This document summarizes and compares different methods for modeling traffic demand, including the traditional four-step model, activity-based models, and microsimulation/agent-based modeling. The four-step model is described as having shortcomings like focusing on aggregate behavior rather than individuals. Activity-based models provide more nuanced modeling by using "tours" rather than trips as the basic unit and considering factors like household interactions. Microsimulation and agent-based modeling simulate individual movements but may not accurately model an entire region. The document examines issues with predicting traffic from new developments and argues newer methods can better account for factors like internal capture rates and parking costs.

1. Creative Methods for Modeling Traffic Demand
John-Mark Palacios
Transportation and Supply Chain Systems
Dr. Evangelos Kaisar
26 July 2013

2. Palacios i
Table of Contents
Introduction .......................................................1
Four-Step Model ..................................................2
Trip Generation .................................................................3
Trip Distribution ................................................................3
Mode choice .....................................................................4
Assignment.......................................................................4
Methodology .....................................................................5
Activity-Based Model.............................................8
Traffic demand from a development ....................... 10
Microsimulation and Agent based modeling............... 12
Methodology ................................................................... 14
Interpretation ................................................................. 18
Conclusion ....................................................... 19
Bibliography ..................................................... 21

3. Palacios 1
Introduction
Transportation demand forecasting is an integral part of the transportation
planning process, yet it is also one of the most imperfect. Typically, transportation
planners have used the Four-Step Model or rough tables pulled from the Institute of
Transportation Engineers' (ITE) Trip Generation Manual to predict trips from a
proposed development or within a region. The Four-Step Model has several
shortcomings, however. McNally and Rindt point out some of these flaws, such as
the fact that it focuses on aggregate behavior instead of individual driver behavior,
the artificial constraints it places on an individual's choice, and neglecting some of
the reasons why individuals choose a certain route1. The Four-Step Model reduces
each trip to a mode choice without allowing a combination of modes, or outright
ignores mode choice. The ITE Trip Generation Manual also tends to underestimate
the internal capture rate of a proposed development, especially if it doesn't fit the
old typical suburban development model. Shoup also points out that the Trip
Generation Manual fails to consider economic realities of things like parking2.
Similar issues occur with the four-step model. Both these methods are frequently
used to determine traffic impacts from proposed developments. The Activity-Based
Model seeks to address many of the shortcomings in the Four-Step Model by
providing a finer level of detail. Very few planning agencies in the U.S. are currently
using this model, however, so it has not been thoroughly tested as a tool for
determining development impacts.
1 Michael G. McNally and Craig Rindt, The Activity-Based Approach, Recent Work
(Irvine, CA: Institute of Transportation Studies, UC Irvine, November 17, 2008), 6,
http://escholarship.org/uc/item/86h7f5v0.
2 Donald C. Shoup, “Truth in Transportation Planning,” Journal of Transportation and
Statistics 6, no. 1 (2003): 11.

4. Palacios 2
Modelers seek accuracy and are likely to brush off anything that is not considered a
professional transportation modeling tool, but computer games have begun to
implement algorithms similar to activity-based modeling, called agent-based
modeling. While they may not be as accurate at modeling traffic as purpose-
designed tools, they do have a potential place in the transportation planning
profession. This project takes a look at the methods and capabilities of a purpose-
built transportation planning model and an agent-based simulation game.
Four-Step Model
True to its name, the four-step model consists of the following four steps that are
undertaken to predict trips:
1. Trip Generation
2. Trip Distribution
3. Mode Choice
4. Assignment3
Trips are categorized based on origin and destination, primarily focusing on home,
work, and other destinations, and delineated according to the following criteria:
 Home-based work: trips to or from work, beginning or ending at home.
 Home-based nonwork: trips beginning or ending at home that do not begin
or end at work.
 Nonhome based: trips neither beginning nor ending at home4.
3 Cambridge Systematics, Inc. et al., Travel Demand Forecasting: Parameters and
Techniques (Washington, D.C.: National Cooperative Highway Research Program,
2012), 3, http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_716.pdf.
4 Ibid., 31.

