microsimulation for urban transportation planning: miracle or mirage?

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Microsimulation for UrbanTransportation Planning:Miracle or Mirage?

Catherine T. Lawson

CITIES by their nature are multi-player, multi-activity,

complex weavings of people, materials, activities, and

infrastructures. In the past, only crude aggregate

models were available to understand their complex urban

transportation systems. However, an emerging technology—

microsimulation—offers a set of new tools that are well-suited

for transportation and other urban systems and are capable of

providing many benefits to a variety of users. Microsimulation is

a modeling technique that operates with individual units (i.e.,

persons, households, vehicles, or firms), providing a level of

analysis on a unit-by-unit basis. There are many variations of

microsimulation, including combinations of individual units and

time periods (i.e., persons in vehicles moving on a network on a

continuous basis or households paying taxes on an annual basis).

Over the last decade, experiments with high-performance

supercomputing clusters and advancements in computational

mathematics have expanded the realm of research and applications

in microsimulation models. As models that operate at the level of

the individual behavioral entity, any circumstance where individ-

ual entities act, interact, or react to anything in their environment

or to each other is a potential opportunity for microsimulation. For

example, O’Donoghue and Evans looked at reforming social

assistance in three European countries using a cross-national

Journal of Urban Technology, Volume 13, Number 1, pages 55–80.

Copyright # 2006 by The Society of Urban Technology.

All rights of reproduction in any form reserved.

ISSN: 1063-0732 paper=ISSN: 1466-1853 online

DOI: 10.1080=10630730600752868

Microsimulation for Urban Transportation Planning 55

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microsimulation model. Social science applications include labor

markets, tax policy, day care, and public pensions. Microsimula-

tion models are now being used in medical research, telecommu-

nication, and other disciplines. Advances being made in

microsimulation research in these diverse fields have value to

other seemingly unrelated users of the technology. The very

nature of this expansive field suggests the need for a “pan-

disciplinary” approach to research, where a researcher with little

or no interest in the inputs or outcomes of a particular area never-

theless learns lessons from the techniques developed and/or

applied in that area.

A pan-disciplinary world creates a challenge for the

exchange of ideas. Consider Lakshmanan and Anderson’s use of

the term “hyperarchy” to describe today’s world of rapid change

and interdependence, where team-based collaborations work

with permeable organizational boundaries. An example of this

phenomenon is the cross-fertilization of ideas in microsimulation

techniques used to “create” synthetic populations for research in

homeland security applications, based on previous work in trans-

portation planning. This article examines the challenges faced in

applying microsimulation to transportation planning and how

overcoming those challenges contributes to making further

advances in microsimulation in general.

Transportation Planning: A Case Study for the Introductionof Microsimulation

Transportation planning is currently undergoing a metamorphosis

from a traditional modeling process to microsimulation. The

passage of the Federal-Aid Highway Act of 1962 required urban

areas to use a comprehensive transportation planning process to

qualify for federal matching funds for the construction of transpor-

tation improvements. The invention of computers helped acceler-

ate a standardized approach to data collection and the development

of travel demand forecasting models. These models were designed

to help planners forecast where new highway capacity was needed,

given estimates of population and employment in some future

year. While these models have grown increasingly complex

since they were first devised, their underlying modeling

approaches have remained relatively unchanged.

The structure of the models requires urban areas to be broken

into zones. Census, traffic data, and survey data from these zones

Harding

Troitzsch et al.

Puvimanasinghe et al.

Habbema et al.

Edwards

56 Journal of Urban Technology/April 2006

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are aggregated into zonal averages. The aggregate models have

four separate components:

. trip generation (forecasts the number of trips leading to or

from each zone)

. trip distribution (matches origins with destinations)

. mode choice (predicts what travel modes will be used for

each trip)

. network assignment (estimates the resulting traffic volumes

on major arterials).

