso del Norte
Stanton
BOTA
river
international boundary
international port-of-entry
JUAREZ
EL PASO
Figure 7. International ports-of-entry between El Paso and Juarez
15
Simultaneous to the survey efforts, weekday count information was obtained from
customs authorities on both sides of the border, for passenger and pedestrian crossings
northbound and southbound, and by port-of-entry.
Ports-of-entry surveyed
As shown in Figure 7, by 1996 there were 4 international ports-of-entry operating
between El Paso and Juarez:
crossing type
1. Paso del Norte bridge Northbound passenger vehicles, northbound and Southbound pedestrians, no trucks.
2. Stanton bridge Southbound passenger vehicles and pedestrians, no trucks.
3. Bridge of the Americas Both directions passenger vehicles, pedestrians, and trucks.
4. Zaragoza bridge Both directions passenger vehicles, pedestrians, and trucks.
Since El Paso and Juarez are divided by the Rio Grande river, all these international
ports-of-entry are bridge-type crossings.
Surveys at the international ports-of-entry were conducted for passenger-vehicle (i.e.
auto), pedestrian, and commercial truck modes. Since only person-travel was intended to be
estimated by the TTDM, only the passenger-vehicle and pedestrian surveys were actually used
for its development. Samples of the survey instrument used for person-travel in the 1996 study
are shown on appendix A.
After electronic coding, edit checking, and data optimization, a total of 3,390 surveys (e.g.
trip records) from all 4 ports-of-entry were stored in a root database for the TTDM. Table 1
presents a comparison of person-crossings and sample size for the port-of-entry surveys.
Table 1. Summary of average weekday crossings and corresponding survey sampling (Oct 96)
person- expansioncrossings/day factor
auto 26,096 80 326.20ped 9,501 293 32.43auto n/a n/a n/aped n/a* n/a* n/aauto n/a n/a n/aped n/a n/a n/aauto 13,804 330 41.83ped 9,502 288 32.99auto 39,081 471 82.97ped 1,936 300 6.45auto 57,628 342 168.50ped 2,658 209 12.72auto 14,331 174 82.36ped 921 276 3.34auto 14,440 358 40.34ped 199 269 0.74
TOTAL 190,097 3,390
sampledirection modeport-of-entry
BOTANB
SB
ZaragozaNB
SB
NB
SBPaso del Norte
StantonNB
SB
16
Database management
As the database foundation of the TTDM, the information from the intercept travel
surveys at the ports-of-entry was summarized and electronically coded directly into a simple root
table labeled TripsExp.
In its initial version, the TripsExp table was organized with the following fields:
CLAVE: Record (survey) unique number. ESTACION: Port-of-entry code (Paso del Norte=1, Stanton=2, BOTA=3, Zaragoza=4). SENTIDO: Direction of flow code (northbound=1, southbound=2). HORA: Time of day (hr:min). X_MODE: Code for person border-crossing mode (pedestrian = 1, passenger-vehicle = 2). MODE_ORI: Code for access mode from origin to POE (walk=1, bus=2, taxi=3, auto=4). MODE_DES: Code for access mode from POE to destination (walk=1, bus=2, taxi=3, auto=4). TTAZ_ORI: Origin TAZ number, using the transborder zonal structure. TTAZ_DES: Destination TAZ number, using the transborder zonal structure. PURP: Code for 10 generic transborder trip purposes (HBW=1, HBU=22, HBSc=2,
HBIm=33, HBSh=333, HBO=3, NHSc=4, NHIm=55, NHSh=555, NHO=5). RESID: Place of residency code (Juarez=1, El Paso=2, other MX=3, other US=4). IMM: Dummy variable if trip-end is identified as POE immigration office (yes=1). ExpFACT: Expansion factor specific to POE, direction and crossing mode (refer to Table 1).
For specific modeling processes, some additional fields were later incorporated into
TripsExp table.
Transborder travel characterization: overall volume and modes
Based on the survey information summarized in the TripsExp table, the overall majority of
transborder trips in 1996 had origins and destinations within the El Paso-Juarez region. This is
shown in Table 2, where daily person-crossings are summarized as transborder local (TRBR
local), transborder external-local (TRBR exlo), and transborder through (TRBR thru) trip
categories.
Table 2. 1996 weekday transborder travel by category
TRBR TRBR TRBR TRBRlocal exlo thru all
person-crossings/day 166,501 13,162 10,434 190,09788% 7% 5% 100%
The weekday totals shown include both person crossing modes (auto and pedestrian),
both directions of travel, and travel by local area residents as well as non-residents. Figure 8
shows further disaggregation of the weekday transborder travel, by crossing mode, indicating
auto (motorized passenger-vehicles such as automobiles, taxis, vans, pick-ups, motorcycles, etc.)
as the prevailing one.
17
18
pedestrian13%
auto87%
auto100%
pedestrian0%
pedestrian20%
auto80%
a) TRBR local b) TRBR exlo c) TRBR thru
Figure 8. Crossing mode share of transborder person-trips
When analyzing TRBR local trips in particular, the survey shows that 2/3 are done by
Juarez residents, and the rest by El Paso residents as depicted in Figure 9.
El Paso residents
33%
Juarez residents
67%
Figure 9. Proportion of TRBR local trips by traveler place of residence
pedestrian8%
auto92%
pedestrian18%
auto82%
Furthermore, the choice of crossing mode varies somewhat depending on the traveler’s
place of residence as shown in Figure 10. Of the total TRBR local trips by Juarez residents,
nearly 1/5 cross the border as pedestrians, while for El Paso residents this proportion drops to
less than 1/10.
a) Juarez resident b) El Paso residents
Figure 10. Crossing mode of TRBR local trips by traveler’s place of residence
19
Focusing on pedestrian crossings exclusively, four main access modes from-and-to the
ports-of-entry were identified in the 1996 survey: Walk-only (W), Bus (B), Taxi (T), and Auto (A).
From these, up to 16 different access (in-out port-of-entry) combinations are possible regardless
of the direction of travel, which have been summarized as follows:
WW: walk to POE/walk from POE
WB: walk to POE/bus from POE or bus to POE/walk from POE
WT: walk to POE/taxi from POE or taxi to POE/walk from POE
WA: walk to POE/auto from POE or auto to POE/walk from POE
BB: bus to POE/bus from POE
BT: bus to POE/taxi from POE or taxi to POE/bus from POE
BA: bus to POE/auto from POE or auto to POE/bus from POE TT: taxi to POE/taxi from POE TA: taxi to POE/auto from POE or auto to POE/taxi from POE AA: auto to POE/auto from POE
In this regard, the 1996 travel survey shows somewhat different patterns for El Paso and
Juarez residents as depicted in Figures 11 and 12.
0%
10%
20%
30%
40%
50%
60%
WW WB WT WA BB BT BA TT TA AA
EP residentsSB direction
0%
10%
20%
30%
40%
50%
60%
WW WB WT WA BB BT BA TT TA AA
EP residentsNB direction
a) Northbound b) Southbound
Figure 11. Access mode share of pedestrian crossing by El Paso residents
0%
10%
20%
30%
40%
50%
60%
WW WB WT
0%
10%
20%
30%
40%
50%
60%
WW WB WTTT TA AA
JZ residentsNB direction
A TT TA AA
JZ residentsSB direction
WA BB BT BA WA BB BT B a) Northbound b) Southbound
Figure 12. Access mode share of pedestrian crossings by Juarez residents
Bus use in port-of-entry access (BB, BA, WB) is prevalent for Juarez residents that cross
as pedestrians, accounting for close to 70% of pedestrian crossings, compared to 40% for El
Paso residents. El Pasoan pedestrian crossers tend to depend more on the auto and walking
(AA, WA, WW) for access to the ports-of-entry.
Taxi mode, as a means of traveling to the port followed by pedestrian crossing is
practically non-existent for Juarez residents, and very seldom used by El Paso residents; taxi
users rather complete the entire transborder trip in this mode, and thus wait in line and cross
through inspection booths for autos.
In general, access mode patterns appear to show somewhat consistent patterns for both
travel directions once disaggregated by resident type; nevertheless more attention should be
given to the northbound mode selection in addition to the traveler’s place of residence, since
northbound travelers incur the highest delay, and in theory mode could be an important criterion
for selecting round-trip options that reduce travel times for frequent commuters.
Transborder travel characterization: trip purpose
Transborder trip purpose was also characterized by the 1996 travel survey. The
following ten categories were defined, as sub-sets of the generic ones used under model
components 1 and 2:
HBW: home-based-work
HBU: home-based-university
HBSc: home-based-school (non-university)
HBIm: home-based-immigration business (POE immigration offices)
HBSh: home-based-shop
HBO: home-based-other
NHSc: non-home-based-school (includes university)
NHIm: non-home-based-immigration business (POE immigration offices)
NHSh: non-home-based-shop
NHO: non-home-based-other
As depicted in Figure 13, according to the 1996 travel survey, people in the El Paso-
Juarez area cross to the other side of the border to shop and work. The more general purposes
HBO and NHO also account for an important proportion of transborder trips, and these categories
include trips such as visits to family and friends, and site seeing .
20
21
HBW14% HBU
2%
HBSc4%
HBIm1%
HBSh23%
HBO34%
NHSc3%
NHIm1%
NHO12%
NHSh6%
Figure 13. Overall distribution of tranbsorder trip purposes
Similar to transborder mode choice, trip purpose has a notoriously different distribution
depending on the traveler’s place of residence; in addition, significant differences can also be
observed by travel direction. These conditions are depicted in Figures 14 and 15.
HBW10%
HBU0% HBSc
0%HBIm
0%
HBSh20%
HBO59%
NHSc1%
NHIm0%
NHO7%
NHSh3% HBW
7%
HBU0%
HBSc3%
HBIm0%
HBSh16%
HBO33%
NHSc6%
NHIm0%
NHO21%
NHSh14%
a) Southbound (primary) b) Northbound
Figure 14. Distribution of tranbsorder trip purposes by El Paso residents
NHSh1% NHO
2%
NHIm0%
NHSc1%
HBO30%
HBSh32%
HBIm1%
HBSc8%
HBU4%
HBW21%
NHSh10%
NHO21%
NHIm1%
NHSc4%
HBO24%
HBSh20%
HBIm3%
HBSc4%
HBU2%
HBW11%
a) Northbound (primary) b)Southbound
Figure 15. Distribution of tranbsorder trip purposes by Juarez residents
For El Paso residents, the southbound direction is the initial leg of transborder round trips
so southbound tends to characterize the primary trip purposes (Figure 14a), defined as the main
influence driving the need to cross the border. In this case, HBO (visit friends and/or family) and
HBSh (home based shopping) account for the majority (80%) of all trips. For Juarez residents,
the initial leg is northbound so primary trip purposes are those going in the northbound direction
(Figure 15a), and in this case HBO, HBSh, and HBW seem to be the main ones, although HBU
and HBSc (education), account for an important share of trips.
For both El Paso and Juarez’ residents, the return crossing trips seem to have a large
proportion of non-home-based purposes.
Sampled field data: POE queue measurements
Northbound port-of-entry delay, particularly for passenger vehicles, is an important
network attribute and input to the TTDM, therefore the need to characterize it. In this regard,
POE vehicle delay was defined as the average time required to pass through a particular POE. It
would be desirably measured for all vehicles as the total elapsed time from arrival at the end of
the queue to the time of exit from the POE. Direct gathering of this information presented several
challenges, thus a simplified procedure was selected to indirectly estimate this delay during
specific times of the day; this was achieved through the use of the following expression:
di = Nvi*Tp (Eq. 1)
where
di: POE vehicle delay [min], at time i of the
day.
Nvi: Number of vehicles in queue waiting to
cross the POE, at instant i of the day.
Tpi: Inspection processing rate [min/veh], at
time i of the day.
In order to estimate Nvi, the queue length at that instant needed to be establish, therefore
queue lengths were sampled once every hour, during a 12-hour period, and on two weekdays
within the survey period. In addition, the physical layout of the roadway approaches for each of
22
23
the ports-of-entry was studied, focusing on variations in the number of lanes to determine vehicle
capacity under different queue lengths.
Regarding Tpi, processing time at individual primary inspection booths northbound was
sampled randomly on all ports-of-entry, as well as the number of inspection booths opened for
each hour of operation. Thus
Tpi = Nbi * tb (Eq. 2)
where
Nbi: Number of inspection booths opened at
the POE, at time i of the day.
tb: Average processing rate of individual
inspection booths [min/veh].
The result of the described data gathering and its use in Equations 1 and 2 are
summarized in the delay patterns by port-of-entry shown in Figure 16.
0
5
10
15
20
25
30
35
40
07:3
0 a.
m.
08:3
0 a.
m.
09:3
0 a.
m.
10:3
0 a.
m.
11:3
0 a.
m.
12:3
0 p.
m.
01:3
0 p.
m.
02:3
0 p.
m.
03:3
0 p.
m.
04:3
0 p.
m.
05:3
0 p.
m.
06:3
0 p.
m.
PDNBOTAZARA
NB
veh
del
ay (m
in)
BOTA avg = 31.3min
PDN avg = 24.4 min
ZARA avg = 18.8 min
tb = 0.67 min/veh
Figure 16. Northbound crossing delay by time of day for different ports-of-entry
Data gathering was exclusively centered on the northbound direction, since there was
virtually no delay observed for the southbound direction.
Census data: zone level demographics
In order to relate transborder travel patterns to the land use in the region, spatially
disaggregated population and employment information was obtained from sources in both the US
and Mexico. Since the level of detail and the type of fields can vary somewhat when comparing
US and Mexican sources, an effort was made to identify and use data of similar types for both
sides; in addition to simplifying the model, the intention was to facilitate potential TTDM
replication at other border regions.
As previously stated, the survey data used to develop the TTDM model was collected at
POEs, and was developed through application of a brief intercept questionnaire. No socio-
economic characteristics of transborder travelers were obtained, and only information about the
trip was gathered. Trip information included origin-destination locations, and purpose of the
transborder trip; nevertheless, by combining these two elements, it was possible to identify
residence zone location for any HB trip, and thus infer gross socioeconomic information for many
of the surveyed travelers. This characteristic of the survey data related trip making to
demographic information aggregated at the zone level, and thus, the survey information was
related to traffic analysis zones (TAZs), the structure used for travel modeling, as will be
explained in Chapter 4.
US sources
On the US side, population information is available from the US Census Bureau.
Detailed population data is made available by block groups, which represent the basic unit of
area by which information is disaggregated spatially. In addition to population size, population
income reported by household, was selected as an explanatory variable for transborder travel.
Household income was obtained as a block group average from the US Census data, and then
re-estimated for each TAZ. At the time the TTDM model was under development, the latest
population information available was that from the 1990 census, so some extrapolation was
required to estimate 1996 base year conditions.
Employment data on the other hand, was obtained through the Texas Workforce
Commission. This data was summarized for different economic activities, and provided spatially
by employer address. Further refinement of this information by the El Paso Metropolitan Planning
Organization, has produced a GIS where it has been organized into TAZs. At the time the TTDM
model was under development, the employment information available was from 1996.
24
Mexican sources
On the Mexican side, both population and employment information is available from the
Instituto Nacional de Estadística, Geografía e Informática (INEGI), a federal agency in charge of
demographic and economic census planning and implementation. Similar to U.S. Census
practice, INEGI organizes the data in small area units, equivalent to block groups, called Area
Geo-Estadística Básica (AGEB).
Population size and income, as well as total employment is also available by AGEB from
INEGI. Unlike U.S. Census practice though, income is only reported per capita, and thus seeking
consist formats on both sides of the border, special unpublished data from INEGI was requested
to estimate income by household. As will be explained further, per capita income could still be
used on the Mexican side as an explanatory variable for transborder travel models based on
aggregate zonal values. Regarding employment, similar to U.S. Census practice, this data on the
Mexican side is also offered by INEGI as AGEB totals under different economic activities.
All demographic information on the Mexican side of the study area was converted from
AGEBs to TAZ. At the time the TTDM model was under development, the latest demographic
information available from INEGI was that for the 1990 population census, and 1995 economic
census, requiring extrapolation procedures to estimate 1996 base year conditions.
Database adjustment
In order to tie demographic information to the survey data, a second version of the
transborder travel database was constructed as shown in Figure 17, by relating the root table
TripExp to table Demog, that summarizes demographic characteristics by TAZ.
Table Demog was organized with the following fields:
TTAZ_ID: TAZ identification number, under the transborder zonal structure
EMP: Total TAZ employment (96 base year).
POP: Total TAZ population (96 base year).
HHincCAT: Category of average household income for the TAZ (96 base year).