5. Palacios 3
While not discussed in the literature reviewed for this paper, it could be argued that
the heavy focus on home and work trips no longer fits with the modern mobile
society, with people carrying smartphones, tablets, and computers, and able to work
from any location. The four-step model has been around since the 1950s5, before the
advent of the Information Age.
Trip Generation
Trip Generation takes into consideration the characteristics of the individual,
generally done at an aggregate level using Traffic Analysis Zones. These could be
comparable to the Census block, and include data such as the following that might
be obtained from the Census or the American Community Survey:
 Population
 Employment
 Auto ownership
 Income
 Employment industry6
 Household size
Trip Distribution
This step calculates the number of trips between different Traffic Analysis Zones. If
a number of homes are within Zone A and a number of employment centers are in
Zone B, then those living in Zone A who work in Zone B would be expected to
generate home-based work trips between the two zones.7
5 McNally and Rindt, The Activity-Based Approach, 5.
6 Cambridge Systematics, Inc. et al., NCHRP 716, 3.
7 Ibid.

6. Palacios 4
Mode choice
This step splits the trips calculated in step two into motor vehicle, transit, bicycle,
and walking trips, based on the local area's options and the local residents'
proclivity towards each mode. NCHRP Report 716 begins the section on mode
choice by pointing out that this step is often skipped in order to simplify things and
return a number of vehicle trips instead of person trips.8 This is really a significant
flaw with the four-step model, or at least with the way it is frequently implemented.
While planners try to design more livable cities where people have alternatives to
the car, and citizens clamor for these options9, transportation planners assume that
everyone is driving. Since neighbors and local officials are primarily interested in
the automobile traffic impacts of a proposed development, this further encourages
skipping this step. This is a severe disconnect between the livable streets movement
and the tried-and-true transportation forecasting methods.
Assignment
The final step takes the vehicle trips and assigns them to a route in the roadway
network. This will factor in details such as travel time on each route alternative and
congestion on each route, and give a total number of added vehicle trips to that
network. If the transit mode was considered, rider trips will be assigned to the
transit network, with individuals choosing which routes and stops to use, taking into
consideration travel time and related factors along the way.10
8 Ibid., 53–55.
9 Angie Schmitt, “Poll: Republicans Support Transpo Policies to Avert Climate
Change, Too,” Streetsblog Capitol Hill, June 16, 2011,
http://dc.streetsblog.org/2011/06/16/yale-poll-americans-support-transpo-
policies-to-avert-climate-change/.
10 Cambridge Systematics, Inc. et al., NCHRP 716, 4–5.

7. Palacios 5
Methodology
The four-step model is generally run using Florida's version of CUBE Voyager, called
the Florida Standard Urban Transportation Model Structure, or FSUTMS. There are
other software that can run this model, but the focus of this report is on FSUTMS.
The models are available for any region of Florida online at fsutmsonline.net.
System
The system used to run FSUTMS had an Intel Xeon E5607 CPU running at 2.27 Ghz,
with 24GB RAM and running Windows 7 Enterprise for the operating system. With
this system, a network-wide model run took over 6 hours to complete.

8. Palacios 6
Model run
The desired model is opened using the FSUTMS
launcher, which in our case was SERPM 6.5.3. This
particular model was originally set up so the
Metropolitan Planning Organizations could develop the
2035 Long Range
Transportation Plan, so there
is a 2005 baseline scenario as well as a 2035 scenario,
with the projected changes in demographics (see Figure
1). Running the model is done by simply double-
clicking on the desired
scenario and going
through the
screens that follow. Optionally, a new
scenario can be created as a "child" of one of
those already setup. SERPM has the entire
roadway network for the three county area set
up already. It can be edited by selecting the
S65_{Year}.NET file under "Inputs" in the data section
(refer to Figure 2). Note
that the edit will affect
whichever scenario is selected
in the Scenario section. The
network shown in Figure 3 is
made up of links for
roadways and nodes for
intersections. New links
can be added to show new
roadways, or links can be edited to
Figure 1. Scenarios in SERPM
6.5.3
Figure 2. Input Data in SERPM
6.5.3
Figure 4. Output Network file in
SERPM.