Although most metropolitan planning organizations in the

United States continue to use travel demand forecasting models

with this four-step structure, these models have long been criti-

cized for shortcomings that limit the validity of their results.

Trip GenerationEfforts to enhance and update these models remain an active

endeavor for transportation researchers, software vendors, and

planning professionals. For example, the trip-generation procedure

was designed to estimate trip origins and destinations as a linear

function of the characteristics of households. It has been enhanced

to incorporate time of day, characteristics of employers, and other

activity centers in each zone. However, the process still only pro-

vides zone-specific outputs, relying solely on average calculations

as proxies for behavioral responses. In some cases, survey and

census data are used to determine who travels and how far. Yet,

the models have no mechanism for incorporating changes in

trip-making practices when individual household members

respond to policy changes or household conditions, such as new

parking regulations or the use of zoning language that would

allow mixed-use commercial developments.

Trip DistributionIn the trip distribution process, the original versions of travel

demand forecasting models were based on the notion of a

gravity model. This refers to the concept of “attractiveness,”

where trips are distributed across all zones, based on how attrac-

tive one zone is in relation to all other zones. Attractiveness

encompasses employment and other descriptors of destinations.

Attractiveness generally increases as employment and other activi-

ties increase and declines sharply as distance and travel time

between zone pairs increases. Efforts at improving models


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include “matching” manufacturing workers to manufacturing jobs.

However, this technique, which still relies on zone-specific

outputs, is less effective in urban areas where the predominant

employment sites are not easily diagnosed.

Mode ChoiceModels used for mode choice traditionally divide trips into travel

by automobile or by transit. In this modeling process, the number

of trips and destinations are fixed and an estimation is made to

determine the percentage of trips carried by each mode.

Using an econometric technique known as a multi-nominal

logit model, recent attempts have been made to include a larger

variety of modes such as various forms of transit, as well as car-

pooling, bicycling, and walking. Although survey data is collected

from individual households (disaggregate data), the modeling

process continues to rely on a zonal structure which is not necess-

arily geographically scaled to incorporate information on user-

friendly infrastructures (i.e., bike paths and sidewalks).

Network AssignmentThe final process, the network assignment of trips, requires analysts

to develop computerized routines that, in most cases, look for the

shortest route from the origin to the destination (minimal path

program). The program runs iteratively until an “acceptable”

solution is found. This technique has often been referred to as

“the black box” component because of the level of software knowl-

edge needed to run and interpret the outputs. In the end, the outputs

of the models are compared with observed counts and specific ad

hoc adjustments made to “fix” the models. Although the algorithms

and computerized routines have increased in complexity to include

computerized proxies for congestion and to allow the assignments to

be changed to equalize traffic across the network, the models are still

not able to incorporate human responses to congestion or other

route-specific changes that influence driver behavior.

Although shortcomings have been recognized in technical

aspects of travel-demand forecasting models, they continue to

serve as an adequate methodology for providing planners with

notions on where new capacity needs to be built. Significant socio-

demographic changes have influenced household travel patterns

since the original models were developed including: an increase in

labor force participation by women, smaller household size, more

single-parent households, and an increased propensity to “trip

chain” (e.g., making multiple stops on the way to and from work).

Lawson 1998


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Concerns over the use of traditional travel demand forecast-

ing models increased after the passage of the Clean Air Act

Amendments and the Intermodal Surface Transportation Equity

Act. It became clear that the analytical requirements of the legis-

lation were far greater than the capability of the travel demand

forecasting model process. Conformity determination standards

explicitly required transportation planners to prepare long-range

demand forecasts and conduct air quality modeling to predict

absolute levels of pollution as a function of travel demand and

changes in the regional transportation system. Overall, the

federal legislation called for an urban technology capable of

including:

. land use and demographic characteristics of different regions

. the natural environment

. multi-modal transportation networks

. populations and commercial environments

. unique travel decisions of individuals

. detailed vehicle representations

. detailed dynamics of regional traffic

. the unique influence of traffic on congestion and air quality.

