Creative Methods for Modeling Traffic Demand

John-Mark Palacios

Transportation and Supply Chain Systems

Dr. Evangelos Kaisar

26 July 2013

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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

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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

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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.

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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.

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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.

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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

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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.

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

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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.

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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

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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.

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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

Martin/Alexiou/Bryson, PLLC. Travel Demand Forecasting: Parameters and

Techniques. Washington, D.C.: National Cooperative Highway Research

Program, 2012.

http://onlinepubs.trb.org/onlinepubs/nchrp/nchrp_rpt_716.pdf.

Electronic Arts. SimCity Societies: Interview. Interview by Strategy Informer. Web.

Accessed July 26, 2013.

http://www.strategyinformer.com/pc/simcitysocieties/115/interview.html.

Ewing, R., M. Greenwald, M. Zhang, J. Walters, M. Feldman, R. Cervero, L. Frank, and J.

Thomas. “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.

Ewing, Reid, Eric Dumbaugh, and Mike Brown. “Internalizing Travel by Mixing Land

Uses: Study of Master-Planned Communities in South Florida.”

Transportation Research Record: Journal of the Transportation Research

Board 1780, no. -1 (January 1, 2001): 115–128. doi:10.3141/1780-11.

Li, Shi-Chiang. “RE: Activity Based Modeling,” June 6, 2013.

McNally, Michael G., and Craig Rindt. The Activity-Based Approach. Recent Work.

Irvine, CA: Institute of Transportation Studies, UC Irvine, November 17, 2008.

http://escholarship.org/uc/item/86h7f5v0.

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Moore, Chase. “Chicago 1.0,” June 7, 2013.

http://steamcommunity.com/sharedfiles/filedetails/?id=151352135.

Picado, Rosella. “A Test of Transferability: The SE Florida Activity-Based Model.”

presented at the TRB National Planning Applications Conference, Columbus,

Ohio, May 7, 2013.

http://www.trbappcon.org/2013conf/presentations/319_1_4_319_Southeas

t%20Florida%20ABM%20Transfer.pptx.

Schmitt, Angie. “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/.

Shoup, Donald C. “Truth in Transportation Planning.” Journal of Transportation and

Statistics 6, no. 1 (2003): 1–12.

Sun, Wu. “Activity-Based Model Update.” presented at the Transportation Modeling

Forum, San Diego, June 2013.

http://www.sandag.org/uploads/publicationid/publicationid_1763_16133.p

df.

Willmott, Andrew. “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.