9. Palacios 7
modify number of lanes or other roadway properties.
Once the model is run, the network file can be selected in order to display the
results, such as total volume for each node (Refer to Figure 4). Figure 5 shows a
portion of the network around Florida Atlantic University in Boca Raton, with the
volumes turned on for each link. If a second run were performed with modifications
to links representing a roadway improvement or demographics representing a
proposed development, this could be used to perform a visual
comparison of the two scenarios.
Figure 3.
SERPM
roadway
network,
links and
nodes.

10. Palacios 8
Figure 5. Links with volume display turned on in SERPM after the model is run.
Activity-Based Model
The Activity-Based Model (ABM) offers a much more nuanced method, essentially
performing a microsimulation for each person in the study. Instead of using the trip
as the basic unit, ABM uses the "tour," which is defined as the sequence of trips that
begin and end at the same location.11 Instead of treating the decisions for each trip
11 Ibid., 89.

11. Palacios 9
separately, ABM recognizes that each trip of a tour is dependent on the other. For
instance, if someone drives alone to work, they are not likely to carpool on the way
home. Because this model considers the entire tour, it takes into account "soak
duration," or the time spent at a destination. Stopping by the store for 30 minutes
after work would give a 30-minute soak duration. Household behavior is linked, so if
it becomes inconvenient for one parent to drop a child off at school on his way to
work, the other has to add that trip into her tour.12
These nuances theoretically add up to a more accurate model, although very few
planning agencies have put Activity-Based Models into practice. In 2011,
Metropolitan Planning Organizations in Portland, San Francisco, Sacramento, Los
Angeles, New York City, Denver, Atlanta, and Columbus, Ohio had implemented
Activity-Based Models.13 San Diego completed development of an Activity-Based
Model in January 2013,14 which South Florida borrowed to adapt to our own
region.15
One of the benefits of the Activity-Based Model includes the ability to model more
data in the future, as the models are tweaked. McNally and Rindt suggest that
12 Ibid., 91–92.
13 Ibid., 93.
14 Wu Sun, “Activity-Based Model Update” (presented at the Transportation
Modeling Forum, San Diego, June 2013), 59,
http://www.sandag.org/uploads/publicationid/publicationid_1763_16133.pdf.
15 Rosella Picado, “A Test of Transferability: The SE Florida Activity-Based Model”
(presented at the TRB National Planning Applications Conference, Columbus, Ohio,
May 7, 2013), 4,
http://www.trbappcon.org/2013conf/presentations/319_1_4_319_Southeast%20Fl
orida%20ABM%20Transfer.pptx.

12. Palacios 10
abilities in the long term might include adding new behavior and performing agent-
based simulation.16
Li points out that the activity-based model in development for South Florida, the
Southeast Florida Regional Planning Model (SERPM) version 7, used different
demographic data and analysis zones than the four-step model (SERPM version 6),
2010 in the new model and 2005 in the old model.17 So running these two models
would have some differences inherent in the demographics that will generate
differing results. Since the model is still under development, we were unable to
obtain access to SERPM 7. While Florida utilizes CUBE Voyager software, other areas,
such as San Diego's activity-based model on which our local one was based,18 utilize
different software, to which we do not have access at the University.
Traffic demand from a development
Various reports take issue with the status quo of trip forecasting from a proposed
development. With the four-step model developed in the post-war era of suburban
growth, and the ITE Trip Generation Manual developed in the same era, it should
come as no surprise that the four-step model frequently ignores non-vehicular
travel and the Trip Generation Manual focuses on suburban areas without transit or
pedestrian facilities.19 Modern trends such as New Urbanism and Transit Oriented
Development that focus on providing mixed land use as well as transit access and
walking and bicycling amenities get treated equally to a suburban strip mall
surrounded by a sea of parking and accessible only by car.
16 McNally and Rindt, The Activity-Based Approach, 15.
17 Shi-Chiang Li, “RE: Activity Based Modeling,” June 6, 2013.
18 Picado, “A Test of Transferability: The SE Florida Activity-Based Model.”
19 Shoup, “Truth in Transportation Planning,” 2.