ATYPE: TAZ area type (96 base year).
25
Figure 17. Database design relating POE survey data and zonal demographics
Due to the significant difference in both income levels and development densities
between the two sides of the border, the HHincCAT and ATYPE fields had to be defined under
separate categories for the US and Mexican sides:
In terms of household income, the categories used are the following:
El Paso Juarez
HHincCAT range [US dollars/yr] HHincCAT range [US dollars/yr]
1 $0 - $10,000 1 $0 - $2,171
2 $10,000 - $20,000 3 $2,171 - $6,508
3 $20,000 - $30,000 3 $6,508 - $10,847
4 $30,000 - $40,000 4 $10,847 - $13,018
5 $40,000 - $50,000 5 $13,018 - $19,527
6 >$50,000 6 >$19,527
In 1996, $1 US dollar was equivalent to $7.60 MX pesos.
26
The ranges shown were initially established for optimal trip generation variance when
developing the EPTDM and JZTDM respectively, and have been adopted and tested for the
TTDM component.
Area type is a measure of the activity density, where activity density for a given TAZ i is
defined by:
ActDensi = [POPi + (EMPi * B)] / AREAi (Eq. 3)
where
POPi: Total population at TAZ i.
EMPi: Total employment at TAZ i.
AREAi: Total area (acres) of TAZ i.
B = Total city POP / Total city EMP
The area type categories for each side of the border have been defined as follows:
El Paso Juarez
ATYPE ActDens range ATYPE ActDens range
6 0 - 26 (RURAL)
5 0 - 1 (RURAL) 5 26 - 62
4 1 - 10 4 62 - 100
3 10 - 15 3 100 - 135
2 15 - 50 2 135 - 200
1 > 50 (CBD) 1 > 200 (CBD)
It is estimated that the bi-national region had in 1996 a combined population of 1.8
million, 40% living in El Paso and 60% in Ciudad Juarez. This population is concentrated in an
area of just over 570 square-miles in El Paso, and 95 square-miles in Ciudad Juarez. Overall,
the 1996 average per-capita income in El Paso was $16,500 dollars, while in Juarez was $3,500
dollars. These statistics provide a perspective of the different income and density conditions on
each side of the border.
27
Chapter summary
The current chapter has described the sampled field data necessary to characterize
transborder travel patterns, as well as the demographic information available on both sides of the
US-Mexico border needed to develop the TTDM. This information has been organized into a
preliminary transborder travel database.
A final version of the database was developed by adding multimode transportation
network skims to the root table, including the northbound POE delay estimated from the queue
measurements. This final version of the data base will be discussed in Chapter 4.
28
Chapter IV. Transborder transportation networks
The modeling tasks require, a mathematical representation of the transportation
infrastructure in the transborder study area. This representation of the real networks, is
conventionally constructed as a simplified version of the street layout and other relevant
transportation paths. It is depicted graphically as an interconnected web of links and nodes with
attached attributes, describing specific physical and operational characteristics. Thus, the
information is conveniently stored and organized in a geographic information system (GIS)
environment.
El Paso and Juarez have developed roadway and transit GIS-based networks, as part of
their respective TDMs; in both cases TransCAD has been used as the software platform. Under
the current study an effort was undertaken to join these networks at the ports-of-entry, and to
make network attributes compatible between the cities as well. A description of the joined
networks follows.
The roadway system
The roadway system for the transborder network has been conceived to represent
motorized and non-motorized travel. All current vehicle types were included under motorized
travel, while non-motorized travel for the current version of the TTDM includes only pedestrians.
In this regard, pedestrian mobility has been characterized over the roadway network links,
depending upon the presence or absence of sidewalks and their connectivity. In general, a set of
attributes provided by link, offer different degrees of mobility for each of the modes considered.
Figure 18 shows the joined roadway system for the El Paso-Juarez metropolitan area,
highlighting the location of the POE links created to enable transborder flow modeling. It is
important to point out that as a simplified version of the real system infrastructure, only main
roadways were included in the original JZTDM and EPTDM networks as true on-system links,
while local roads have been aggregated by TAZ through the specification of centroid connector
links (not shown in Figure 18 for clarity). The resulting TTDM network thus carries over this
convention, and represents conditions for base year 1996.
29
GIS link Existing roadway
Figure 18. View of the modeled roadway network between El Paso and Juarez (1996)
Fields and attributes
The roadway system for the transborder network has been summarized under the fields
shown in Table 3. Most of the fields are mode-specific, according to three generic mode
definitions established under the original TDMs:
30
1) Low-occupancy motorized transportation (AUTO). Includes automobiles, motorcycles, vans, and trucks.
2) Non-motorized transportation (NON-MOTORIZED). Only considers walking.
3) High-occupancy public motorized transportation (TRANSIT). Includes public transit, and special bus services (schools and industry).
This differentiation of modes is reflected in the fields defined for the roadway GIS.
Table 3. Fields included in the GIS for the transborder roadway network
Field Description
LINK_ID Link identification number
LENGTH Length in miles
FUNCL Functional class of roadway
ATYPE Area type where roadway is located
DIR Direction of flow: 0=two-way, 1=one-way
SPD_HWY Average 24-hr speed for autos, in mph
TIME_HWY Link's travel time for autos, in minutes
LANES Total number of traffic lanes for autos
CAP_AB Weighted daily capacity [vpd], AB direction
CAP_BA Weighted daily capacity [vpd], BA direction
DIR_WLK Direction of walking flow: 0=two-way, 1=one-way
SPD_WLK Walking speed, in mph
TIME_WLK Link's walking time, in minutes
DIR_BUS Direction of transit flow: 0=two-way, 1=one-way
SPD_BUS Transit speed, in mph
TIME_BUS Link's transit time, in minutes
mod
e
spec
ific
for
mod
em
ode
auto
non-
mot
oriz
e dtra
nsit
c fo
spec
ific
for
spe
ific
r
In the case of the AUTO mode, the link attributes have been summarized according to
the area type where the links are located, and each link’s functional classification. For the El
Paso side, this matrix of attributes is shown in Table 4, while Table 5 shows the matrix of
attributes for the Juarez side.
The suggested values of the attributes come from original TDM development on each
side of the border (Refs 24 and 25).
31
Table 4. Speed and capacity link attributes used for the El Paso side of the network Speed [mph]Capacity [vpl] CBD Fringe Urban E Urban N Urban W Suburb N Suburb W Suburb E Rural Suburb NM Rural NM
1 2 3 3 3 4 4 4 5 4 515 25 26 29 26 35 35 33 40 35 40
40,000 40,000 40,000 40,000 40,000 40,000 40,000 40,000 40,000 40,000 40,000Border 25 26 35 33 33 34 34 34 45 34 45
Highway 13,100 11,750 11,350 11,350 11,350 10,250 10,250 10,250 6,300 10,250 6,300Freeway 29 31 31 35 32 38 38 38 49 38 40Radial 17,250 22,750 19,550 19,550 19,550 11,750 11,750 11,750 7,600 11,750 7,600
25 25 25 25 25 26 26 26 40 36 4013,100 11,750 11,350 11,350 11,350 10,250 10,250 10,250 6,300 10,250 6,300
Ppal arterial 17 26 29 32 32 33 33 33 45 33 43divided 8,350 7,500 7,100 7,100 7,100 6,250 6,250 6,250 4,400 6,250 4,400
Ppal arterial 17 25 27 31 31 32 32 32 44 36 40undivided 7,550 6,800 6,400 6,400 6,400 5,600 5,600 5,600 3,900 5,600 3,900Divided 14 24 26 27 28 28 30 29 41 36 41arterial 7,250 6,500 5,500 5,500 5,500 4,050 4,050 4,050 2,550 4,050 2,550
Undivided 14 24 26 27 26 27 28 31 41 36 41arterial 6,600 5,950 5,050 5,050 5,050 3,750 3,750 3,750 2,400 3,750 2,400
Collector 11 18 24 24 24 28 29 28 42 29 39divided 6,200 5,550 4,650 4,650 4,650 3,350 3,350 3,350 1,800 3,350 1,800
Collector 11 18 22 25 25 27 29 27 42 29 38undivided 5,700 5,100 4,300 4,300 4,300 3,150 3,150 3,150 1,700 3,150 1,700Frontage 20 25 30 31 30 37 37 37 42 37 42
road 8,350 7,500 7,100 7,100 7,100 6,250 6,250 6,250 4,400 6,250 4,40017 24 25 26 25 34 34 34 40 34 40
18,000 18,000 18,000 18,000 18,000 18,000 18,000 18,000 18,000 18,000 18,000Trans 18 20 20 20 20 23 23 23 24 23 24
Mountain Rd 12,000 12,000 12,000 12,000 12,000 12,000 12,000 12,000 12,000 12,000 12,000Freeway 32 33 33 33 33 34 34 34 37 34 38circumf 17,250 19,550 19,550 19,550 19,550 11,750 11,750 11,750 7,600 11,750 7,600
Ramp
Area Type
13
14
11
12
9
7
8
4
5
6
Func
tiona
l Cla
ssifi
catio
n
Connector 0
1
2
Expressway 3
Table 5. Speed and capacity link attributes used for the Juarez side of the network
Speed [mph]Capacity [vpl] CBD low CBD high Urban low Urban high Suburb Rural
1 2 3 4 5 615 15 15 15 25 35
30,000 30,000 30,000 30,000 30,000 30,00029 29 29 29 31 38
17,250 17,250 17,250 17,250 22,750 11,75025 25 25 25 29 36
13,100 13,100 13,100 13,100 11,750 10,250Ppal arterial 17 17 17 17 26 33
divided 8,350 8,350 8,350 8,350 7,500 6,250Ppal arterial 17 17 17 17 25 32undivided 7,550 7,550 7,550 7,550 6,800 5,600
Minor arterial 14 14 14 14 24 28divided 7,250 7,250 7,250 7,250 6,500 4,050
Minor arterial 14 14 14 14 24 27undivided 6,600 6,600 6,600 6,600 5,950 3,750
Minor arterial 11 11 11 11 18 28unpaved 6,200 6,200 6,200 6,200 5,550 3,350
17 17 17 17 24 3418,000 18,000 18,000 18,000 18,000 18,000
Func
tiona
l Cla
ssifi
catio
n
Connector 0
Freeway 2
Expressway 3
4
8
Area Type
Ramp 12
5
6
7
For the non-motorized (i.e., pedestrian) mode, speed has a constant value of 3 mph on
any link with continuous sidewalks. In the case of transit, speeds have been obtained for specific
links from the bus schedules in operation. For these two generic modes there are no capacity
constraints.
32
Considerations at the POEs
As previously stated, the original TDM networks have been joined through new links
created at the ports-of-entry, in substitution of previous external station nodes. The time and
corresponding speed values at these new links, have been selected initially to be consistent with
the observed northbound delays (as illustrated in Figure 16, Chapter III).
The transit system
The transit system in a TransCAD environment is developed as a sub-network of the
roadway system, requiring two related GIS coverages built on top of the roadway links: one
depicts the set of transit fixed routes, while the other depicts transit stops for the routes. This
condition allows the use of travel speed and travel time attributes (or any other associated cost)
defined in the underlying roadway system, for both the transit service as well as for pedestrian
access to and from stops.
Figure 19 shows the transit route system for the El Paso-Juarez metropolitan area, as
depicted in centerline format in TransCAD. All routes on each side of the border have been
included: 48 in El Paso and 140 in Juarez.
Fields and attributes
The fields used in the transit system are the following:
Table 6. Fields included under the transborder transit system a) Transit Route GIS
Field Description
ROUTE_ID Route identification number
ROUTE_NAME Route name
HEAD_OFF Headway at off-peak period, in minutes
HEAD_AM Headway at AM peak period, in minutes
HEAD_PM Headway at PM peak period, in minutes
FARE User fare, in dollars
b) Transit Stop GIS
Field Description
STOP_ID Stop identification number
LON Longitud of stop
LAT Latitude of stop
ROUTE_ID Route identification number
PASS_CNT Pass count at the stop location
NODE Roadway system node ID associated to stop
33
roadway link for POE
transit stops
JUAREZ
EL PASO
Figure 19. View of the El Paso-Juarez transit system (base year 1996)
For the 1996 base year transborder network, no formal bi-national transit service was in
operation, yet, as depicted in the zoom-in window in Figure 19, transit stops near the ports-of-
entry were close enough that users on one side of the border could transfer to the transit service
on the other side by walking the length of the POEs. Thus no adjustments to the original transit
system routes were required when building the transborder version.
34
Elements for modeling
Along with the development of the base link-node layout of the transportation networks,
and following state-of-practice, it is necessary to define a compatible traffic analysis zone (TAZ)
structure. From the combination of these GIS elements, optimum travel paths and associated
costs, also referred to as “skims”, can be obtained for all possible TAZ pairs. These skims are
then attached to the trips recorded from the OD field surveys, creating the main data basis for
TDM development.
For the El Paso-Juarez TTDM, the procedure followed was similar in principle, with the
slight difference that skims were only needed for transborder TAZ pairs, in particular TRBR-local,
TRBR-exlo, and TRBR-thru trips as defined in Chapter II, and depicted by the bi-national OD
matrix in Figure 5.
The zone structure
For modeling purposes, TAZs serve as a coarse geographic reference of the origins and
destinations in urban travel. The zoning structure allows the aggregation and simplification of
travel exchange between different geographic locations in the study area. Ideally, the boundaries
of a TAZ should attempt to follow surrounding links of the main roadway system, so travel with
destination to, or originating in the TAZ would use those surrounding links as immediate access
to the transportation system. This was the general premise followed when developing the original
TDMs.
Similar to the context and issues with transportation networks, the zone structures
originally developed for the EPTDM and the JZTDM, were for the most part adopted and carried
over to the TTDM zone structure. The only changes were the deletion of the external TAZs
originally defined at POEs.
Figure 20 shows a view of the TAZ structure established for the El Paso-Juarez
transborder region; the zoom-in window shows as additional detail, the relation maintained
between zone boundaries and the main roadway system. It also exemplifies the convention
followed for centroid and centroid-connector coding. In this regard the TTDM zone structure has
maintained the one centroid per TAZ definition, and therefore the premise of a single point
assignment per TAZ.
In summary, El Paso has 660 internal TAZs, while Juarez has 425, for a total of 1,085
TAZs in the transborder zone structure.
The primary fields and attributes used under the TAZ structure, are those available from
census sources on both sides of border, as explained in Chapter III.
35
centroid connector
TAZ boundary
link
Figure 20. View of the joint El Paso-Juarez TAZ structure (base year 1996)
Development of base skim matrices
Having established a transportation network structure, and a related TAZ structure,
transborder shortest path skims could then be developed for all modes involved. These skims
are intended to portray the most probable paths and associated costs between all transborder
TAZ pairs, and thus help establish average trip information about each OD record obtained with
the POE surveys.
36
As a first step, base skim matrices were developed by only considering a single or
dominant mode for the entire transborder trip. Therefore using the tools available in TransCAD,
base skim matrices were developed for the AUTO, TRANSIT, and NON-MOTORIZED generic
modes. Further rearranging of particular elements of these base skim matrices on specific survey
records (specific transborder OD pairs) would allow proper characterization of their combined
access modes and POE selection, while optimizing data storage requirements (developing skim
matrices for each access mode combination, and for each possible POE would exponentially
increase data storage unnecessarily).
For AUTO generic mode, POE selection was included as an attribute of the path, in addition to
travel time, since the survey showed that these two factors are not fully correlated (i.e., POE
selected is not necessarily the one under the path that yields the least overall transborder travel
time). Therefore, skim matrices have been developed to establish minimum paths separately
through the three POEs available:
SkmA_BOT For this skim matrix, paths were forced to go through the BOTA POE. This
was accomplished by disabling the links for the PDN/STANTON and
ZARAGOZA POEs.
SkmA_PDN For this skim matrix, paths were forced to go through the PDN/STANTON
POE. This was accomplished by disabling the links for the BOTA and
ZARAGOZA POEs.
SkmA_ZAR For this skim matrix, paths were forced to go through the ZARAGOZA POE.
This was accomplished by disabling the connection links for the BOTA and
PDN/STANTON POEs.
Each of these skim matrices for the AUTO generic mode include two fields:
1) TIME: auto travel time (field minimized for optimal path).
2) LENGTH: traveled length.
For TRANSIT generic mode, POE selection was not included as an attribute of the path, since for
this mode the survey showed that POE selection is highly dependent on overall transborder OD
travel time. Instead, the matrices where obtained for different headway configurations at different
periods of the day; even though transit operating speeds remain fairly similar throughout the day,
37
varying headways make a significant difference in overall travel time, and become an important
variable when characterizing trip information of transit users. Thus the developed matrices are:
SkmB_AM This skim matrix has been obtained assuming AM headways only, and
enabling all POE links.