Developing Microsimulation Technologies for TransportationPlanningRecognizing the need to review and modernize travel models, the

U.S. Department of Transportation (DOT) and the U.S. Environ-

mental Protection Agency (EPA) sponsored a research program

focused on the preparation of, and experimentation with, micro-

simulation, referred to as the TRansportation ANalysis SIMulation

System (TRANSIMS). TRANSIMS was designed at the Los

Alamos National Laboratory by a team of researchers from a

variety of disciplines. The system was designed to simulate the

movements of individuals, vehicles, and households on a

second-by-second basis throughout the day. By modeling the

behavior of individuals in one-second “time-steps,” an entirely

new approach to transportation modeling was to be developed.

At a regional scale, TRANSIMS was intended to provide the capa-

bility to evaluate highly congested operations and overall effects

of infrastructure changes.

The decision to invest almost exclusively in a single

approach was not universally accepted, and some in the transpor-

tation research community would have preferred the funding to

Harvey and Deakin

Beimborn et al.

Morrison and Loose


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have been used as seed money for a variety of smaller model

improvement efforts, competitively awarded. Others were uncom-

fortable with the decision to license the experimental results to the

private sector at the conclusion of the experiment as the means of

promoting implementation.

The TRANSIMS system was originally built around four

modules: the population synthesizer, the activity generator, the

route planner, and the traffic microsimulator. Each of these

modules used a variety of inputs, implemented a series of processes,

and provided outputs that became inputs to the other modules. The

flexibility and interaction among the modules provided an opportu-

nity for feedback and internal consistency within a simulation

environment not previously attainable. The technology was to

make it possible to predict the effects of changing the transportation

infrastructure, using a scientific approach to traffic planning by

allowing the experiment to be precisely re-created.

The following is a brief description of the TRANSIMS

modules:

The population synthesizer is programmed to build a realistic

synthetic population for a region by “creating” the households and

matching them with census data. The flexibility of reassigning

these household characteristics means that any number of popu-

lation changes can be accommodated through mircrosimulation.

For example, the members of a household can “grow older”

over time—simulating the process of aging in place. The

purpose of the population synthesizer is to populate the modeling

process with the most realistic version of the traveling public—

rather than relying upon a set of average data (e.g., average

incomes) used in traditional travel demand forecasting.

Once the synthetic population is generated, the TRANSIMS

procedure produces a list of activities for each of the household

members using the activity generator. The sets of activities are

based on demographic and activity surveys collected from actual

households. The activity sets from the actual households are

matched to like-kind synthetic household members. Each individ-

ual household member is given a set of activities, matched by age,

gender, and relation within the household. Activity types (i.e.,

work, shopping, school, etc.), duration, beginning time and

ending time, and travel preference (i.e., driving or walking) are

assigned over a 24-hour period. The activity location is explicitly

established using geographic information systems (GIS)—thus the

time to go from one place to another and the way to go (mode

choice) can be calculated and tested for reasonableness.

Howard

Beckman et al.


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The route planner module contains the procedures used to

process the sets of activities, the locations, and the time available

for each activity. It then determines the feasibility of accomplish-

ing the expected travel patterns. The router contains network

layers, each assigned to a particular mode. There is a walk layer,

a street layer, a bus layer, and a rail layer. The computer proces-

sing moves the individual from one place to another by a combi-

nation of one or more of these mode layers. When it is

necessary to transfer between layers, the walk layer is used (e.g.,

transferring from bus to car requires use of the walk layer).

Specific time periods associated with each activity help determine

the feasibility of the mode of travel. For example, if the synthetic

householder needs to be at work by 9:00 AM and lives 20 miles

from the work location, walking would not be a feasible mode

of travel. If a bus is available and could travel the distance in

time, but has a schedule that makes the trip impossible to accom-

plish, only an auto can be used to make the work trip. Thus, every

set of activities, modes, and time constraints becomes part of the

process.