13. Palacios 11
While these developments generate fewer trips because individuals can live, work,
and shop within the same area, methods like the Trip Generation Manual do not
effectively account for this internal trip capture rate.20 The four-step model only
looks at whether a trip is internal to the model or external, traveling to or from an
area outside of the model's region.21 One way this method could account for a
development's internal trip capture would be by setting the region to be the
development boundaries; but this would cripple the model by only providing one
traffic analysis zone. Calandra proposed a methodology for VMT disaggregation that
basically adds a step of reorganizing the zones into internal/external after the
Assignment step of the four-step model, but this can only look at larger
developments with multiple traffic analysis zones.22 Ewing, Dumbaugh, and Brown
endeavored to create a model for internal trip capture by evaluating 20 mixed-use
communities in South Florida and viewing demographic characteristics, but this
early effort has some shortcomings that the authors acknowledged—mostly due to
larger communities that incorporated as cities skewing the results.23 These all seem
to have issues determining internal trip capture with smaller scale developments.
20 R. Ewing et al., “Traffic Generated by Mixed-Use Developments—Six-Region Study
Using Consistent Built Environmental Measures,” Journal of Urban Planning and
Development 137, no. 3 (2011): 248–261, doi:10.1061/(ASCE)UP.1943-
5444.0000068.
21 Cambridge Systematics, Inc. et al., NCHRP 716, 48–49.
22 Mike Calandra, “VMT Disaggregation Methodology” (presented at the
Transportation Modeling Forum, San Diego, June 2013), 30–36,
http://www.sandag.org/uploads/publicationid/publicationid_1763_16133.pdf.
23 Reid Ewing, Eric Dumbaugh, and Mike Brown, “Internalizing Travel by Mixing
Land Uses: Study of Master-Planned Communities in South Florida,” Transportation

14. Palacios 12
In Truth in Transportation Planning, Shoup proposes that the data need to account
for the price of parking, as the traditional model encourages development of more
free parking.24 While the four-step model can account for parking costs in a
simplified manner, NCHRP 716 recognizes that more realism is needed to evaluate
changes in parking cost as well as mixed-use developments, and implies that
activity-based modeling would better account for them.25
Microsimulation and Agent based modeling
Other methodologies to determine traffic impacts include microsimulation or agent-
based modeling. Both essentially simulate the movements of each individual in a
network in order to gauge how the whole system will function. Microsimulation
generally refers to a simulation performed on a smaller scale to analyze a corridor
instead of a region—but one that simulates movements on a microscopic, or
individual, level. Programs such as CORSIM are used to perform this type of
microsimulation. Figure 6 shows a screenshot of a CORSIM simulation. Strengths of
this type of microsimulation are in modeling the minor details that contribute to
congestion such as driver behavior, weaving, lane choice, etc.
Figure 6. CORSIM simulation of I-4 in Orlando, showing one on-ramp and the merge area. Each vehicle is
modeled as a separate agent for this stretch of I-4 in Orlando, but the segment was modeled alone.
Research Record: Journal of the Transportation Research Board 1780, no. -1 (January
1, 2001): 115–128, doi:10.3141/1780-11.
24 Shoup, “Truth in Transportation Planning,” 11–12.
25 Cambridge Systematics, Inc. et al., NCHRP 716, 89.