SkmB_OFF This skim matrix has been obtained assuming off-peak headways only, and
enabling all POE links.
SkmB_PM This skim matrix has been obtained assuming PM headways only, and
enabling all POE links.
For each of these skim matrices, the optimal path is selected though the Pathfinder method
(Ref 26) in TransCAD, which makes use of the generalized cost of travel, and thus includes
fare in addition to overall travel time, grouping similar routes and service conditions. The
skim matrices for the TRANSIT generic mode include the following seven fields:
1) FARE: sum of fares charged for the trip
2) IVTT: sum of in-vehicle travel times.
3) WAIT1: initial wait time (1/2 headway).
4) TFER_WAIT: sum of transfer wait times (1/2 headways).
5) TFER_TT: sum of transfer travel times.
6) ACC_TT: access travel time (origin to first transit stop).
7) EGR_TT: egress travel time (last transit stop to destination).
For NON-MOTORIZED generic mode, POE selection was not included as an attribute of the path,
since according to the survey, POE selection in this case is more dependent on total distance
between origin and destination (and travel time). In addition the tolls are considerably lower than
for autos, and thus not a relevant factor for POE selection. Only pedestrian travel has been
included as part of this mode, and only one matrix needed to be developed.
Skm_WLK This matrix includes two fields:
1) TIME_WLK: walking travel time.
2) LENGTH: traveled length (minimized).
38
Database update with combined skims
To be able to portray all transborder transportation possibilities, attributes of the 7 base-
skim matrices needed to be combined as previously suggested; this actually was accomplished
while attaching the skims to the POE survey trip records, a process that allowed the optimization
of data storage needs.
In preparation of the combination step, the observed transborder modes described on
Chapter III, were consolidated according to Table 7.
Table 7. Consolidation of combined modes for transborder trips. OBSERVED CONSOLIDATED TRANSBORDER MODES TRANSBORDER MODEScode description code description
TTAA auto to POE/auto from POE (xing on same auto) AA auto xing/auto access
WB walk to POE/bus from POE or bus to POE/walk from POEBB bus to POE/bus from POE
TT taxi to POE/taxi from POETA taxi to POE/auto from POE or auto to POE/taxi from POEWT walk to POE/taxi from POE or taxi to POE/walk from POE PA ped xing/auto accessWA walk and auto combination accessAA auto to POE/auto from POE
WW walk to POE/walk from POE PW ped xing/walk access
BT bus to POE/taxi from POE or taxi to POE/bus from POE ped xing/bus and autoBA bus to POE/auto from POE or auto to POE/bus from POE combined access
ped xing/bus accessPB
PM
AU
TO x
ing
PED
ESTR
IAN
xin
g
This process resulted in the definition of the following fields:
MNLMODDE: consolidated transborder mode. AA_IVTT: total in-vehicle travel time (minutes) for AA mode. AA_DIST: total in-vehicle distance (miles) for AA mode. PB_FARE: total fare (US dlls) for PB mode. PB_IVTT: total in-vehicle travel time (minutes) for PB mode. PB_INWT: initial wait time (minutes) for PB mode. PB_TRWT: total transfer wait times (minutes) for PB mode. PB_TRTT: total transfer travel time (minutes) for PB mode. PB_ACTT: access travel time (minutes) for PB mode. PB_EGTT: egress travel time (minutes) for PB mode. PA_IVTT: total in-vehicle travel time (minutes) for PA mode. PA_DIST: total in-vehicle distance (miles) for PA mode. PA_OVTT: total out-of-vehicle travel time (minutes) for PA mode.
39
PW_TT: total walk travel time (minutes) for PW mode. PW_DIST: total walk distance (miles) for PW mode. PM_aIVTT: total auto in-vehicle travel time (minutes) for PM mode. PM_aDIST: total auto in-vehicle distance (miles) for PM mode. PM_bFARE: total bus fare (US dlls) for PM mode. PM_bIVTT: total bus in-vehicle travel time (minutes) for PM mode. PM_bINWT: bus initial wait time (minutes) for PM mode. PM_bTRWT: total bus transfer wait time (minutes) for PM modes. PM_bTRTT: total bus transfer travel time (minutes) for PM mode. PM_bACTT: bus access travel time (minutes) for PM mode. PM_bEGTT: bus egress travel time (minutes) for PM mode. A_QT: queue time when crossing by AUTO. A_QC: toll paid when crossing by AUTO. P_QC: toll paid when crossing as PEDESTRIAN.
The combination of the appropriate base-skim attributes, and their attachment to the root
database was done through a computer program, designed in VisualBasic code. Appendix B,
presents a printout of the code. Figure 21 shows the final database design.
Figure 21. Database design relating POE survey data with skims, and zonal demographics
40
Chapter summary
The current chapter has described the characteristics of the transborder GIS-based
transportation networks prepared for the TTDM. In addition, detail explanation has been provided
on skim development to properly characterize the multimodal combination of POE access and
crossing, while optimizing data storage requirements.
A final version of the transborder trip database was developed by adding these
transportation network skims to the root table. With this final database ready, the TTDM
calibration process could then be accomplished.
41
Chapter V. Model development
Overall, the El Paso-Juarez TTDM could be depicted as being in the 4-step-sequential
family, yet its bi-nation transborder condition involves intricacies that have required special
treatment within the component steps; the general logic flow has been previously described on
Chapter II. The present chapter describes the methodology followed in the formulation and
mathematical calibration of each of the TTDM components, as well as in the validation process.
Person trip generation
Trip generation is the initial step in the classical travel demand modeling process,
providing the total number of trip productions and trip attractions for each TAZ in the study area.
In this regard, trip production is conventionally defined as the home end (origin or destination) of
a home-based trip, or the origin of a non-home-based trip; trip attraction on the other hand is
conventionally defined as the non-home end (origin or destination) of a home-based trip, or the
destination of a non-home-based trip. These trip productions and attractions are usually
expressed as person or vehicles trips, and are further categorized by the purpose of the trip.
As an initial approach compatible with the original TDMs on each side of the border, the
trip generation component for the El Paso-Juarez TTDM has been set up to yield daily person-
trips, using a simple cross-classification model structure.
Trip rate estimation
The initial stage in the calibration of the trip generation component required the
development of trip generation rates, based on information from travel surveys; as previously
detailed in Chapter III, under the current project, the travel surveys used were those from the
1996 POE intercepts. Due to the type of survey design and resulting data, an optimal trip-based
approach was to develop the production rates on a per capita basis, under specific categories of
income; in the case of attractions, trip rates could be developed per employee under area type
categories. In both cases, trips were differentiated between northbound (Juarez to El Paso), and
southbound (El Paso to Juarez) flows, as well as between the 10 trip purposes characterized in
Chapter III.
The generation rates have been conceived as follows.
42
Equation 4 defines the estimated daily trip productions per capita, for trip purpose ρ,
crossing direction δ, and income category ι.
(Eq. 4)
Nρδι
Σn=1
popιpρδι =
EFpρδιn
In this expression, EFpρδιn represents the production expansion factor of record n, under
trip purpose ρ, crossing direction δ, and income category ι ; popι represents the total population
under income category ι. Nρδι is the total number of survey records under the specific group ρδι.
Equation 5 defines the estimated daily trip attractions per employee, for trip purpose ρ,
crossing direction δ, and area type category α.
(Eq. 5)
Nρδα
Σn=1
empαaρδα =
EFaρδαn
In this expression, EFaρδαn represents the attraction expansion factor of record n, under
trip purpose ρ, crossing direction δ, and area type category α ; empα represents the total
employment under area type α. Nρδα is the total number of survey records under the specific
group ρδα.
The use of expansion factors directly in the trip rate expressions results from the fact that
the sample number per POE is different and thus, the total daily trips cannot be obtained as a
simple count; each record in the resulting database has an expansion factor attached
corresponding to the POE where the data was collected. The specific values per port-of-entry are
shown on Table 1 (Chapter 3).
Having developed base trip generation rates, the rates where then compared between
adjacent income and area type categories, to establish optimal category aggregation.
The process of grouping category levels for income (in the case of productions), and area
type (in the case of attractions), was based on a statistical comparison of the estimated trip rates
in adjacent categories, in order to identify those that were not significantly different. Categories
with trip rates that were not significantly different, were grouped. These paired comparisons
needed to be established through an iterative process between adjacent categories, and for this
43
purpose a Z-test was used for comparing the estimated trip rates. The mathematical formulation
used was:
Z = (Eq. 6)
µx-µy
sx2 sy
2 Νx Νy
where: Z : test statistic µx, µy : mean trip rate in adjacent categories X and Y sx, sy : trip rate std deviation in adjacent categories X and Y Νx, Νy : Number of records in adjacent categories X and Y
The hypothesis being tested is that the difference between the two mean trip rates is zero
(Ho: µx-µy =0). This hypothesis is rejected at the significance level of 0.05, that is, when |Z|<1.96.
The procedure yielded the final trip rates, and optimal category groups shown on Tables
8 to 11; the rates are expressed as daily person trips, per 1,000 residents in the case of
productions, or 1,000 employees in the case of attractions.
Table 8. Transborder trip production rates in Juarez
IncomeAgrRange
Low Inc 1,2 A 10.61 5.75 1.74 0.68 4.04 1.37 0.61 1.31 15.03 10.27 14.18 12.05Med Inc 3,4 B 11.61 4.07 4.53 2.21 7.53 6.96 0.34 1.43 31.45 11.80 25.94 26.72High Inc 5,6 C 67.77 0.00 45.18 0.00 22.59 45.88 0.00 0.00 45.01 67.84 90.36 45.88
HBSh HBOtrps/1000hab trps/1000hab trps/1000hab trps/1000hab trps/1000hab trps/1000hab
HBW HBU HBSc HBIm
ATYPEAgrRange
High Act 1,2 A 1.32 0.18 3.67 7.29Med Ac 3,4 B 10.84 0.48 14.46 13.87Low Act 5,6 C 6.55 0.56 26.03 49.39
trps/1000emp
NHSc NHIm NHSh NHOtrps/1000emp trps/1000emp trps/1000emp
Table 9. Transborder trip attraction rates in El Paso
t Juarez to El PasoEl Paso to Juarez
ATYPEAgrRange
High Act 1,2 A 45.92 25.29 NA NA 18.44 7.28 NA NA 18.62 13.95 41.38 37.57Med Ac 3,4 B 43.00 17.23 NA NA 17.29 9.91 NA NA 17.97 13.57 42.52 47.54Low Act 5 C 29.14 29.63 NA NA 9.89 0.00 NA NA 9.73 4.69 11.04 29.27
HBSc HBIm HBSh HBOtrps/1000emp trps/1000emp trps/1000emp trps/1000emp trps/1000emp trps/1000emp
HBW HBU
t
ATYPEAgrRange
High Act 1,2 A 5.63 NA 4.66 16.38Med Act 3,4 B 3.07 NA 11.00 24.86Low Ac 5 C 0.00 NA 19.70 0.42
NHSc NHIm NHSh NHOtrps/1000emp trps/1000emp trps/1000emp trps/1000emp
t
Juarez to El PasoEl Paso to Juarez
44
Table 10. Transborder trip production rates in El Paso
IncomeAgrRange
Low Inc 1,2 A 4.35 3.27 NA NA 1.99 0.05 NA NA 10.62 12.14 19.57 33.58Med Inc 3,4 B 1.17 4.26 NA NA 0.46 0.00 NA NA 3.75 6.08 8.17 16.88High Inc 5,6 C 6.02 7.87 NA NA 0.00 0.00 NA NA 1.68 1.52 15.16 15.25
HBW HBU HBSc HBIm HBSh HBOtrps/1000hab trps/1000hab trps/1000hab trps/1000hab trps/1000hab trps/1000hab
ATYPEAgrRange
High Act
1,2 A 9.19 NA 14.93 43.40Med Ac 3,4 B 9.44 NA 3.12 20.12Low Ac 5 C 0.00 NA 5.07 29.98
NHONHShNHSc NHImtrps/1000emp trps/1000emp trps/1000emp trps/1000emp
Table 11. Transborder trip attraction rates in Juarez
Treatment of special generators
In addition to the mean trip rates developed by categories of income and area type, trip
rates were developed for special generators. Special generators are areas of the two cities
(represented at the TAZ level) that show unusual trip generation characteristics; it is better for the
overall predicting accuracy of the model if the trip rates of special generators are developed
separately, since this prevents an outlier effect that might drastically bias otherwise average
values. In the El Paso Juarez TDM these special generator TAZs were the ones with the highest
attractions rates, usually representing large shopping malls or districts, and residential areas
close to the border.
The special generators identified in the El Paso-Juarez study area, according to the
observation frequency on the travel surveys, were:
El Paso Juarez UTEP Thomason Hospital Downtown Juarez PDN POE area El Paso Airport Pronaf area BOTA POE area Waterfill area ZARAGOZA POE area Downtown El Paso Fox Plaza Mall Bassett Center Mall Cielo Vista Mall Sunland Park Mall Zaragoza comercial strip
tt
Juarez to El PasoEl Paso to Juarez
ATYPEAgrRange
High Act 1,2 A 4.64 2.25 NA NA 0.00 0.00 NA NA 4.56 4.17 5.29 4.55Med Act
t3,4 B 8.20 8.72 NA NA 4.37 0.11 NA NA 10.19 10.82 33.93 60.66
Low Ac 5,6 C 5.31 19.96 NA NA 2.61 0.00 NA NA 12.54 6.61 51.32 89.04
HBW HBU HBSc HBIm HBSh HBOtrps/1000emp trps/1000emp trps/1000emp trps/1000emp trps/1000emp trps/1000emp
ATYPEAgrRange
High Act 1,2 A 1.44 1.47 4.93 9.80Med Act
t3,4 B 6.45 2.28 12.41 32.48
Low Ac 5,6 C 21.60 4.53 34.57 84.00
NHOtrps/1000emp trps/1000emp trps/1000emp trps/1000emp
NHSc NHIm NHSh
Juarez to El PasoEl Paso to Juarez
45
Using a simplified version of the approach employed for the rest of the urban area
(Equations 4 and 5 without disaggregating by income group or area type), trip rates where
developed for special generators. The results are shown on Tables 12 to 15.
Table 12. Transborder trip production rates from special generators in Juarez
SpecialGenerator
Downtown JZPronaf area
Waterfill area
HBSh HBOtrps/1000hab trps/1000hab trps/1000hab trps/1000hab trps/1000hab trps/1000hab
HBW HBU HBSc HBIm
SpecialGenerator
Downtown JZ 37.36 69.98Pronaf area 170.71 179.20
Waterfill area 20.75
NHShtrps/1000emp
NHOtrps/1000emptrps/1000emp trps/1000emp
NHSc NHIm
Juarez to El PasoEl Paso to Juarez
Table 13. Transborder trip attraction rates from special generators in El Paso
SpecialGenerator
UTEP 539.40 203.79PDN area 732.51 880.64
BOTA area 263.85 1426.2 118.51 173.97ZARAG area 270.32 94.10Downtown EP 2442.8 1880.6 432.52 240.53
Fox Plaza 2076.1 1643.4Bassett Ctr 866.34 8.90 2227.4 3673.4Cielo Vista 799.05 375.38
Sunland Park 194.92 131.95Zaragoza strip 996.04 540.51 109.52 55.10
Thomason 386.53 132.07EP Airport 816.12 98.39
trps/1000emp trps/1000emp trps/1000emp trps/1000emp trps/1000emp trps/1000emp or hab
HBSh HBOHBW HBU HBSc HBIm
SpecialGenerator
UTEP 179.34PDN area 145.69
BOTA area 49.14 108.60ZARAG area 57.92Downtown EP 383.68 126.23
Fox Plaza 700.77Bassett Ctr 174.06 1113.7Cielo Vista 245.54
Sunland Park 64.97Zaragoza strip 160.98 7.34
Thomason 29.25EP Airport 1008.9
trps/1000emp trps/1000emp or hab
NHSh NHOtrps/1000emp trps/1000emp
NHSc NHIm
Juarez to El PasoEl Paso to Juarez
46
Table 14. Transborder trip production rates from special generators in El Paso
SpecialGenerator
UTEPPDN area
BOTA areaZARAG areaDowntown EP
Fox PlazaBassett CtrCielo Vista
Sunland ParkZaragoza strip
ThomasonEP Airport
trps/1000hab trps/1000hab trps/1000hab trps/1000hab trps/1000hab trps/1000hab
HBSh HBOHBW HBU HBSc HBIm
SpecialGenerator
UTEP 87.53PDN area 135.48
BOTA area 1108.9 108.48ZARAG area 36.36Downtown EP 512.37 390.72
Fox Plaza 1246.8Bassett Ctr 626.27 1406.6Cielo Vista 130.61
Sunland ParkZaragoza strip 293.41 21.07
Thomason 28.44EP Airport 3108.3
trps/1000emp trps/1000emp or hab
NHSh NHOtrps/1000emp trps/1000emp
NHSc NHIm
Juarez to El PasoEl Paso to Juarez
Table 15. Transborder trip attraction rates from special generators in Juarez
SpecialGenerator
Downtown JZ 496.55 504.62Pronaf area 2239.4 1096.9
Waterfill area 521.80 1105.2
trps/1000emp trps/1000emp trps/1000emp trps/1000emp trps/1000emp trps/1000emp
HBSh HBOHBW HBU HBSc HBIm
SpecialGenerator
Downtown JZ 277.71 790.16Pronaf area 577.01 634.17
Waterfill area 127.58 0.00
trps/1000emp trps/1000emp
NHSh NHOtrps/1000emp trps/1000emp
NHSc NHIm
Juarez to El PasoEl Paso to Juarez
Model application
The developed cross-classification of trip rates were applied to all the TAZs in the study
area. Table 16 shows an example of the final trip generation results, expressed as total daily
person-trips by TAZ.