The traffic microsimulator executes the travel plans and com-

putes the overall intra- and inter-modal transportation system

dynamics. The module consists of the road network, vehicle

files, a transit network, a walking network, and traveler plans.

The Traffic Microsimulator has the ability to “navigate” an indi-

vidual from point to point on the transportation network using

an algorithm.

The computer program uses a set of rules that keeps the

vehicles from colliding, moving from one grid cell to another

using a cellular automata principle. However, the vehicles are

capable of creating a traffic jam. Throughout the experimental

phase, a number of studies were conducted to understand traffic

behavior using microsimulation technologies. (See Table 1.)

Early TRANSIMS ExperimentsDallas/Ft. Worth, Texas was the site of the first experiment using

TRANSIMS. A 25-square mile corridor was targeted for analysis,

with attention being paid to a discrete set of infrastructure changes,

primarily using the traffic microsimulator module. The experiment

began in 1995, used 10,000 links, and 200,000 synthetic travelers.

The study focused on a major shopping/business center, the

Galleria area, located near the interchange of the Lyndon

B. Johnson Freeway (I-635), and the Dallas North Tollway.

FHWA 2005


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TABLE 1Sample of Simulation Experiments Using TRANSIMS Technologies at Los Alamos National Laboratory

Author(s) Date Report ID Title Research Findings

Nagel & Schreckenberg 1995 LA-UR 95-2132 Traffic Jam Dynamics in StochasticCellular Automata

Cellular Automata (CA) models aretested with Los Alamos project andNorth-Rhine-Westfalia (NRW) inGermany

Rickert et al. 1995 LA-UR 95-4367 Two Lane Simulations Using CellularAutomata

In trying to simulate lane-changingbehaviors, some inconsistenciesappear in the model

Schreckenberg & Nagel 1995 LA-UR 95-2160 Physical Modeling of Traffic withStochastic Cellular Automata

Comparison of Cellular Automation(CA) to Follow-the-Leader theory forunderstanding cars in lanes

Nagel 1995 LA-UR 95-4018 Particle Hopping vs. Fluid-DynamicalModels for Traffic Flow

Compares Stochastic Traffic CellularAutomaton with other theories (i.e.,car-following and fluid dynamicalmodels)

Nagel et al. 1996b LA-UR 96-659 Network Traffic as a Self-OrganizedCritical Phenomena

Tests in NRW indicate paradox withtraffic management systems

Nagel et al. 1996a LA-UR 96-50 Parallel Traffic Microsimulation byCellular Automata and Applicationfor Large-Scale TransportationModeling

Discussion of computational issues,CA, multi-lane traffic, network andoverview of TRANSIMS

Kelley & Nagel 1997 LA-UR 97-4453 Relaxation Criteria for Iterated TrafficSimulations

TRANSIMS Dallas/Ft. Worth casestudy used to test concept of“relaxed” (vehicles moving out toarterials when freeway fills up duringpeak) network with three regimes: anuncongested network, an “unrelaxed”congested network, and a “relaxed”congested network

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Nagel & Barrett 1997 LA-UR-97-1334

Using Microsimulation Feedback forTrip Adaptation for Realistic Trafficin Dallas

TRANSIMS Dallas/Ft. Worth casestudy used to test re-routing of trips

Nagel et al. 1997 LA-UR 97-3531 TRANSIMS Traffic FlowCharacteristics

Discussion of “rules” of the modelcompared to TRANSIMS Dallas/Ft.Worth case study logic

Nagel et al. 1998a LA-UR 98-2944 Comparison between Three DifferentTraffic Micro-Simulations andReality in Dallas

TRANSIMS Dallas/Ft. Worth casestudy used to compare three micro-simulation methods

Nagel et al. 1998b LA-UR 98-312 Regional Transportation Simulations TRANSIMS Dallas/Ft. Worth casestudy used with trip tables to considerCPU operations