15. Palacios 13
Agent-based modeling can perform similar activities for a regional level. To some
degree, an activity-based model is an agent-based model.26 At some level, however,
they may aggregate data instead of keeping the individual simulation. If the goal is
to merely display overall traffic volumes similar to that shown in Figure 5 for the
Four-Step model, then the individual data will be aggregated into the total volumes
for each link. A true agent-based model can maintain the individual agents into a
simulation.
Non-professional traffic simulators have begun utilizing agent-based simulation.
Developers of the recently released game Simcity 5 touted its Glassbox agent-based
model that ran every aspect of the simulation. For traffic, it modeled an individual's
trip, what path it chose, and maintained the simulation of each individual
throughout the interface.27 The prior version of Simcity, known as Simcity Societies,
had a similar agent-based approach, as the program did offer the ability to follow
individuals around the city and showed traffic based on individual movements.28
26 Ana L. C. Bazzan and Franziska Klügl, “A Review on Agent-based Technology for
Traffic and Transportation,” The Knowledge Engineering Review FirstView (2013):
6–7, doi:10.1017/S0269888913000118.
27 Andrew Willmott, “GlassBox: A New Simulation Architecture” (presented at the
Game Developer’s Conference, San Francisco, March 7, 2012),
http://www.andrewwillmott.com/talks/inside-
glassbox/GlassBox%20GDC%202012%20Slides.pdf.
28 Electronic Arts, SimCity Societies: Interview, interview by Strategy Informer, Web,
accessed July 26, 2013,
http://www.strategyinformer.com/pc/simcitysocieties/115/interview.html.

16. Palacios 14
Methodology
For testing purposes, we had access to Cities in Motion 2, a city simulator with a
focus on transit. We did not have access to technical information on the modeling
algorithm of this game, but it also seems to be agent-based. Bazzan and Klügl point
out that the first version of this game was agent-based,29 and our observations with
the second version's behavior would agree. The following section documents how
this game models traffic.
System
The test system was an iMac with an Intel Core 2 Duo CPU running at 3.06 Ghz, with
6 GB RAM and an ATI Radeon HD 2600 Pro graphics card with 256 MB VRAM. This
is a bit underpowered to run Cities in Motion 2, which actually has a minimum video
memory requirement of 512MB RAM. While the CPU meets the minimum
requirement, the recommended processor requirement of 3Ghz Quad Core would
have worked better. Many times the simulation slowed to a crawl with the CPU
usage at 100%.
Base Map
The base map used for testing was a fan-made recreation of Chicago, including
topography and a fairly accurate road and rail network within the boundaries.30
Modifications were made to this map by adding transit routes and modifying
roadways in order to visualize impacts to traffic patterns.
29 Bazzan and Klügl, “A Review on Agent-based Technology for Traffic and
Transportation,” 10.
30 Chase Moore, “Chicago 1.0,” June 7, 2013,
http://steamcommunity.com/sharedfiles/filedetails/?id=151352135.

17. Palacios 15
Behavior
Just like SimCity, Cities in Motion 2 allows you to track an individual's movements
across the city. See Figure 6 for an example of what this looks like. Each vehicle in
that picture is being modeled for an agents' trip. Individuals decide whether to take
their private vehicle or public transit, based on factors such as income and ticket
prices and transit coverage and frequency. (It is not entirely clear whether travel
time is a factor, because the roadways seemed to back up for days, regardless of
peak hours—and drivers could easily sit in traffic for two hours or more.) Trip
purpose is also considered, as the info window shown in Figure 7 shows
"commercial building" for the origin and destination, while the workplace is
different, indicating that this was a shopping trip or something similar.
Figure 7. Cities in Motion 2 screenshot showing white arrow and large info box tracking a motorist, while
the mouse hovers over another behind him.
Evaluation
Cities in Motion 2 does collect some aggregate data, providing a visualization of
traffic hotspots that can be overlayed on top of the graphics. Figure 8 shows what
this looks like. This would be the closest thing to CUBE's link volume screen
illustrated previously in Figure 5, albeit much simpler for a layperson to understand.
It should be noted that there seem to be a number of odd behaviors—not
necessarily bugs, probably just a result of the simplifications done to make the game

18. Palacios 16
playable in real time. Occasionally, a vehicle will disappear and the person can
suddenly be found in a building across town. This could merely be a reset check
built into the code after it realizes someone has been stuck in traffic all night. Other
issues include a wayfinding algorithm that seems somewhat haphazard, as vehicles
would frequently make u-turns at on-ramps or use on-and off-ramps as thru lanes
instead of staying on a freeway. See Figure 8 for an example.