47
48
Table 16. Example of final trip generation table (daily person-trips 1996) TTAZ HBW1p HBW1a HBU1p HBU1a . . .
HBWp HBWa HBUp HBUa HBScp HBSca HBImp HBIma HBShp HBSha HBOp HBOaEL PASO TAZS 4832 17562 0 3071 721 6689 0 2027 9962 28642 25311 30358JUAREZ TAZS 17562 4832 3071 0 6689 721 2027 0 28642 9962 30358 25311
NHScp NHSca NHImp NHIma NHShp NHSha NHOp NHOaEL PASO T
TAZS 2660 1928 810 123 6159 4431 13289 6671
JUAREZ AZS 1928 2660 123 810 4431 6159 6671 13289
HBW2p HBW2a HBU2p HBU2a . . . NHOp NHOa1 0 4 0 0 . . . 0 2 0 0 . . . 4 22 3 29 0 0 . . . 2 16 0 0 . . . 27 103 0 14 0 0 . . . 0 8 0 0 . . . 14 54 2 14 0 0 . . . 2 8 0 0 . . . 13 55 7 22 0 0 . . . 5 12 0 0 . . . 20 86 3 6 0 0 . . . 2 3 0 0 . . . 6 27 3 11 0 0 . . . 2 6 0 0 . . . 55 188 0 12 0 0 . . . 0 6 0 0 . . . 23 89 2 16 0 0 . . . 1 9 0 0 . . . 37 12
10 0 7 0 0 . . . 0 4 0 0 . . . 13 4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1080 13 1 2 0 . . . 7 2 1 0 . . . 5 81081 35 2 6 0 . . . 19 2 2 0 . . . 4 81082 30 1 5 0 . . . 16 5 2 0 . . . 13 221083 43 1 7 0 . . . 23 5 3 0 . . . 12 211084 4 0 1 0 . . . 2 1 0 0 . . . 2 31085 8 0 1 0 . . . 4 1 1 0 . . . 4 6
In summary, the trip generation model yielded the following total daily transborder trips
for home-based trip purposes:
The totals for non-home-based trip purposes were:
Overall the trip generation estimated for base year 1996, yields 63,745 daily trips
produced in El Paso and attracted to Juarez, while there were 101,503 daily trips produced in
Juarez and attracted to El Paso. In total, the model estimates 165,248 daily transborder (TRBR
local) person-trips; this compared to the 166,501 daily trips obtained directly from POE counts
and surveys (Table 2, Chapter III), represents an error of less than 1%.
Person trip distribution
Having determined each zone’s potential for trip-making, the trip distribution step in travel
modeling can then establish the quantity of trip exchange between zone pairs. That is, it
connects trip productions and attractions, creating a matrix from the independent production and
attraction arrays.
Revisiting the classical concepts and relation between trip generation and trip distribution,
Figure 22 schematically exemplifies how the productions and attractions for a given zone (e.g.,
zone 93) in the trip generation step, end up “distributed” to the other zones in the study area. As
seen here, this resulting trip exchange between zones is conventionally presented as a two-
dimensional matrix, where each of its cells represent the number of trips produced at zone i (row
i) and attracted to zone j (column j). This Production-Attraction matrix (also referred to as the “P-
A matrix”) is transformed to Origin-Destination (O-D), simply by reconfiguring the cell values to
produce a matrix symmetric around the main diagonal.
j=93ZONE Productions Attractions 1 2 3 . . . 93 . . . 425
1 3,643 4,670 1 215 306 421 . . . 128 . . . 132 1,237 1,854 2 259 198 235 . . . 560 . . . 223 5,049 2,983 3 362 268 210 . . . 376 . . . 69. . . . . . . . . . . . . . .. . . . . . . . . . . . . . .. . . . . . . . . . . . . . .93 9,351 5,360 Σ T93j= 9,351
Σ Ti93= 5,360
i=93 93 163 754 196 . . . 412 . . . 63. . . . . . . . . . . . . . .. . . . . . . . . . . . . . .. . . . . . . . . . . . . . .425 942 661 425 16 39 53 . . . 12 . . . 321
(a) Trip generation table (b) Trip distribution matrix
P A
Figure 22. Schematic relationship between trip generation and trip distribution
Do to its simplicity and as an initial approach to test, the trip distribution component of the
TTDM was conceived under the previously described basic concept, using the algorithms of a
doubly constrained gravity model. The limited type of data gathered under the current POE
survey design, restricted the use of other more sophisticated approaches for trip distribution.
Brief review of the gravity model
The doubly constrained version of the classical gravity model has the following form:
Tij = βi*Pi*αj*Aj*f(tij) (Eq. 7) Where:
Tij : Trips produced in zone i and attracted to zone j. Pi : Total trips produced in zone i. βi : Balancing factor for row i (production constraint). Aj : Total trips attracted to zone j. αj : Balancing factor for column j (attraction constraint) f(tij) : Impedance (decreasing) function, based on the travel
time between zone i and zone j.
49
The two constraints that the model is required to meet are that 1) the sum of trips in any
specific row of the matrix should equal the total number of trips produced in that zone, and 2) that
the sum of trips in any specific column should correspond to the number of trips attracted to that
zone. A simplified version of the two conditions are written as follows:
Tij = Pi (Eq. 8) Σ j
Tij = Aj (Eq. 9) Σ i
The expression of both balancing factors βi and αj can thus be derived through simple algebraic
manipulations of Equations 7 to 9. These have the following simplified forms:
βi = (Eq. 10)
1
Σ αj*Aj*f(tij) j
1 αj = (Eq. 11)
Σ βi*Pi*f(tij)
i
As shown here, the balancing factors are interdependent, meaning that the calculation of
one set requires the values of the other set, furthermore suggesting an iterative process until
convergence is achieved. Thus, the practical approach to solving this formulation is to specify
separate singly constrained models to both productions (Equation 12) and to attractions
(Equation 13). The first one is obtained by making αj = 1 since in this case the columns are not
being balanced. Similarly, the second one is obtained by making βi = 1 since in this other case
the rows are the ones not being balanced.
Tij = Pi* (Eq. 12) Aj*f(tij)
Σ Aj*f(tij)
j
Tij = Aj* (Eq. 13) Pi*f(tij)
Σ Pi*f(tij)
i
The solution for the doubly constrained model can then be converged upon by iteratively
applying Equation 12 to balance the productions (rows), and Equation 13 to balance attractions
(columns).
50
Model estimation
For the trip distribution component, the 10 original trip purposes were aggregated into 5
as follows:
Original purpose Aggregate purpose HBWork HBW HBUniversity HBU HBShop HBSh HBSchool HBImmigration HBO HBOther NHSchool NHShop NHImmigration NHB NHOther
This aggregation was needed in order to have a significant sample size for proper gravity
model parameter estimation. In the case of HBU trip purpose, the only attraction is the TAZ
where the University of Texas (UTEP) is located, so its distribution was already developed from
the trip generation step.
The following task was to develop transborder travel length frequency distributions
(TLFD) for the rest of the aggregate trip purposes. Figures 23 to 26 show TLFDs obtained
directly from the POE survey database, using skimmed auto travel times, and considering zero
waiting time at the ports-of-entry. Since waiting time has no apparent influence on the choice of
transborder destination, there is no need to include it as part of the time impedance and to
establish a spatial separation distribution.
0%
2%
4%
6%
8%
10%
12%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89
Minutes
Perc
enta
ge
Avg Time: 25.36min
HBWAll directions, residents from both sides
Figure 23. TLFD for HBW trip purpose, obtained from 1996 POE survey
51
52
0%
2%
4%
6%
8%
10%
12%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89
Minutes
Per
cent
age
Avg Time: 20.23min
HBShAll directions, residents from both sides
Figure 24. TLFD for HBSh trip purpose, obtained from 1996 POE survey
0%
2%
4%
6%
8%
10%
12%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89
Minutes
Perc
enta
ge
survey intrazonalsurvey interzonal
Avg Time: 22.67min
HBO (includes hbo, hbs, hbi)All directions, residents from both sides
Figure 25. TLFD for HBO trip purpose, obtained from 1996 POE survey
0%
2%
4%
6%
8%
10%
12%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89
Minutes
Perc
enta
ge
Avg Time: 21.42min
NHB (includes nhs, nhsh, nhi, nho)All directions
Figure 26. TLFD for NHB trip purpose, obtained from 1996 POE survey
53
Using the TLFDs shown, four corresponding gravity models were estimated, yielding as
impedance function f(tij), a set of friction factor tables for HBW, HBSH, and HBO trip purposes,
and a negative exponential function for the NHB trip purpose. The friction factor tables were
adjusted for proper average travel times and descending functional form (see appendix C).
Model application
0%
2%
4%
6%
8%
10%
12%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89
Gravity modelExt survey
HBW (ff table)
The developed gravity models were applied to each of the aggregate trip purposes, using
as input the corresponding TRBR production-attraction tables. The final result of this process
was the development of four production-attraction (PA) matrices, that in turn were converted into
origin-destination (OD) all-mode person-trip matrices. A comparison of the survey TLFDs and
those resulting from the application of the gravity model are shown in Figures 27 to 30.
0%
2%
4%
6%
8%
10%
12%
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89
Gravity modelExt survey
HBSh (ff table)
Figure 27. Comparison of HBW gravity-based and travel-survey TLFDs
Figure 28. Comparison of HBSh gravity-based and travel-survey TLFDs
54
0%
2%
4%
6%
8%
10%
12%1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89
Gravity modelExt survey
HBO (ff table)
Figure 29. Comparison of HBO gravity-based and travel-survey TLFDs
0%
2%
4%
6%
8%
10%
12%
1
5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89
Gravity modelExt survey
NHB (neg exp c=0.0393)
3
Figure 30. Comparison of NHB gravity-based and travel-survey TLFDs
The previous set of figures show that the modeled TLFDs follow observed patterns to a
fair extent; although some atypical spikes are evident in a couple of trip purposes, differences in
the average travel times were less than 10% in all cases. At this stage no other comparison tests
between model and observed data were considered effective for trip distribution. Validation
results would eventually pinpoint the need to revisit this component.
As previously explained, the fifth OD matrix corresponding to the HBU transborder trip
purpose was obtained directly at the trip generation step, since only one attraction location was
involved. Thus at this point in the model sequence, all matrices were considered ready to be
utilized in the mode choice component.
Crossing mode choice
The objective of this TTDM component is initially to forecast the percent share of
pedestrian and auto crossings at the ports-of-entry, for the different transborder OD pairs. This is
necessarily tied to the type of modes available as access to the ports-of-entry, and their
corresponding networks on each side of the border; therefore for modeling purposes, the
combination of integral crossing modes (including access) have been consolidated into the five
generic ones, as previously defined on Table 7 (Chapter IV):
AA: crossing by AUTO w/ AUTO access (includes walk access on one side)
PB: crossing as PEDESTRIAN w/ BUS access (includes walk access on one side)
PA: crossing as PEDESTRIAN w/ AUTO access (includes walk access on one side)
PW: crossing as PEDESTRIAN w/ WALK access only
PM: crossing as PEDESTRIAN w/ BUS and AUTO combined access
In principle, these modes have been organized in the generalized structure shown in
Figure 31; to model the mode choice process, different MNL and nested-logit configurations have
been explored, which required further refinements to this preliminary tree.
transborder
mode choice
θ
AA PB PA PW PM
Figure 31. General tree structure defined for transborder mode choice
Brief review of the MNL and nested-logit models
The MNL and nested-logit models are two of the mathematical forms used under the
framework of discrete choice analysis, which makes use of the random utility concept:
55
56
Uin = Vin + εin (Eq. 14)
Where: Uin : Utility of alternative i for decision maker n. Vin : Deterministic component of the utility. εin : Random component of the utility (disturbances).
The deterministic component of the utility can be expressed as:
ΣA
a=0 Vin = βina Xina (Eq. 15)
Where:
Xina : Attributes for alternative i. βina : Attribute coefficients. A : Number of attributes for alternative i.
If we assume εin to have a logistic distribution, thus, it has been proven (Ref 27) that for a
set Cn of j alternatives, the probability of choosing alternative i is given by:
(Eq. 16) Pn (i) =
ΣeVin
eVjn
j Cn∈
Equation16 is known as the multinomial version of the logit model or MNL. The nested-
logit version is an extension of the MNL, requiring that each nest η of alternative’s utilities be
included as a weighted term, conventionally known as the logsum:
(Eq. 17) θ logsumη = θ ln Σk C η∈
eVηk
Where:
Cη : Set of alternatives within nest η. k : Number of alternatives in nest. θ : Coefficient of logsum upper nest-level, as exemplified in Figure 31.
The β and θ coefficients in these models are conventionally estimated using maximum-
likelihood theory. BIOGEME (Ref 28) was the particular software used for this purpose.
Model specification and estimation
Having reviewed the mode-choice model types and a preliminary general structure, the
calibration process required the selection of attributes. In this regard, transportation network
attributes such as components of travel time and cost, and aggregate attributes of origin and
destination zones such as income and area type were considered. In this context, multiple
combinations of attributes were tested, under different tree structures; in order to optimize
resources under the current version of the mode choice component, all trip purposes were
aggregated into a single one. In the end, the following structure showed the better fit.
Px θ Ax
PW AA PB PA PM
UAA = β01 * (AA_IVTT + A_QT) + β02 * (AA_COST + A_QC) UPB = β01 * (PB_IVTT + PB_TRTT + PB_ACTT + PB_EGTT + PB_INWT + PB_TRWT) + β02 * (PB_FARE + P_QC) UPA = β01 * (PA_IVTT + PA_OVTT) + β02 * (PA_COST + P_QC) UPW= β94 + β01 * (PW_TT) + β02 * (P_QC) UPM = β01 * (PM_aIVTT + PM_bIVTT ) + β01 * (PM_bTRTT + PM_bACTT + PM_eEGTT + PM_bINWT + PM_bTRWT) + β02 * (PM_aCOST + PM_bFARE + P_QC) UAx = θ * ln[exp(UAA)] UPx = θ * ln [exp(UPB + UPA + UPW + UPM)] + β98* (ATOIN_P) + β99* (ATYPE_A)
The selected structure has an upper nest for AUTO and PEDESTRIAN crossing choices,
with the same θ coefficient. The actual integrated modes have been specified in the lower nest,
as access choices.
The ATOIN_P and ATYPE_A variables in the upper nest for PEDESTRIANS,
represented a special conditional selection of database fields. ATOIN_P is the area type range
(ATYPE) in case of NHB records, or the income range (HHincCAT) in case of HB records, of the
57
production TAZ. ATYPE_A is the area type range (ATYPE) of the attraction TAZ. The utility
functions are self explanatory, and their variable arrangement resulted from a trial-and-error
process until parameters showed optimum values.
Since the POE sample was stratified, corresponding weights were specified in the
estimation of nested versions, and therefore the coefficients were obtained through the Weighted
Exogenous Sample Maximum Likelihood (WESML) approach in BIOGEME.
For both US and MX residents, the best models were those were only northbound trip
records were used; this relates to the fact that the most important delay takes place in this
direction of travel, and thus this delay represents a significant factor when choosing a mode for
the complete round trip.