Williams et al. 1999 LA-UR 99-471 The TRANSIMS Approach to EmissionsEstimation

Discussion of use of ComprehensiveModal Emission Model (CMEM),with concerns over testing with CAdata rather than CA-like aggregationsof measure data

Nagel et al. 1999a LA-UR 98-4389 TRANSIMS for Urban Planning Discussion of TRANSIMS modules andcredibility; computing requirementsfor Portland experiment

Nagel et al. 1999b LA-UR 98-5628 TRANSIMS for TransportationPlanning

Discussion of TRANSIMS modules

Simon & Nagel 1999 LA-UR 98-3903 Simple Queuing Model Applied to theCity of Portland

Discussion of queue models; resultsfrom Portland and EMME/2

Barrett et al. 2001 LA-UR 01-4609 Actuated Signals in TRANSIMS Discussion of signal control algorithmsand application in Portland

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In the experiment, TRANSIMS used local interactions of

simulated vehicles on a second-by-second basis to generate the

speed of each vehicle. As a result, the speeds were sensitive to

signal timing, local traffic conditions, and other “real-world”

experiences. Each synthetic driver/vehicle was tracked and per-

formance measured for comparison with other synthetic drivers.

Rilett points out this major difference between traditional travel

demand forecasting and microsimulation. A stochastic,

microscopic-based model, such as TRANSIMS, is capable of

modeling individual demand responses to changes in supply. In

other words, a synthetic individual’s desire to participate in a

series of activities in different locations on a second-by-second

basis interacts with the ability of the transportation system to

meet this demand. The output from the microsimulation is rich

in information as every traveler and vehicle is modeled explicitly,

which affords model users a great variety of potential metrics. For

example, a primary measure of effectiveness (MOE) was devel-

oped that used a synthetic individual traveler’s time of travel

and average speed from the beginning to the end of her trip.

Other uses include an equity analysis, intended to show how

benefits and costs could be assigned to subgroups of travelers. A

reliability analysis was conducted that focused on the variability

of day-to-day travel times.

The Portland Metropolitan Region, Oregon was the site of the

second major experiment. The objective of the Portland study was

to demonstrate the full capability of TRANSIMS to analyze and

forecast traffic on a regional basis, rather than simply in a corridor.

In 1998, the Portland Metropolitan Planning Organization

(METRO) agreed to allow its transportation planning network

(EMME/2) to be transformed into the TRANSIMS format, the

“all-street” network. The new network included left-turn lanes,

road elevations, traffic signal locations, and other critical represen-

tations.

The goal of the TRANSIMS experiment in Portland was to

track individuals and vehicles on a second-by-second basis,

model the location and duration of activities, and track the location

of individuals as they use a regional transportation system. The

system was expanded to include transit and walking as modes of

travel.

The experiment is still on-going. The current study team is

composed of Portland METRO, AECOM Consult, and PB

Consult with support from the Los Alamos National Laboratory

and IBM. The team developed the following principles to guide

Beckman

Los Alamos National Laboratory

1998

Los Alamos National Laboratory

2002

Ducca


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its work and to facilitate future applications of the modeling

process:

1. Make maximum use of existing data.

2. Make maximum use of existing demand models.

3. Blend rule-based and behavioral techniques.

4. Incorporate feedback.

5. Represent the entire region.

6. Meet all performance standards of existing models and

provide additional measures.

Early in the study, the team decided to develop the model in two tracks.

Track 1.

To facilitate the operational aspects of a regional scale microsimu-

lation model, the team decided to use existing trip tables. Figure 1

illustrates how Track 1 uses these existing trip tables, combined

with the Router and Microsimulator, to create regional traffic

simulations. This allows potential users of TRANSIMS to gain

more information about regional traffic flows without needing to

convert to a complete activity-based model. Many of the benefits

of using a microsimulation platform for traffic are gained by using

this shortcut in the modeling process.