19. Palacios 17
Figure 8. Cities in Motion 2 Traffic Density, before (top) and after (bottom) roadway improvements and
added transit service.

20. Palacios 18
Interpretation
Is there any purpose to city
simulation games besides just
gaming? With a lack of
sophistication compared to
professional transportation
modeling tools, the first response
would be to write the simulation
games off as nothing more than
toys. However, there could be
several potential uses to a
transportation simulation that is
accessible to everyone, mostly in
the area of public involvement.
Rather than having a consultant do
all the work and expecting some
tables and charts or maybe a 3D
model, agent-based traffic
simulation games could put
visualization in the hands of
citizens. If a consultant or an
agency handed out files of an
existing city, citizens could even
come up solutions to traffic problems, and get an idea firsthand as to whether their
idea would improve anything. Figure 10 shows some potential changes that could be
made, along with a significant impact to traffic. Unlike specialized software that
requires training in order to use it, computer games have fast learning curves and
offer instant gratification. If used in the public involvement phase of a project, they
would not have to be accurate—just accurate enough to start a discussion. Modelers
could then run the scenarios in professional tools to try for a more accurate
prediction.
Figure 9. Screenshot from Cities in Motion 2 showing a
vehicle traveling straight thru from and offramp to an
onramp, with plenty of capacity on the freeway.

21. Palacios 19
Figure 10. Screenshots showing changes to a highway in Cities in Motion 2. Besides some changes
upstream and downstream, the bottom photo adds dedicated bus lanes in both directions and a longer
two-lane on-ramp for the northbound (left) direction. The bottom photo was taken at night.
Conclusion
Modeling by nature is trying to predict the future. Sophisticated computer
algorithms definitely help. But between proper calibration and validation, ultimately
accurate modeling is more like an art than a science—it requires knowing how best

22. Palacios 20
to set a certain set of variables to match current conditions, a skill that comes with
practice and experience. It also requires accurate input data, or else it's little better
than wild guessing. When the input data itself is a prediction of what the
demographics of an area are expected to be, it becomes even more difficult to create
accurate forecasts.
Modelers have sought increased accuracy over the traditional four-step model, and
planners have realized the need for more flexibility than manuals like the ITE Trip
Generation Manual. Activity-Based modeling is a good step in that direction. But
utilizing traffic simulation games may add another distinct level of flexibility by
encouraging innovative ideas and collaboration with the general public.

23. Palacios 21
Bibliography
Bazzan, Ana L. C., and Franziska Klügl. “A Review on Agent-based Technology for
Traffic and Transportation.” The Knowledge Engineering Review FirstView
(2013): 1–29. doi:10.1017/S0269888913000118.
Calandra, Mike. “VMT Disaggregation Methodology.” presented at the
Transportation Modeling Forum, San Diego, June 2013.
http://www.sandag.org/uploads/publicationid/publicationid_1763_16133.p
df.
Cambridge Systematics, Inc., Vanasse Hangen Brustlin, Inc., Gallop Corporation,
Chandra R. Bhat, Shapiro Transportation Consulting, LLC, and
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http://escholarship.org/uc/item/86h7f5v0.

24. Palacios 22
Moore, Chase. “Chicago 1.0,” June 7, 2013.
http://steamcommunity.com/sharedfiles/filedetails/?id=151352135.
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http://www.trbappcon.org/2013conf/presentations/319_1_4_319_Southeas
t%20Florida%20ABM%20Transfer.pptx.
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Too.” Streetsblog Capitol Hill, June 16, 2011.
http://dc.streetsblog.org/2011/06/16/yale-poll-americans-support-
transpo-policies-to-avert-climate-change/.
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Statistics 6, no. 1 (2003): 1–12.
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Forum, San Diego, June 2013.
http://www.sandag.org/uploads/publicationid/publicationid_1763_16133.p
df.
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Game Developer’s Conference, San Francisco, March 7, 2012.
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