This reduction in the sample size, originally reduced the t-test values of most of the
coefficients and even produced counter-intuitive signs. This was solved when the global travel
time (in-vehicle and out-of-vehicle) was aggregated. Table 17 summarizes the final parameters
calibrated for the mode choice model.
Table 17. Resulting coefficients of the mode choice model
coeff descrption coeff value st dev t-testβ01 global travel time -0.01286 0.06164 -0.21β02 out-of-pocket cost -0.12562 0.04462 -2.82β94 ASC (PW) -1.25072 0.10103 -12.38β98 area-type/income of production TAZ -0.21922 0.04616 -4.75β99 atype of attraction TAZ -0.19422 0.02636 -7.37θ LogSUM 0.64096 0.11386 5.63
value time (dlls/hr) 6.14$ ovtt/ivtt N/A1st wait/2nd wait N/A
Looking at the resulting coefficients, these have intuitively correct signs, and with the
exception of global travel time the resulting t-statistics are strong. Since a known weakness of
the WESML approach is the efficiency of coefficients, the resulting value for the global travel time
was nevertheless adopted. Moreover, the computed value-of-time, which relates the travel time
coefficient to that of the out-of-pocket cost, was at the higher end of the usually accepted range.
The need to aggregate all travel time components, unfortunately does not allow their comparison.
Finally, as theoretically required, the logsum coefficient is positive and less than one.
58
Model application
Since the attributes of the user were incorporated into the mode choice model as
aggregate zonal averages, the application of this component was straightforward, using the
attributes on an OD pair basis. According to the selected nest structure, Equations 18 through 24
define the mode shares, as simple and conditional probability estimations per OD pair.
Upper nests:
P(Ax) = (Eq. 18) UAx e
UAx e + UPx e
P(Px) = (Eq. 19) UPx e
UAx e + UPx e
Lower nests:
P(AA) = P(Ax) * P(AA|Ax) = P(Ax) * (Eq. 20) UAAe Ue AA
P(PB) = P(Px) * P(PB|Px) = P(Px) * (Eq. 21) UPBe
UPB e + UPAe + UPWe + UPMe
P(PA) = P(Px) * P(PA|Px) = P(Px) * (Eq. 22) UPAe
UPB e + UPAe + UPWe + UPMe
P(PW) = P(Px) * P(PW|Px) = P(Px) * (Eq. 23) UPWe
UPB e + UPAe + UPWe + UPMe
P(PM) = P(Px) * P(PM|Px) = P(Px) * (Eq. 24) UPMe
UPB e + UPAe + UPWe + UPMe
The utility functions and therefore the probability definitions above, consider a single trip
producing TAZ at a time. Since any given OD pair most likely had trip productions on both ends,
the combined mode share of the pair had to incorporate zonal attributes from the two TAZs
59
60
involved. A simplified approach to accomplish this was to apply weights based on the HB and
NHB productions on each TAZ pair, as defined by Equation 25.
P(m)ij = (Eq. 25) [P(m)i HB ∗ pi HB] + [P(m)i NHB ∗ pi NHB] + [P(m)j HB ∗ pj HB] + [P(m)j NHB ∗ pj NHB]
pi HB + pi NHB + pj HB + pj NHB
Where:
P(m)ij : Weighted probability of choosing mode m between origin TAZ i and destination TAZ j
P(m)i HB , P(m)i NHB : Probabilities of choosing mode m at production TAZ i
P(m)j HB , P(m)j NHB : Probabilities of choosing mode m at production TAZ j
pi HB , pi NHB : Total productions at origin TAZ i
pj HB , pj NHB : Total productions at destination TAZ j
The final result of this component was the construction of a matrix with the share of trips
(given as probabilities) that will use each of the specified integrated crossing modes, between all
the transborder origin and destination TAZs.
As a preliminary model validation, the mode shares were then applied to the sum of OD
person-trip matrices developed at the trip distribution step. As a summary, Figure 32 shows the
resulting shares aggregated by the integrated crossing modes; the data in Chapter III was
reorganized, combining crossings by US and Mexican residents.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
AA PB PA PW PM
observedmodeled
Figure 32. Integrated crossing mode shares
Modeled and observed shares showed differences of less than 1%, therefore the
transborder mode choice component was considered acceptable.
Travel assignment and POE flow validation
Having completed the initial three components of the TTDM, the remaining step was the
assignment of the resultant transborder travel through the different transportation networks.
Figure 33 shows a refined version of the assignment and validation flow chart, that includes the
specific OD matrices resulting from the previous TTDM components.
Figure 33. Assignment and validation process for the TTDM
61
In general, the transborder OD matrices for each of the integrated crossing modes were
processed as follows:
TRBR pre-assignment carrier screenline post-assignment ODmtx conversion (ODmtx) network conversion (POE links only)
AA person to vehicle-trips AUTO non (stays as vehicle flow)
PW none (stays as person-trips) PED non (stays as person flow)
PB none (stays as person-trips) TRANSIT non (stays as person flow)
PA person to vehicle-trips AUTO convert back to person flow
PM person to vehicle-trips AUTO convert back to person flow
The factor used to convert between person and vehicle trips was based on the average
occupancy of 1.88 passengers/veh observed on transborder crossing vehicles, under the TRBR
local category.
AUTO crossing validation
The OD matrix for the AA mode converted to vehicle-trips, and that with vehicular
external flows (TRBR-exlo and TRBR-thru) were combined, and then assigned to the AUTO
network using a user-equilibrium (UE) approach, which balances traffic flow volumes until paths
for the same OD pair show the same travel time from volume adjusted speeds. As described in
Chapter II, the vehicular external flows were obtained exogenous to the model; Figure 34
schematically summarizes these external vehicular flows for base year 1996.
El P
aso
int z
ones
Juar
ezin
t zon
es
US
ext z
ones
MX
ext z
ones
El Paso EP TRBR US-only TRBRint zones local local exlo exlo
938
Juarez TRBR JZ TRBR MX-onlyint zones local local exlo exlo
1207
US US-only TRBR US-only TRBRext zones exlo exlo thru thru
1207 1565MX TRBR MX-only TRBR MX-only
ext zones exlo exlo thru thru938 1565
Figure 34. External transborder vehicle-trips/day, crossing as AUTO flows
62
To validate the model, the POE link volumes resulting from the assignment procedure
were compared to observed vehicle counts at each port-of-entry. As shown in Figure 33, a
feedback loop allows manual adjustment of the crossing delay at the POE links to re-assign and
re-distribute vehicular flows through the POEs until modeled and observed volumes are within a
designated error range (>10% for two-way flow). The resulting POE flows after the initial
transborder assignment iteration were the following:
Table 18. Comparison of observed and modeled daily AUTO crossings at POEs
POE dir errorby dir by loc by dir by loc
NB 11,931 10,083SBNBSB 6,138 9,294NB 21,550 24,763SB 28,200 24,971
ZARA NB 8,491 7,364SB 7,934 7,714
NB TOT 41,972 42,210 0.57%SB TOT 42,272 41,979 -0.69%
TOTAL 84,244 84,189 -0.07%
-8.20%
PDN
STANTON
BOTA
count modelAUTOS/day
16,425
19,377
49,734
15,078
18,069
49,750
7.24%
-0.03%
Since all screenline flows were within a 10% error range, no adjustments were made to
the POE AUTO delay.
PEDESTRIAN crossing validation
In the case of PEDESTRIAN crossings, the assignment was done in separate steps for
each of the remaining crossing modes.
1) The OD matrix for the PW mode was assigned directly to the PEDESTRIAN network
using an AON approach.
2) The OD matrix for the PB mode was assigned to the TRANSIT network using a
Pathfinder approach previously described in Chapter IV. Transit assignment with
TransCAD loads out-of-vehicle access, egress and transfers on the underlying
63
PEDESTRIAN network; since all transit based crossing has been coded to walk-
transfer between services on both sides of the border, the POE links of the network
capture such pedestrian volume.
3) For simplicity, the combined AUTO-TRANSIT access for pedestrian crossings (PM
mode) was considered as AUTO access only. Since the actual crossing validation
was directed at pedestrian flow, and the PM share was small enough (less than 2% of
all transborder trips), the resulting network loading did not warrant a more thorough
characterization effort. Therefore the person-trips of both matrices were converted to
vehicle-trips, added together, and assigned using a UE approach on the AUTO
network. Once this was done, the POE link volumes resulting from this assignment
were converted back from vehicle to person-trips.
4) Due to their low volume, the external flows (TRBR-exlo and TRBR-thru) crossing the
border as pedestrians were directly assigned to the PEDESTRIAN network with an
AON approach (no capacity constraints), which assigns all flows in an OD pair to the
single path with the optimum cost (minimum travel time). These flows usually access
the POEs as intercity bus services, and therefore the described simplification has an
insignificant impact on the transportation systems of the border cities, but loads the
POE links with appropriate pedestrian volumes. Figure 33 depicts this adjustment to
the original flow chart presented in Chapter II. As with the vehicular case, the
pedestrian external flows were obtained exogenous to the model; Figure 35
schematically summarizes these external flows for base year 1996.
El P
aso
int z
ones
Juar
ezin
t zon
es
US
ext z
ones
MX
ext z
ones
El Paso EP TRBR US-only TRBRint zones local local exlo exlo
1190
127
127 7
1190 7
Juarez TRBR JZ TRBR MX-onlyint zones local local exlo exlo
US US-only TRBR US-only TRBRext zones exlo exlo thru thru
MX TRBR MX-only TRBR MX-onlyext zones exlo exlo thru thru
Figure 35. External transborder person-trips/day, crossing as PEDESTRIAN flows
64
To validate the model, the resulting person flows at POE links were added and compared
to observed pedestrian counts. As shown in Figure 33, a feedback loop allows manual
adjustment of the crossing delay at the POE links to re-assign and re-distribute pedestrian flows
through the POEs until modeled and observed volumes are within a designated error range
(>10% for two-way flow). The resulting POE flows after the initial transborder assignment
iteration were the following:
Table 19. Comparison of observed and modeled daily PEDESTRIAN crossings at POEs
POE dir errorby dir by loc by dir by loc
NB 9,501 9,734SB 5,900 4,965NBSB 3,602 4,482NB 1,936 2,563SB 2,658 2,363
ZARA NB 921 565SB 199 456
NB TOT 12,358 12,862 4.08%SB TOT 12,359 12,266 -0.75%
TOTAL 24,717 25,128 1.66%
1,120 1,021 -8.84%
19,181 0.94%STANTON
BOTA 4,594 4,926 7.23%
PEDESTRIANS/daymodelcount
PDN19,003
Since all designated screenline flows were within a 10% error range, no adjustments
were made to the POE delay.
Chapter summary
The current chapter has described the development of the 4 main components of the
TTDM:
• Trip distribution
• Trip distribution
• Mode choice
• Multimode trip assignment and validation
65
Having calibrated and done a preliminary validation of the El Paso-Juarez TTDM, the
model was ready to be used to forecast demand of different transportation scenarios. A case
study for transborder BRT service is presented in Chapter VI.
66
Chapter VI. Scenario evaluation
With the multipurpose, multimodal capabilities introduced to the El Paso-Juarez TTDM, in
theory the model could be used to evaluate a diversity of transborder transportation scenarios
under different land-use conditions; also in theory, the TTDM can establish the impact that one
side of the border would experience in relation to transportation projects on the other side. The
present chapter presents a case study were these notions are tested, specifically seeking to
evaluate the effect of a transborder transit system.
Proposed transborder transit improvements
Transborder transit for the El Paso-Juarez area is not a new concept; in fact streetcars
ran across the border from the late 1800s, and electric trolleys and trolleybuses operated for half
a century in the region. Even as recently as 1994 there still was a bus service operating between
the two cities. Currently there is no transborder transit service in operation and there is very little
passenger information available on previous systems to understand what used to drive such
demand; nevertheless increased delay at the POEs intuitively enhances the potential of a
transborder transit service if this could somehow reduce crossing times, and as a result, overall
travel times.
Several factors play a role in providing desirable travel time for transit operation, among
others, service frequency (or headway) and average transit speed. For the present case study,
frequency has been pre-established in the 5-10 minute range, and overall transit speed in the 18-
23 mph range. These premises can be met in practice by a variety of transit concepts, one of
these being at-grade Bus Rapid Transit or BRT.
Alternative transborder transit routes
Also playing a significant role in travel time is route location and extension, since these
contribute to the accessibility or out-of-vehicle travel component. Review of the OD surveys has
allowed an initial identification of predominant transborder transit desire lines and transit user
markets. From these, two main corridors have been identified, and four route configurations have
been laid out for preliminary consideration.
67
The tourist-UTEP corridor: This transit user market ideally connects the University of
Texas at El Paso (UTEP) and the Pronaf area in Juarez, covering on its southbound
path, the tourist areas of downtown Juarez and those along the historic Avenida 16 de
Septiembre. Northbound connects the Mexican middle-class college student population
located East of Juarez, with a major college attractor in UTEP.
El Paso community services corridor: This transit user market is composed mainly by
lower-income transborder travelers living in West Juarez, that are attracted by retail in
downtown El Paso, as well as Fox Plaza and Bassett Center malls, and by community
services such as medical and non-college education along the Texas-Alameda corridor.
Southbound, the path connects El Paso’s South-central resident areas with relatives and
friends in Central and West Juarez.
El Paso Downtown
Stanton bridge
PDN bridge
Juarez Downtown
BOTA bridge
UTEP BassettCenter
Pronaf
Bus StationUTEP
Figure 36. Location map with main transborder destination
Based on these corridors, the following route alignments have been defined for further
modeling:
68
Option 1) Downtown connector:
This option simply provides a highly efficient transit connection between the CBDs on both sides
of the border. It relies heavily on the existing transit routes on both sides as the feeder system to
the rest of the region. Since there are no significant trip origins along the route in this option, it is
highly dependant on transfers, but is also the least expensive of the alternatives, based on length
(1.4 miles). Figure 37, shows the location of the transborder transit route for Option 1; for
simplicity, the existing transit network on both sides of the border is omitted.
Option 2) UTEP-Pronaf route.
This route follows the tourist-UTEP corridor previously described. It has a total length of 5.1 miles
stretching from UTEP to the Pronaf commercial area in Juarez. Figure 38 shows the transit route
location under this option.
Figure 37. Route for Option 1 Figure 38. Route for Option 2
Option 3) Basset-Bus Station route.
This route follows the El Paso community services corridor previously described. It has a total
length of 11.9 miles, connecting Bassett Center mall with the Juarez Intercity Bus Station. Figure
39 shows the transit route location under this option.
Option 4) Multi-route scenario.
This option attempts to capture the combined impact of the two desire lines described, by
implementing simultaneously the UTEP-Pronaf and the Bassett-Bus Station routes. Since these
69
two routes join their alignments for a stretch of 1.4 miles, the total combined length of
infrastructure comes to 15.6 miles. Figure 40 shows the transit route location under option 4.
Figure 39. Route for Option 3 Figure 40. Route for Option 4
Option 0) Do-nothing scenario. As a reference for the alternative routes, a do-nothing scenario
was analyzed as well. This also served as the test option to validate the TTDM for year 2005,
which is one of the forecast years under the current EPTDM.
General assumptions
As a base consideration, all options were modeled for year 2005. In addition, all the
described options were assumed to use the PDN POE to cross northbound, and the Stanton POE
to cross southbound. Specific stop locations have been designated on each of the alternatives,
averaging 0.35 miles separation.
As a BRT system, all routes will have dedicated lanes including the POE approaches,
thus allowing the bypass of vehicles waiting in line to cross. Therefore, crossing delay of
transborder BRT users at the primary inspection has been chosen at a maximum of 5 minutes.
Consistent with recent trends, average crossing delay for automobiles was increased by 15
minutes on all POEs, compared to the values presented in Chapter III for 1996.
70
GIS update
In preparation for running the TTDM, networks and demographics were updated to
represent year 2005 conditions. In this regard, the 2005 versions of the EPTDM and JZTDM
were used as references.
In the case of networks, links were added to represent new roadways, transit routes and
walking paths implemented since 1996; these changes represented less than 2% of the original
total link miles. The most significant changes were done for demographics, although the same
TAZ structure was kept. In summary, El Paso population grew 13% since 1996 (from 0.68 to
0.77 million), while Juarez population grew almost 22% (from 1.07 to 1.30 million) in the same
time period;. employment was updated accordingly, as summarized in Table 20.
Table 20. Year 2005 employment by economic activity for El Paso-Juarez
Economic El Paso Juarezactivity employment employment
basic 107,386 229,811retail 63,818 83,568service 175,470 104,460total: 346,674 417,839
As required by the TTDM, population and employment were combined at the TAZ level to
estimate area types. Figure 41 show the resulting distribution and as discussed in Chapter III, the
area type categories have different scales for each side of the border.