Track 2.

After setting up the router and microsimulator, the Track 2 process

moves towards a full-scale microsimulation operation. Figure 2

shows how Track 2 requires the construction of a complete

activity-based model, represented by the full diagram. Within

Track 2, travel demand elements include population synthesis,

activity times and locations, and mode preference. In Track 2,

FIGURE 1Conceptual Framework for the Portland Study Track 1


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the router and microsimulator represent network supply elements.

Additional advances can be accommodated in these two modules.

The Track 2 process consists of the following steps:

1. Synthesize a regional population.

2. Forecast activity times and locations.

3. Determine the modes for accessing activities.

4. Route tours through the system.

5. Simulate the results.

6. Feedback the results of the simulation to the other model

components.

According to Pederson, the TRANSIMS original concept of a

complete street network was impossible to achieve, so the research

team decided to use the existing, and considerably simpler, metro-

politan planning level transportation network. The level of detail

was reduced from 124,900 links to 11,900; from 246,900 activity

locations to 20,900; from 30,400 lane miles to 8,100; and from

1,750 traffic signals to 1,370. Rather than actually using zone-to-

zone level geographies from typical travel demand models, or

the highly detailed door-to-door addresses, the Portland team

used aggregates of activity sites that form large blocks. Thus,

the assembly of data to support the model is more intensive than

traditional travel demand models, but not as detailed as originally

expected. Some additional work was needed to add pocket lanes,

parking lots, lane connectivity, and signal and sign warrants.

Transit services were also added to the coding details, including

bus stops, rail lines, waiting areas, and rail stations.

FIGURE 2Conceptual Framework for the Portland Study Track 2


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Figure 3 illustrates the steps to move from a traditional travel

demand forecasting model to a microsimulation process that uses

second-by-second time-steps. For example, after the process data

is assembled to support the modeling effort, dynamic traffic

assignment (DTA) is used to route trips by time of day. The tran-

sition allows planners to use Track 1 and/or Track 2 deployment

strategies. Within the transition framework, individual trips on the

system become tours for individual activity patterns.

Once the tours are generated, the microsimulated traffic volumes

are compared to archived intelligent transportation systems (ITS),

traffic counters, and to the original router demand for trips on a par-

ticular link by time of day, by direction of travel, and by speed.

The visualizations created using geographic information

systems (GIS) and automated graphics provide insights into the

location and duration of congestion and other traffic phenomenon.

Work continues on the Track 2 process to accurately depict

location choice. The research team is incorporating a tour-based

location choice model in place of the traditional trip-based destina-

tion choice model. Many interesting modeling insights are being

gained through the systematic application of microsimulation tech-

niques. For example, elements in the feedback process and the com-

plexity of the decision-making process must be addressed to actually

propel synthetic individuals through a series of daily activities.

FIGURE 3Transition Path for TRANSIMS


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Additional TRANSIMS ExperimentsEarly in its development, TRANSIMS was licensed to universities

for research purposes. Thirty-one copies of version 1.0 and 1.1 and

one copy of version 3.1 were licensed. No consistent attempt was

made to follow up on the use of the licenses. Published results indi-

cate that versions 1.0 and 1.1, while useful for research purposes,

were premature for large-scale applications. Examples of this early

research include an evaluation by the Center for Engineering

Studies at North Carolina State University in Raleigh, experiments

by Rilett et al., and by Kim and Rilett.

Hobeika has developed a tutorial package to teach trans-

portation planning using microsimulation tools. He is also conduct-

ing a small-scale microsimulation demonstration using the

Blacksburg, Virginia network and demographic data as a case

study. Dixon and Chang have received funding for a project using

TRANSIMS to model the entire city of Moscow, Idaho focusing

on a demonstration of software and curriculum enhancements.