Figure 41. Area type distribution by TAZ for 2005
71
In the case of special generators, employment was kept unchanged, with 1996 values.
As another input to the TTDM, income was established by TAZ, under the three
categories described in Chapter III. Figure 42 schematically shows the region-wide distribution of
income by TAZ, while Table 21 presents a summary.
Figure 42. Income levels by TAZ for 2005
Table 21. Year 2005 population by TAZ income level for El Paso-Juarez
TAZ El Paso Juarezincome level* population population
Low inc (1, 2) 199,224 608,791Med inc (3, 4) 505,724 505,167High inc (5, 6) 61,300 181,342total: 766,248 1,295,300
Note *: The income levels have different scales
for each city, as explained in Chapter III.
Once this information was introduced in the proper GIS structures, the TTDM was ready
to be used for the evaluation of alternatives.
72
TTDM application
Having established the general premises of the transborder transit options under
consideration, and once the alternative BRT routes were separately coded in the transit network
GIS, the TTDM was used to forecast the corresponding passenger demand for year 2005. A brief
description of the results follows.
Trip estimation
Using the adjusted socio-economic conditions of the region, transborder trips were
estimated using the cross-classification trip rates developed under the trip generation model
component, and then distributed using the calibrated impedance functions and friction factors.
Overall totals (bi-directional) are shown in Table 22, disaggregated by trip purpose.
Table 22. Summary of TRBR-local daily person-trips by trip purpose for year 2005
HBW HBU HBSc HBIm HBSh HBO NHSc NHIm NHSh NHO TOTAL147 3,691 9,082 3,032 45,479 64,524 5,275 1,500 12,018 25,844 197,592 per-trips/day27,
As part of this process, the control totals for the external trips (TRBR exlo and TRBR
thru), were computed as a function of the TRBR local trips, keeping the same ratios as those
observed in 1996. Therefore the overall transborder person-trip totals for 2005 were as follows:
TRBR local (88%): 197,592 person-trips/day
TRBR exlo ( 7%): 15,718 person-trips/day
TRBR thru ( 5%): 11,227 person-trips/day
TRBR TOTAL: 224,536 person-trips/day
Each of these were kept in separate transborder OD person-trip matrices. The results
from the trip generation and distribution model components were considered the same for all the
options under evaluation.
Crossing mode choice
To run the mode choice components, skim matrices were developed for the three generic
modes: AUTO, TRANSIT, NON-MOTORIZED. In the case of TRANSIT, separate skims were
73
74
developed for each of the 5 transit options (including the do-nothing scenario, that does not
consider BRT), and then independently introduced into the mode choice component, with the
AUTO and NON-MOTORIZED skims. As a result, 5 different mode share matrices were
obtained, each corresponding to one of the options under evaluation; these combined with the
2005 OD person-trip matrix resulted in mode-specific OD trip matrices. Figure 43 summarize the
resulting crossing mode shares under each of the 5 options considered, for TRBR local person-
trips.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
AA PB PA PW PM
96 mod
Op 0
Op 1
Op 2
Op 3
Op 4
Figure 43. Integrated crossing mode shares under the 5 different options evaluated
For comparison purposes, Figure 43 shows the shares modeled for 1996 as well.
As an initial outcome, it is important to notice that the Do-Nothing scenario (Option 0)
shows a notorious reduction of auto-based crossings compared to the results from the 1996
modeled conditions (from 86% to 79%); this could be explained in great part by the increase in
crossing times experienced since, and coded for the 2005 TTDM version. Since the TTDM trip
generation component is not sensitive to crossing delay, the auto-based crossings are then
expected to be re-distributed to pedestrian-based crossings (PB, PA, PW, and PM). Further
reductions in the auto-based crossing share were obtained with each of the BRT options; and as
intuitively expected, Option 4 yielded the largest auto-based crossing reduction together with the
highest increase in transit demand, as depicted by the PB category.
Regarding external trips, mode shares were kept the same as those observed in 1996,
that is 80% automobile and 20% pedestrian crossing for TRBR exlo trips, and 100% automobile
crossing for TRBR thru trips.
Assignment for validation
Following the logic depicted in Chapter II, multimodal OD matrices for internal and
external conditions were combined and assigned to the different transportation networks. As an
overall validation test, the crossing volumes resulting under Option 0 are shown first in Tables 23
and 24.
Table 23(a) shows a comparison between counts and TTDM crossings for auto-based
transborder trips. As part of the assignment step, person-trips were converted to vehicle-trips
using the average occupancy factors established from the 1996 data (1.88 for TRBR local, 2.45
for TRBR exlo, and 3.33 for TRBR thru), therefore the comparison shows the modeled volumes
as vehicles/day.
Table 23(a). Count and TTDM results for auto-based crossings
POE dir errorby dir by loc by dir by loc
NB 9,389 8,913SB 0NB 3,457SB 4,119 9,734NB 21,467 27,613SB 30,168 28,346
ZARA NB 8,615 8,410SB 8,641 8,813
NB TOT 42,928 44,936 4.68%SB TOT 42,928 46,893 9.24%
TOTAL 85,856 91,829 6.96%
AUTOS/daycount model
PDN16,965 18,647 9.91%
STANTON
BOTA 51,635 55,959 8.37%
17,256 17,223 -0.19%
The overall total auto-crossings/day from the TTDM are 7% higher than those obtained
from field observations. When the results are disaggregated by direction of flow, or by crossing
location, the differences are still less than 10%, thus the assignment was considered validated.
75
Table 23(b) shows a comparison between counts and model auto-crossings obtained for
year 2005 from the conventional approach using external stations at the POEs. In this case, the
conventional model volumes have close to a 40% difference compared to the field counts.
Table 23(b). Count and conventional model volumes for auto-based crossings
POE dir error
by dir by loc by dir by locNB 9,389 17,500SB 0NB 3,457SB 4,119 8,500NB 21,467 29,121SB 30,168 40,079
ZARA NB 8,615 11,300SB 8,641 11,300
NB TOT 42,928 57,921 34.93%SB TOT 42,928 59,879 39.49%
TOTAL 85,856 117,800 37.21%
AUTOS/daycount model
PDN16,965 26,000 53.26%
STANTON
BOTA 51,635 69,200 34.02%
17,256 22,600 30.97%
Finally, Table 24 shows a comparison between counts and TTDM volumes for pedestrian
crossings.
Table 24. Count and TTDM results for pedestrian crossings
POE dir error
by dir by loc by dir by locNB 16,950 16,342SB 10,861 10,013NB 0 0SB 4,342 7,947NB 1,561 3,828SB 5,117 4,017
ZARA NB 1,809 1,231SB 0 1,163
NB TOT 20,320 21,401 5.32%SB TOT 20,320 23,140 13.88%
TOTAL 40,640 44,552 9.63%
PEDESTRIANS/daycount model
34,302 6.68%STANTON
1,809 2,394 32.34%
BOTA 6,678 7,845 17.48%
PDN32,153
The overall total pedestrian-crossings/day from the TTDM are less than 10% higher than
those obtained from field observations. When the results are disaggregated by direction of flow,
the difference grow to almost 14% in the case of SB flows, although NB shows a difference of just
76
over 5%. When results are viewed by POE location, the highest error is shown at the Zaragoza
POE with a difference of over 30%. The Downtown POE location nevertheless shows an error of
less than 10%, which under the present effort has been considered appropriate for transborder
transit demand modeling.
Assignment for alternative scenarios
Having established a preliminary validation of the TTDM, the assignment component was
completed to forecast transit demand for the alternative BRT routes. Table 25 shows the demand
estimated under Options 1 to 4.
Table 25. BRT demand forecasts under alternative routes
Fare: $0.75 dollars per trip with free transfer at POEAvg speed: 18mphPeak headway: 5 minutesTransfer time at POE: 5 minutes
Option 1Route Length boardings transferred Average
[miles] [pax/day] trips boardings/mileDowntown connector 1.4 23,120 93% 16,514
Option 2Route Length boardings transferred Average
[miles] [pax/day] trips boardings/mileUTEP-PRONAF 5.1 25,874 65% 5,073
Option 3Route Length boardings transferred Average
[miles] [pax/day] trips boardings/mileBASSETT-BUS ST 11.9 30,269 45% 2,544
Option 4Route Length boardings transferred Average
[miles] [pax/day] trips boardings/mileUTEP-PRONAF 10,687BASSETT-BUS ST 23,315Totals for Op 4 15.6 34,002 30% 2,180
The BRT alternative that yielded the highest demand was Option 4. Yet, Option 1
presented the highest ratio of passengers per mile, although with the highest transfer rate.
77
The previous set of tables show the versatility that the new approach has for evaluating a
variety of alternatives. Such forecasts would not have been possible through the conventional
approach for modeling the border.
Chapter summary
The current chapter has presented a case study where several non-conventional
solutions for transborder transportation have been evaluated. The case study also provided an
opportunity to test and validate the TTDM for a more recent set of conditions on both sides of the
border, updating networks, demographics and land-use. As a result, the exercise helped to
understand the added versatility and precision acquired with this type of approach.
This tool can now be used to asses the influence that one side of the border would
experience in relation to transportation projects and land-use changes on the other side, and thus
represents an important contribution for modeling of bi-national conurbations.
78
Chapter VII. Final analysis
The present research effort focused on development of a new procedure for regional
travel modeling of bi-national conurbations. This procedure steps away from the conventional
approach of studying each side separately, and thus from modeling ports-of-entry through the use
of external zones (as schematically depicted by Figures 44a and 44b). The new approach
extends the model boundaries beyond international limits, covering the urban areas of both sides,
and thus joining the two systems through the ports-of-entry, and eliminating the need for external
zones at these locations (as depicted by Figures 45).
(a) El Paso approach
POE
POE POE
(b) Juarez approach
POE
POE POE
internal zone
external zone
Figure 44. Conventional modeling approaches in the El Paso-Juarez bi-national conurbation
POE
POE
POE
Figure 45. New approach proposed for the bi-national conurbation
79
Crossing at the international ports-of-entry is nevertheless qualitatively different from
travel within a cohesive region. Among other issues, inspection times differ depending upon
direction of crossing, and trip purposes may be different for travelers from Mexico and the US.
Developing and validating an international crossing model with mode choice capability is
therefore more complex than simply joining together two existing TDMs. These issues have been
considered herein and an initial set of modeling methodologies have been researched and tested
with encouraging findings and improvement recommendations that are summarized below.
This study thus represents an unprecedented effort for any border urban area in the
United States or Mexico.
Preliminary conclusions
• The TTDM significantly improves POE forecast accuracy, with a multimodal dimension.
On average, the TTDM reduced the forecasting error on POE links by more than two-
thirds compared to the conventional approach, to less than 10%, including that for
pedestrian-based crossings, an ability not previously available. Although some POEs
under specific modes still showed high error levels (e.g., pedestrian movements at
Zaragosa), the TTDM was able to improve crossing volume forecasts in two different
years (with different land-use conditions), and for an array of access modes, without
having to rely on unrealistic adjustments of crossing delay or capacity.
Regarding the accumulated volume loading at specific network links beyond the POE
areas, the difference between the TTDM and the conventional approach was minimal.
This was due to the larger influence of purely internal travel on each side of the border,
relative to transborder travel.
Therefore the main TTDM contribution centers on POE forecasting accuracy with
multimodal capabilities, and with POE-attribute sensitivity that can be taken full
advantage to properly account for air quality impacts (a significant POE issue, due to the
context of inspection delay and thus motor-vehicle idling), and to study a wider range of
cost-efficient means of moving people across the international border such as adding
inspections booths on existing POEs versus reducing inspections times, or. adding a new
POE location versus implementing transborder transit, to mention a few possibilities.
80
• TTDM is sensitive to POE delay for mode choice estimation.
The TTDM shows appropriate sensitivity to crossing time but although crossing time is
included in the mode choice component, trip generation forecasts are unaffected by
changes in POE delay. This has the potential of yielding unrealistic travel volumes,
although with correct mode shares (proportions). So far the overall error levels shown
are within acceptable range, but it should be noted that this error increased from the 1996
to the 2005 version of the TTDM, over predicting auto-based crossings from less than 1%
to 7%; more importantly this increase in over-prediction error took place when increased
auto-based delay was coded for the 2005 version of the TTDM, which is counter intuitive.
Caution should therefore be exercised for future year forecasts if delay is significantly
different than current trends.
• POE intercept questionnaires are a cost-effective survey instrument for a TTDM.
The POE cordon intercept questionnaire seems to be an ideal survey tool; brief and
apparently non-intrusive to the subject, allows for fast data collection with minimum cost,
both for instrument preparation and application (minimum deployment costs). With a
notoriously high proportion of non-telephone households on the Mexican side, household
surveys end up being more expensive, and yet, could not seem to capture enough
transborder travelers to develop a multimodal, multi-POE bi-directional TTDM (a sample
of close to 50,000 trip records between the 94-El Paso, and the 96-Juarez household
surveys did not yield enough transborder records). Through the POE intercept
questionnaires close to 3,400 trip records were obtained and used to develop the TTDM.
Although sacrificing information from the subject’s household and that on trips beyond the
crossing OD pair, the POE questionnaires yielded enough robustness to validate a
preliminary model. Adding carefully designed questions, could nevertheless incorporate
trip-chaining patterns and more socio-economic attributes of the subject, with little added
cost.
• Current census data fields can be used to develop a TTDM.
To expand the surveyed travel patterns to the entire urban area, disaggregate census
data at the zone level was used under the current format offered by both INEGI and the
US Census Bureau. Although income data for the Mexican side was initially transformed
(per capita to household), further model development showed that this was not
necessary; moreover, there is no need to complement census information with other
81
sources. This is a promising prospect for replicating the effort at bi-national conurbations
along the US-Mexico border. This means following a similar approach with locally
gathered data; direct parameter transferability is not recommended.
Recommendations on further work
• TTDM refinements in trip-generation.
As previously concluded, crossing delay only plays a role in mode share estimation, but
not on actual transborder trip making. The over-estimation of trips for the 1996 and then
for the 2005 version, suggests that the trip-generation TTDM component should be
sensitive to delay, at least for choice trip purposes (e.g., shopping). A simple initial
approach could be the development of adjustment factors based on 1996 and 2005
forecasting errors, correlated to observed delay. A more sophisticated approach can
include discrete choice trip-generation models based on stated-preference (SP) surveys
that could yield trips by time-of-day, or on a time-series evaluation of crossing delay
behavior and crossing counts.
• TTDM refinements in auto-occupancy estimation.
Apparently not as significant as a delay sensitive trip-generation refinement, future
research should at least explore auto-occupancy sensitivity to delay (currently occupancy
is set constant at 1.88pax/veh for TRBR local trips). For this, auto-occupancy needs to
be surveyed at different crossing-delay scenarios to establish the level of correlation.
This was not reviewed for the 2005 validation, so there is a possibility that the auto-based
crossing over-estimation could have been answered at least partially by this factor.
• Refinements in TTDM accuracy by POE location.
As previously concluded, the current version of the TTDM showed a higher than
expected forecasting error for pedestrian crossings at the Zaragosa POE; in addition the
Downtown POEs could benefit from separate treatment, thus requiring more accuracy by
crossing direction on specific POE locations. This would suggest further refinements,
perhaps by disaggregation of the Mode Choice model, specifically exploring separate
models by user-residence location, and by trip purpose. This was somewhat evident by
the initial review of transborder travel patterns from the survey data, as described in
Chapter II. Nevertheless since the initial model calibration tests yielded acceptable
82
parameter criteria, the model was considered appropriate in a preliminary instance. If the
mode choice model is being refined, it would be convenient to have a separate variable
for pedestrian crossing time (P_QT). In 1996 this was not an issue due to insignificant
pedestrian crossing delay, but currently it is, and thus for the 2005 version, this delay had
to be incorporated in the overall pedestrian travel time.
• Refinements on delay estimation procedure.
As described in Chapter III, crossing delay has been estimated trough a combination of
simple processing-rate equations. For long time periods (daily averages) the resulting
delay seem to be consistent, showing little variability; yet, on short periods, the oscillation
of delay has not been characterized and could be significant due to special inspection
procedures, and thus, demand estimation might not be correctly modeled. With the
availability of micro-simulation software, the delay estimation could be enhanced, by
introducing various distributions of speeds, arrival volumes and processing times (as to
emulate variable inspection procedures taking place), in addition to improved reaction
behavior simulation and queue forming patterns for both motorized vehicles and
pedestrians. This has the potential of improving demand estimation precision, in an
iterative loop between macro and micro levels. New software is available that allows for
seamless interaction between these levels
• Refinements on survey design.
Finally, future POE survey efforts should specifically ask for place of residence, and
possibly even household income, household size, and pre/post-crossing trip-chains. This
would require careful wording as to avoid intimidating the subject, and to keep the brief
quality and thus effectiveness identified in this instrument. Related to the trip-generation
improvements, a small set of SP questions could be added. Due to the relative low-cost
of the POE intercept questionnaire, it is recommended as well that consideration be given
for doubling the sample number on any future survey efforts.
83
Appendix A
POE survey questionnaire Sample forms for BOTA (Córdova) POE
84
Form for PEDESTRIANS crossing in the northbound direction
85
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ogar
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uál e
s el
[1
] Reg
resa
r al h
ogar
5.¿C
uál e
s el
[1
] Reg
resa
r al h
ogar
mot
ivo
de[2
] Tra
bajo
o re
laci
onad
om
otiv
o de
[2] T
raba
jo o
rela
cion
ado
mot
ivo
de[2
] Tra
bajo
o re
laci
onad
oes
te v
iaje
?[3
] Est
udia
res
te v
iaje
?[3
] Est
udia
res
te v
iaje
?[3
] Est
udia
r[4
] Com
er/s
ocia
l/div
ersi
ón[4
] Com
er/s
ocia
l/div
ersi
ón[4
] Com
er/s
ocia
l/div
ersi
ón[5
] Com
pras
/pon
er g
asol
ina
[5] C
ompr
as/p
oner
gas
olin
a[5
] Com
pras
/pon
er g
asol
ina
[6] L
leva
r o re
coge
r a u
na p
erso
na[6
] Lle
var o
reco
ger a
una
per
sona
[6] L
leva
r o re
coge
r a u
na p
erso
na[7
] Otro
____
____
____
____
____
____
[7] O
tro__
____
____
____
____
____
__[7
] Otro
____
____
____
____
____
____
6.¿C
ómo
se ir
á[1
] Cam
inan
do e
xclu
siva
men
te6.
¿Cóm
o se
irá
[1] C
amin
ando
exc
lusi
vam
ente
6.¿C
ómo
se ir
á[1
] Cam
inan
do e
xclu
siva
men
tede
aqu
í a e
se
[2] E
n au
tobú
s o
rute
rade
aqu
í a e
se
[2] E
n au
tobú
s o
rute
rade
aqu
í a e
se
[2] E
n au
tobú
s o
rute
ralu
gar?
[3] E
n ta
xilu
gar?
[3] E
n ta
xilu
gar?
[3] E
n ta
xi[4
] En
auto
móv
il, v
an o
pic
k-up
[4] E
n au
tom
óvil,
van
o p
ick-
up[4
] En
auto
móv
il, v
an o
pic
k-up
7.H
ora
apro
x__
__:_
____
[ ] A
M ó
[ ]P
M
7.H
ora
apro
x__
__:_
____
[ ] A
M ó
[ ]P
M
7.H
ora
apro
x__
__:_
____
[ ] A
M ó
[ ]P
M
re/9
Puen
te d
e C
órdo
vaFe
cha:
__/N
ovie
m
Form for AUTOMOBILES crossing in the northbound direction
86
ENC
UES
TA D
E VI
AJES
EXT
ERN
OS
eov
Para
veh
ícul
os m
otor
izad
osSe
ntid
o:de
Juá
rez
a El
Pas
o
vehí
culo
1ve
hícu
lo 2
vehí
culo
31.
¿Aho
rita
de
[1] J
uáre
z1.
¿Aho
rita
de
[1] J
uáre
z1.
¿Aho
rita
de
[1] J
uáre
zdo
nde
vien
e?Lu
gar:
____
____
____
____
____
dond
e vi
ene?
Luga
r:__
____
____
____
____
__do
nde
vien
e?Lu
gar:
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
(Tac
har s
ólo
Cru
cero
:___
____
____
____
____
_(T
acha
r sól
oC
ruce
ro:_
____
____
____
____
___
(Tac
har s
ólo
Cru
cero
:___
____
____
____
____
_un
a op
ción
)__
____
____
____
____
__un
a op
ción
)__
____
____
____
____
__un
a op
ción
)__
____
____
____
____
__
[2] O
tra c
iuda
d o
pobl
ado
[2] O
tra c
iuda
d o
pobl
ado
[2] O
tra c
iuda
d o
pobl
ado
¿Cuá
l?__
____
____
____
____
__¿C
uál?
____
____
____
____
____
¿Cuá
l?__
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
__
2.¿Q
ue a
ctiv
idad
[1] H
o gar
2.¿Q
ue a
ctiv
idad
[1] H
ogar
2.¿Q
ue a
ctiv
idad
[1] H
ogar
real
izó
en e
se[2
] Tra
bajo
o re
laci
onad
ore
aliz
ó en
ese
[2] T
raba
jo o
rela
cion
ado
real
izó
en e
se[2
] Tra
bajo
o re
laci
onad
olu
gar?
[3] E
stud
iar
luga
r?[3
] Est
udia
rlu
gar?
[3] E
stud
iar
[4] C
omer
/soc
ial/d
iver
sión
[4] C
omer
/soc
ial/d
iver
sión
[4] C
omer
/soc
ial/d
iver
sión
(Tac
har s
ólo
[5] C
ompr
as/p
oner
gas
olin
a(T
acha
r sól
o[5
] Com
pras
/pon
er g
asol
ina
(Tac
har s
ólo
[5] C
ompr
as/p
oner
gas
olin
aun
a op
ción
)[6
] Lle
var o
reco
ger a
una
per
sona
una
opci
ón)
[6] L
leva
r o re
coge
r a u
na p
erso
naun
a op
ción
)[6
] Lle
var o
reco
ger a
una
per
sona
[7] O
tro__
____
____
____
____
____
__[7
] Otro
____
____
____
____
____
____
[7] O
tro__
____
____
____
____
____
__
3.¿A
don
de s
e[1
] El P
aso
3.¿A
don
de s
e[1
] El P
aso
3.¿A
don
de s
e[1
] El P
aso
dirig
e ah
orita
?Lu
gar:
____
____
____
____
____
dirig
e ah
orita
?Lu
gar:
____
____
____
____
____
dirig
e ah
orita
?Lu
gar:
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
(Tac
har s
ólo
Cru
cero
:___
____
____
____
____
_(T
acha
r sól
oC
ruce
ro:_
____
____
____
____
___
(Tac
har s
ólo
Cru
cero
:___
____
____
____
____
_un
a op
ción
)__
____
____
____
____
__un
a op
ción
)__
____
____
____
____
__un
a op
ción
)__
____
____
____
____
__
[2] O
tra c
iuda
d o
pobl
ado
[2] O
tra c
iuda
d o
pobl
ado
[2] O
tra c
iuda
d o
pobl
ado
¿Cuá
l?__
____
____
____
____
__¿C
uál?
____
____
____
____
____
¿Cuá
l?__
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
____
__
4.¿C
uál e
s el
[1
] Reg
resa
r al h
ogar
4.¿C
uál e
s el
[1
] Reg
resa
r al h
ogar
4.¿C
uál e
s el
[1
] Reg
resa
r al h
ogar
mot
ivo
de[2
] Tra
bajo
o re
laci
onad
om
otiv
o de
[2] T
raba
jo o
rela
cion
ado
mot
ivo
de[2
] Tra
bajo
o re
laci
onad
oes
te v
iaje
?[3
] Est
udia
res
te v
iaje
?[3
] Est
udia
res
te v
iaje
?[3
] Est
udia
r[4
] Com
er/s
ocia
l/div
ersi
ón[4
] Com
er/s
ocia
l/div
ersi
ón[4
] Com
er/s
ocia
l/div
ersi
ón(T
acha
r sól
o[5
] Com
pras
/pon
er g
asol
ina
(Tac
har s
ólo
[5] C
ompr
as/p
oner
gas
olin
a(T
acha
r sól
o[5
] Com
pras
/pon
er g
asol
ina
una
opci
ón)
[6] L
leva
r o re
coge
r a u
na p
erso
naun
a op
ción
)[6
] Lle
var o
reco
ger a
una
per
sona
una
opci
ón)
[6] L
leva
r o re
coge
r a u
na p
erso
na[7
] Otro
____
____
____
____
____
____
[7] O
tro__
____
____
____
____
____
__[7
] Otro
____
____
____
____
____
____
5.Ti
po d
eañ
o:__
__
mar
ca:_
____
____
____
___/
5.
Tipo
de
año:
____
m
arca
:___
____
____
____
_/
5.Ti
po d
eañ
o:__
__
mar
ca:_
____
____
____
___/
ve
hícu
lo
____
____
____
____
__ve
hícu
lo
____
____
____
____
__ve
hícu
lo
____
____
____
____
__
com
bust
ible
:[1
] gas
olin
aco
mbu
stib
le:
[1] g
asol
ina
com
bust
ible
:[1
] gas
olin
a[2
] die
sel
[2] d
iese
l[2
] die
sel
[3] o
tro__
____
____
____
[3] o
tro__
____
____
____
[3] o
tro__
____
____
____
6.#
Ocu
pant
es(in
cluí
r al c
hofe
r):_
____
__6.
# O
cupa
ntes
(incl
uír a
l cho
fer)
:___
____
6.#
Ocu
pant
es(in
cluí
r al c
hofe
r):_
____
__7.
Tipo
de
plac
as[1
] Nac
iona
les
7.Ti
po d
e pl
acas
[1] N
acio
nale
s7.
Tipo
de
plac
as[1
] Nac
iona
les
[2] F
ront
eriz
as[2
] Fro
nter
izas
[2] F
ront
eriz
as[3
] Ext
ranj
eras
, E
stad
o___
____
___
[3] E
xtra
njer
as,
Est
ado_
____
____
_[3
] Ext
ranj
eras
, E
stad
o___
____
___
8.H
ora
apro
x__
__:_
____
[ ] A
M ó
[ ]P
M
8.H
ora
apro
x__
__:_
____
[ ] A
M ó
[ ]P
M
8.H
ora
apro
x__
__:_
____
[ ] A
M ó
[ ]P
M
bre/
96ov
iem
____
/Nha
:Fe
ca
Ret
én:
Punt
e de
Cór
d
Appendix B
VB code to attach skims to survey trip records Combines basic mode skims into multimodal skims
Incorporates attributes into root database
87
Option Compare Database Option Explicit Private Sub Command0_Click() Dim TAB1, TAB2, TAB3, TAB4, TAB5, TAB6, TAB7, TAB20, TAB30 As Recordset Dim MyBASE1 As Database Dim n, mode, mnlmode, est, dir, tazO, tazD, O, D, purp, resid As Integer Dim inc, atyp, m_O, m_D, O_atyp, D_atyp, brEP, brJZ As Integer Dim cl As Long Dim hora, AT, WT, L As Double Dim osgen, dsgen As Variant 'fields for A xing / A access (MNLMODE 1) Dim aa_ivtt, aa_dist As Double 'fields for P xing / B access (MNLMODE 2) Dim pb_fare, pb_ivtt, pb_inwt, pb_trwt, pb_trtt, pb_actt, pb_egtt As Double 'fields for P xing / A access (MNLMODE 3) Dim pa_ivtt, pa_dist, pa_ovtt As Double 'fields for P xing / W access (MNLMODE 4) Dim pw_tt, pw_dist As Double 'fields for P xing / BA access (MNLMODE 5) Dim p5a_ivtt, p5a_dist As Double Dim p5b_fare, p5b_ivtt, p5b_inwt, p5b_trwt, p5b_trtt, p5b_actt, p5b_egtt As Double 'fields for Queue time at POE inspections Dim A_qt, P_qt, A_qc, P_qc As Double Set MyBASE1 = CurrentDb() Set TAB1 = MyBASE1.OpenRecordset("SkmA_BOT", DB_OPEN_TABLE) Set TAB2 = MyBASE1.OpenRecordset("SkmA_PDN", DB_OPEN_TABLE) Set TAB3 = MyBASE1.OpenRecordset("SkmA_ZAR", DB_OPEN_TABLE) Set TAB4 = MyBASE1.OpenRecordset("SkmB_A", DB_OPEN_TABLE) Set TAB5 = MyBASE1.OpenRecordset("SkmB_O", DB_OPEN_TABLE) Set TAB6 = MyBASE1.OpenRecordset("SkmB_P", DB_OPEN_TABLE) Set TAB7 = MyBASE1.OpenRecordset("SkmWLK", DB_OPEN_TABLE) Set TAB20 = MyBASE1.OpenRecordset("MNLext", DB_OPEN_TABLE) Set TAB30 = MyBASE1.OpenRecordset("MNLextSK", DB_OPEN_TABLE) DoCmd.Hourglass False DoCmd.Hourglass True TAB1.Index = "skm1_idx" TAB2.Index = "skm2_idx" TAB3.Index = "skm3_idx" TAB4.Index = "skm4_idx" TAB5.Index = "skm5_idx" TAB6.Index = "skm6_idx" TAB7.Index = "skM7_idx" TAB20.Index = "mnl_idx" TAB20.MoveFirst n = 0 Do While Not TAB20.EOF cl = TAB20!clave mode = TAB20!mode est = TAB20!ESTACION dir = TAB20!SENTIDO hora = TAB20!hora tazO = TAB20!EPTAZ_ORI tazD = TAB20!EPTAZ_DES purp = TAB20!purp resid = Nz(TAB20!resid) inc = TAB20!AToIN_PRD atyp = TAB20!ATYPE_ATT osgen = Nz(TAB20!PRD_SGEN) dsgen = Nz(TAB20!ATT_SGEN) m_O = TAB20!m_O m_D = TAB20!m_D mnlmode = TAB20!mnlmode O_atyp = TAB20!O_atyp D_atyp = TAB20!D_atyp
88
If dir = 1 Then ''''''''''''''''''''''''''''''''''''''''''P XING/W ACCESS SKIMS TAB7.Seek "=", tazO, tazD pw_tt = TAB7!wlk_time pw_dist = TAB7!length_ski ''''''''''''''''''''''''''''''''''''''''''P XING/B ACCESS SKIMS TAB5.Seek "=", tazO, tazD pb_fare = TAB5!fare pb_ivtt = TAB5!invehicle_ pb_inwt = TAB5!initial_wa pb_trwt = TAB5!transfer_w pb_trtt = TAB5!transfer_t pb_actt = TAB5!access_tim pb_egtt = TAB5!egress_tim If hora >= 0.292 And hora <= 0.375 Then TAB4.Seek "=", tazO, tazD pb_fare = TAB4!fare pb_ivtt = TAB4!invehicle_ pb_inwt = TAB4!initial_wa pb_trwt = TAB4!transfer_w pb_trtt = TAB4!transfer_t pb_actt = TAB4!access_tim pb_egtt = TAB4!egress_tim End If If hora >= 0.667 And hora <= 0.75 Then TAB6.Seek "=", tazO, tazD pb_fare = TAB6!fare pb_ivtt = TAB6!invehicle_ pb_inwt = TAB6!initial_wa pb_trwt = TAB6!transfer_w pb_trtt = TAB6!transfer_t pb_actt = TAB6!access_tim pb_egtt = TAB6!egress_tim End If ''''''''''''''''''''''''''''''''''''''''''A and P XING/A ACCESS SKIMS If est = 3 Then 'BOTA TAB1.Seek "=", tazO, tazD aa_ivtt = TAB1!Time aa_dist = TAB1!length_ski If purp = 55 Then AT = 1.27 WT = 13.11 L = 0.627 Else AT = 2.54 WT = 19.66 L = 0.94 End If pa_ivtt = aa_ivtt - AT pa_dist = aa_dist - L pa_ovtt = WT End If If est = 1 Or est = 2 Then 'PDN/STANTON TAB2.Seek "=", tazO, tazD aa_ivtt = TAB2!Time aa_dist = TAB2!length_ski If purp = 55 Then AT = 2.358 WT = 10.15 L = 0.471 Else AT = 3.55 WT = 15.22
89
L = 0.71 End If pa_ivtt = aa_ivtt - AT pa_dist = aa_dist - L pa_ovtt = WT End If If est = 4 Then 'ZARAGOZA TAB3.Seek "=", tazO, tazD aa_ivtt = TAB3!Time aa_dist = TAB3!length_ski If purp = 55 Then AT = 1.12 WT = 11.96 L = 0.567 Else AT = 1.12 WT = 17.94 L = 0.85 End If pa_ivtt = aa_ivtt - AT pa_dist = aa_dist - L pa_ovtt = WT End If ''''''''''''''''''''''''''''''''''''''''''P XING/BA ACCESS SKIMS If dir = 1 Then O = tazO D = tazD End If If dir = 2 Then O = tazD D = tazO End If If est = 3 Then brEP = 124 brJZ = 747 AT = 1.4 L = 0.7 WT = 0 End If If est = 1 Or est = 2 Then brEP = 2 brJZ = 690 AT = 2.5 L = 0.5 WT = 0 End If If est = 4 Then brEP = 340 brJZ = 1075 End If If mnlmode = 5 Then If (m_O = 2 And dir = 1) Or (m_D = 2 And dir = 2) Then 'BUS on JZ side If est = 4 Then AT = 2 L = 1 WT = 24 End If TAB5.Seek "=", O, brEP p5b_fare = TAB5!fare p5b_ivtt = TAB5!invehicle_ p5b_inwt = TAB5!initial_wa p5b_trwt = TAB5!transfer_w p5b_trtt = TAB5!transfer_t p5b_actt = TAB5!access_tim p5b_egtt = TAB5!egress_tim - WT If hora >= 0.292 And hora <= 0.375 Then
90