Two additional TRANSIMS projects were undertaken in late

Spring 2005. The Federal Highway Administration (FHWA)

began using TRANSIMS to study the impact of street closings

near the White House, and Rutgers University, along with the

New Jersey Institute of Technology, began a study of traffic in

New Jersey’s Middlesex and Monmouth Counties. No results

are available yet from these studies.

FHWA plans call for the continued development and deploy-

ment of TRANSIMS technologies. This includes continuing the

Track 2 research, developing additional applications, providing

training on its use, and supporting metropolitan planning organiz-

ations (MPOs) and state departments of transportation (DOTs) on

their implementation strategies. As part of the deployment of

TRANSIMS, the FHWA has begun distribution on an “open

source”1 basis. Further information on the open source version

can be found at tmip.fhwa.dot.gov/TRANSIMS/. Teaching

materials on the theoretical and practical aspects of TRANSIMS

are also available at this site. The recently enacted legislation—

the Safe, Accountable, Flexible, Efficient Transportation Equity

Act: A Legacy for Users—contains a provision for the advance-

ment of the TRANSIMS program, including studies intended to

focus on freight, airport, and port activities.

A group of researchers have conducted a series of studies in

Switzerland, using TRANSIMS version 1.1. Research was also

conducted to demonstrate a microsimulation methodology for

pedestrian travel.

Stone et al.

Virginia Polytechnic and State

University

Ducca

1Open source is a term used to

describe the concept of allowing

immediate and open access to the

sources of information and data

used in the creation and

organization of knowledge. This

includes the source code used in

software applications.

SAFETEA-LU

Raney et al. 2002

Balmer et al. 2004

Raney et al. 2003

Nagel and Raney

Gloor et al. 2003

Gloor et al. 2004


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Other Applications of Microsimulation in TransportationPlanningSome transportation researchers describe microsimulation as a

natural extension of the disaggregate modeling technique used in

traditional travel demand forecasting models, rather than an alterna-

tive approach. Microsimulation can be used to generate discrete

choices of individuals. The New York Metropolitan Transportation

Council (NYMTC) is developing a microsimulation model for the

New York-New Jersey-Connecticut metropolitan area using this

concept. The modeling process is referred to as the New York

Best Practices Model (NYBPM) and is run using a propriety soft-

ware application. The research team claims the following advan-

tages when using a discrete-choice microsimulation application:

. substantial savings in terms of calculations and storage of

model outputs

. explicit formulation of chained decisions and time-space

constraints faced by individual travelers

. explicit modeling variability of travel demand rather than the

traditional use of average values

. ability to handle competition for various locations, including

work places.

The system under development has two structures: the household,

auto-ownership, and journey-frequency (HAJ) model and the

mode, destination, and stop choice (MDSC) model. The HAJ

model includes the following elements:

Household Model – forecasts the number and distribution of house-

holds in designated zones, based on census data. The model calcu-

lates the probability of possible combinations of household

characteristics (i.e., income, size, number of workers and non-

workers, number of children). The probabilities are combined

with traditional aggregate demographic forecasts for future years.

Auto-Ownership Model – determines the number of automobiles

available for each household established in the household

model, using household characteristics, vehicle-maintenance

costs, parking availability, transit and highway accessibility,

density and residential nature of the household location.

Journey-Frequency Model – determines the daily number of

paired journeys taken by each person in each household for a set

of purposes, considering intra-household interrelationships,

access to transit, and auto availability.

Vovsha et al.

NYMTC 2006


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The MDSC has the following five submodels:

Pre-Mode Choice Model – uses household characteristics to

distinguish between motorized and non-motorized travel.

Destination Choice Model – based on the previous sorting in the

pre-mode choice, different destination sets with available

attractions for each zone are used to distribute journeys.

Motorized Mode Choice Model – predicts traveler decisions based

on time and cost factors for nine motorized modes.