TAB4.Seek "=", O, brEP p5b_fare = TAB4!fare p5b_ivtt = TAB4!invehicle_ p5b_inwt = TAB4!initial_wa p5b_trwt = TAB4!transfer_w p5b_trtt = TAB4!transfer_t p5b_actt = TAB4!access_tim p5b_egtt = TAB4!egress_tim - WT End If If hora >= 0.667 And hora <= 0.75 Then TAB6.Seek "=", O, brEP p5b_fare = TAB6!fare p5b_ivtt = TAB6!invehicle_ p5b_inwt = TAB6!initial_wa p5b_trwt = TAB6!transfer_w p5b_trtt = TAB6!transfer_t p5b_actt = TAB6!access_tim p5b_egtt = TAB6!egress_tim - WT End If TAB1.Seek "=", brJZ, D p5a_ivtt = TAB1!Time - AT p5a_dist = TAB1!length_ski - L End If If (m_O = 2 And dir = 2) Or (m_D = 2 And dir = 1) Then 'BUS on EP side If est = 4 Then AT = 3.4 L = 1.7 WT = 10 End If TAB5.Seek "=", brJZ, D p5b_fare = TAB5!fare p5b_ivtt = TAB5!invehicle_ p5b_inwt = TAB5!initial_wa p5b_trwt = TAB5!transfer_w p5b_trtt = TAB5!transfer_t p5b_actt = TAB5!access_tim - WT p5b_egtt = TAB5!egress_tim If hora >= 0.292 And hora <= 0.375 Then TAB4.Seek "=", brJZ, D p5b_fare = TAB4!fare p5b_ivtt = TAB4!invehicle_ p5b_inwt = TAB4!initial_wa p5b_trwt = TAB4!transfer_w p5b_trtt = TAB4!transfer_t p5b_actt = TAB4!access_tim - WT p5b_egtt = TAB4!egress_tim End If If hora >= 0.667 And hora <= 0.75 Then TAB6.Seek "=", brJZ, D p5b_fare = TAB6!fare p5b_ivtt = TAB6!invehicle_ p5b_inwt = TAB6!initial_wa p5b_trwt = TAB6!transfer_w p5b_trtt = TAB6!transfer_t p5b_actt = TAB6!access_tim - WT p5b_egtt = TAB6!egress_tim End If TAB1.Seek "=", O, brEP p5a_ivtt = TAB1!Time - AT p5a_dist = TAB1!length_ski - L End If End If If mnlmode <> 5 Then If est = 4 Then AT = 2 L = 1
91
WT = 24 End If TAB5.Seek "=", O, brEP p5b_fare = TAB5!fare p5b_ivtt = TAB5!invehicle_ p5b_inwt = TAB5!initial_wa p5b_trwt = TAB5!transfer_w p5b_trtt = TAB5!transfer_t p5b_actt = TAB5!access_tim p5b_egtt = TAB5!egress_tim - WT If hora >= 0.292 And hora <= 0.375 Then TAB4.Seek "=", O, brEP p5b_fare = TAB4!fare p5b_ivtt = TAB4!invehicle_ p5b_inwt = TAB4!initial_wa p5b_trwt = TAB4!transfer_w p5b_trtt = TAB4!transfer_t p5b_actt = TAB4!access_tim p5b_egtt = TAB4!egress_tim - WT End If If hora >= 0.667 And hora <= 0.75 Then TAB6.Seek "=", O, brEP p5b_fare = TAB6!fare p5b_ivtt = TAB6!invehicle_ p5b_inwt = TAB6!initial_wa p5b_trwt = TAB6!transfer_w p5b_trtt = TAB6!transfer_t p5b_actt = TAB6!access_tim p5b_egtt = TAB6!egress_tim - WT End If TAB1.Seek "=", brJZ, D p5a_ivtt = TAB1!Time - AT p5a_dist = TAB1!length_ski - L End If 'wait times and tolls If est = 3 Then 'BOTA A_qc = 0 P_qc = 0 If dir = 1 Then A_qt = 26 Else A_qt = 0 P_qt = 0 If (hora >= 0.292 And hora <= 0.375) Then '7-9 If dir = 1 Then A_qt = 41 Else A_qt = 0 P_qt = 0 End If If (hora > 0.375 And hora <= 0.5) Then '9-12 If dir = 1 Then A_qt = 26 Else A_qt = 0 P_qt = 0 End If If (hora > 0.5 And hora <= 0.75) Then '12-6PM If dir = 1 Then A_qt = 28 Else A_qt = 0 P_qt = 0 End If If (hora > 0.75 And hora < 0.9167) Then '6-10PM If dir = 1 Then A_qt = 26 Else A_qt = 0 P_qt = 0 End If End If If est = 1 Or est = 2 Then 'PDN A_qc = 1.25 P_qc = 0.25 If dir = 1 Then A_qt = 8 Else A_qt = 0 P_qt = 0 If (hora >= 0.292 And hora <= 0.375) Then '7-9 If dir = 1 Then A_qt = 10 Else A_qt = 0 P_qt = 0
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End If If (hora > 0.375 And hora <= 0.5) Then '9-12 If dir = 1 Then A_qt = 8 Else A_qt = 0 P_qt = 0 End If If (hora > 0.5 And hora <= 0.75) Then '12-6PM If dir = 1 Then A_qt = 10 Else A_qt = 0 P_qt = 0 End If If (hora > 0.75 And hora < 0.9167) Then '6-10PM If dir = 1 Then A_qt = 8 Else A_qt = 0 P_qt = 0 End If End If If est = 4 Then A_qc = 1.25 P_qc = 0.25 If dir = 1 Then A_qt = 5 Else A_qt = 0 P_qt = 0 If (hora >= 0.292 And hora <= 0.375) Then '7-9 If dir = 1 Then A_qt = 10 Else A_qt = 0 P_qt = 0 End If If (hora > 0.375 And hora <= 0.5) Then '9-12 If dir = 1 Then A_qt = 5 Else A_qt = 0 P_qt = 0 End If If (hora > 0.5 And hora < 0.667) Then '12-4PM If dir = 1 Then A_qt = 6 Else A_qt = 0 P_qt = 0 End If If (hora >= 0.667 And hora <= 0.75) Then '4-6PM If dir = 1 Then A_qt = 8 Else A_qt = 0 P_qt = 0 End If If (hora > 0.75 And hora < 0.9167) Then '6-10PM If dir = 1 Then A_qt = 6 Else A_qt = 0 P_qt = 0 End If End If End If With TAB30 .AddNew !clave = cl !mode = mode !ESTACION = est !SENTIDO = dir !hora = hora !EPTAZ_ORI = tazO !EPTAZ_DES = tazD !purp = purp !resid = resid !AToIN_PRD = inc !ATYPE_ATT = atyp !PRD_SGEN = osgen !ATT_SGEN = dsgen !m_O = m_O !m_D = m_D !mnlmode = mnlmode !O_atyp = O_atyp !D_atyp = D_atyp If dir = 1 Then !aa_ivtt = aa_ivtt !aa_dist = aa_dist !pb_fare = pb_fare !pb_ivtt = pb_ivtt
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!pb_inwt = pb_inwt !pb_trwt = pb_trwt !pb_trtt = pb_trtt !pb_actt = pb_actt !pb_egtt = pb_egtt !pa_ivtt = pa_ivtt !pa_dist = pa_dist !pa_ovtt = pa_ovtt !pw_tt = pw_tt !pw_dist = pw_dist !p5a_ivtt = p5a_ivtt !p5a_dist = p5a_dist !p5b_fare = p5b_fare !p5b_ivtt = p5b_ivtt !p5b_inwt = p5b_inwt !p5b_trwt = p5b_trwt !p5b_trtt = p5b_trtt !p5b_actt = p5b_actt !p5b_egtt = p5b_egtt !A_qt = A_qt !A_qc = A_qc !P_qt = P_qt !P_qc = P_qc End If .Update End With n = n + 1 TAB20.MoveNext If n = 1000 Then MsgBox "van 1,000" If n = 10000 Then MsgBox "van 10,000" Loop TAB1.Close TAB2.Close TAB3.Close TAB4.Close TAB5.Close TAB6.Close TAB7.Close TAB20.Close TAB30.Close DoCmd.Hourglass False MsgBox "Terminó!...Viajes Totales:" & n End Sub
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Appendix C
Friction Factor tables
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HBW trip purpose HBSh trip purpose HBO trip purpose
TIME ADJFF0 249801 249302 248503 248004 247105 246706 245807 244708 243109 24100
10 2362011 2227012 2020013 1808014 1633015 1445016 1274017 1042018 800019 594020 387021 163022 69023 16224 11225 6826 5027 3128 2529 2030 1531 1032 833 634 435 336 237 138 139 140 141 142 143 144 145 146 047 048 049 050 0
TIME ADJFF0 45001 44512 43953 43084 42085 40776 38847 36728 34919 3236
10 298611 271812 240613 208914 183915 164016 142817 122218 104119 87320 71721 59222 48023 39324 30525 24926 18127 11228 8729 6230 4431 3132 2833 1934 1835 1736 1637 1538 1439 1340 1241 1142 1043 944 845 746 647 548 449 350 251 152 0
TIME ADJFF0 42441 42412 42133 41724 40985 40176 39087 37658 35669 3342
10 320811 294012 263213 234014 212215 182616 153117 91218 72519 43920 29221 15622 12823 9024 6525 5326 5327 4428 2829 1930 831 132 133 134 135 136 137 138 139 140 141 142 143 144 045 046 047 048 049 050 0
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Acronyms
AON: All-or-Nothing
AGEB: Area Geo-Estadística Básica
EXLO: External-local trip purpose
EPTDM El Paso Travel Demand Model
GIS: Geographic information system
HBW: Home-based-work trip purpose
HBNW: Home-based-non-work trip purpose
HBO: Home-based-other trip purpose
INEGI: Instituto Nacional de Estadística, Geografía e Informática
JZTDM Juarez Travel Demand Model
MNL: Multinomial logit
NHB: Non-home-based trip purpose
OD: Origin-destination
POE: Port-of-entry
SP: Stated preference
TDM: Travel demand model
TLFD: Travel-length frequency distribution
THRU: Through-travel trip purpose
TTDM: Transborder travel demand model
TRBR: Transborder trip category
UE: User-Equilibrium
VMT: Vehicle-miles traveled
VHT: Vehicle-hours traveled
WESML: Weighted Exogenous Sample Maximum Likelihood
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References
1. Traffic maps for year 2000; El Paso and Laredo Districts. Texas Department of Transportation, Transportation Planning and Programming Division.
2. US Department of Transportation, Bureau of Transportation Statistics based on data from US Customs Service, Office of Field Operations, Operations (March of 2004), www.bts.programs/international/border_crossing_entry_data/us_mexico/index.html
3. “Forecasting Freight Traffic Between the US and Mexico”, Y. Fang, R. Harrison, and H. Mahmassani. University of Texas at Austin, Center for Transportation Research 1996 (CTR 1319-2).
4. “A Methodology for Determining the Freight Border Transportation Impact of the North American Free Trade Agreement”, C. Strong, R. Harrison, and H. Mahmassani. University of Texas at Austin, Center for Transportation Research 1996 (CTR 1319-4).
5. “Transportation Issues and the US-Mexico Free Trade Agreement”, R. Harrison, L. Boske, C. Lee, J. McCray. University of Texas at Austin, Center for Transportation Research 1997 (CTR 1319-6F).
6. “Texas’ Role as a U.S.-Mexico Trade Gateway”, A. Weissman, R. Harrison, and M. Trevino. University of Texas at Austin, Center for Transportation Research 1995 (CTR 2932-3F).
7. “Analysis of US-Mexico Traffic within Texas”, R. Harrison, and A. Weissman. University of Texas at Austin, Center for Transportation Research 1995 (CTR 2932-2).
8. “Overview of the Texas-Mexico Border: Background”, J. Hanania, A. Weissman, and R. Harrison. University of Texas at Austin, Center for Transportation Research 1994 (CTR 1976-1).
9. “US-Mexico Trade and Transportation: Corridors, Logistics Practices, and Multimodal Partnerships”, L. Boske, and R. Harrison. University of Texas at Austin, Lyndon Baynes Johnson School of Public Affairs 1995 (LBJ Report 113).
10. “Transborder Traffic and Infrastructure Impacts on the City of Laredo, Texas”, C. Said, R. Harrison, and R. Hudson. University of Texas at Austin, Center for Transportation Research 1993 (CTR 1312-1).
11. “Executive Summary – Truck Traffic in Laredo, Texas: A Case Study of Issues and Remedies”, R. Harrison. University of Texas at Austin, Center for Transportation Research 1993 (CTR 1312-3F).
12. “Effect of the North American Free Trade Agreement on the Transportation Infrastructure in the Laredo-Nuevo Laredo Area”, R. Espinosa, R. Harrison, F. McCullough. University of Texas at Austin, Center for Transportation Research 1993 (CTR 1312-2).
13. “Commercial Surface Transportation Efficiency at the Texas/Mexico Border: A Look at Laredo”, M. Sassin, R. Harrison, and L. Boske. Southwest Region University Transportation Center, College Station 1995 (SWUTC-95-465640-1).
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14. “Texas Model Border Crossing Project: Retro-Fit Report”, C. Quiroga, E. Kraus, B. Stockton, and R. Harrison. University of Texas at Austin, Center for Transportation Research 2003 (CTR 9014-1).
15. “A Prototype Southern Border Facility to Expedite NAFTA Trucks Entering the United States”, B. Bockner, B. Stockton, D. Burke, and R. Harrison. Texas A & M University, Texas Transportation Institute 1996 (MS-7796).
16. “Briefing Document on Texas Model Border Crossing Project”, R. Harrison, and B. Stockton. University of Texas at Austin, Center for Transportation Research 2002 (CTR 9014-P5).
17. “Texas-Mexico Toll Bridge Study: Summary Report”, B. F. McCullough, R. Harrison, and A. Weissman. University of Texas at Austin, Center for Transportation Research 1994 (CTR 1976-6F).
18. “Overview of the Texas-Mexico Border: Assessment of Traffic Flow Patterns”, A. Weissman, M. Martello, J. Hanania, M. Shamieh, C. Said, B. F. McCullough, and R. Harrison. University of Texas at Austin, Center for Transportation Research 1994 (CTR 1976-3).
19. “Overview of the Texas-Mexico Border: Database”, J. Hanania, A. Weissman, and R. Harrison. University of Texas at Austin, Center for Transportation Research 1993 (CTR 1976-2).
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21. “Northern Border Crossing Noteworthy Practices Reference Guide >NP-19”, online resource guide prepared by the Federal Highway Administration (page last modified on July 20, 2004), www.fhwa.dot.gov/uscanada/index.htm
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Transportation, Transportation Planning and Programming Division (November 2000)
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Lerman. MIT Press (1985)
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Vita
Salvador Arturo González-Ayala was born in Mexico City, Mexico on August 17, 1966,
the son of Emilio González Domínguez and Catalina Ayala Madrid. In 1989 he received the
degree of Bachelor of Science in Civil Engineering from the Instituto Tecnológico y de Estudios
Superiores de Monterrey, in Monterrey, México, and in 1991 he received the degree of Master of
Science in Engineering, from the University of Texas at Austin. He has worked as a
transportation engineer and planner on the US-Mexico border since 1991.
Permanent Address: 1368 Copper Gate, El Paso, Texas 79936
This dissertation was typed by the author.
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