Stop-Frequency Choice Model – uses four combinations of beha-

viors including: direct journeys without stops; stops on the way to

a destination; stops on the way back from a destination; or stops on

both the way to and the way back from a destination.

Stop-Location Choice Model – predicts a location zone for each

stop, given a set of distance limits that substantially reduce the

choice-set (e.g., a 3- to 5-mile radius around a residential location).

A technique referred to as a Monte Carlo random pick process

is used to generate the microsimulated travel patterns. According to

Vovsha, systematic variation in travel activities (i.e., season, time of

day, particular events) is incorporated in the probability model

structures (the HAJ and MDSC series), while the Monte Carlo

process variation accounts for the non-modeled random factors. In

this microsimulation application, individuals are modeled as the

unit of analysis, however, the modeling techniques are based on

the traditional travel demand forecasting methodology. The core

model uses a 30-minute time-step for the destination and mode

choice procedures; however, these outputs are ultimately aggre-

gated into four time periods (6:00 AM – 10:00 AM; 10:00 AM –

4:00 PM; 4:00 PM – 8:00 PM; and 8:00 PM – 6:00 AM).

Another microsimulation model has been under development

since 2000 by Cambridge Systematics and Parsons Brinckerhoff

for the San Francisco County Transportation Authority. The

model uses a “full-day pattern” of activities that simultaneously

predicts the main components of each person’s travel. A random

Monte Carlo procedure selects a single pattern for each syn-

thesized person. The synthesized tours and trips are aggregated

to represent flows between zones before traffic assignment. The

model is described as a tour-based microsimulation approach to

modeling destination choice and mode choice. According to

Jonnalagadda et al., this approach differs from traditional travel

demand forecasting models because trip productions are

Vovsha et al.

Vovsha 2006

Freedman et al.

Castiglione et al.


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determined using day-pattern models. The tour-level models used

to determine the primary destination and trip-level models capture

the choice of intermediate stops on a particular tour. The model

uses a decision tree process that explicitly illustrates a series of

travel choices. The model reduces 1,728 zones to forty alternative

potential locations for destination choices. The entire process is

modeled as a disaggregate process in a microsimulation frame-

work. The time-steps are designated as early AM, AM peak,

midday, PM peak, and evening.

The San Francisco model has been used to evaluate a pro-

posed New Central Subway project in downtown San Francisco

in the prescribed manner outlined in the Federal Transit Adminis-

tration (FTA) “New Starts” program. Freedman et al. point out the

challenges of using a microsimulation modeling process in the fra-

mework required for this program. Another application of the San

Francisco model demonstrated its utility for equity analysis for

“communities of concern.” The use of a “disaggregate” structure

made it possible to extract detailed, person-level statistics on

travel with respect to income, marital status, and auto availability

at the household level. These are key components for studying

low-income, single-person headed, and zero-auto households.

Although ethnicity and race are available in the underlying

census data, these characteristics have not yet been incorporated

into the synthesized population used in the microsimulation

model. The model output provides information on mobility and

accessibility benefits from changes in transportation services for

members of various groups in San Francisco.

The Mid-Ohio Regional Planning Commission (MORPC) is

developing an activity/tour-based model applied incorporating

microsimulation. The modeling system was completed in late

2004 and is being used for conformity analysis, transit alternative

analysis, and highway project planning. The model consists of

nine separate linked models, beginning with a model that gener-

ates a synthesized list of household members. The second model

is an auto ownership model. The third model determines the

daily activity pattern for each synthetic person. The fourth

model determines joint travel among household members. The

fifth model generates non-mandatory tours. The sixth, seventh,

and eighth models are used together for tour destination choice,

time of day choice, and tour mode choice models. The time of

day choice model allows for a one-hour temporal resolution

between 5:00 AM and 11:00 PM. The final model determines the

stops and trip-mode choice elements.

Castiglione et al.

Castiglione et al.

Anderson and Donnelly

Vovsha 2005


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The research team describes their process as being a microsi-

mulation application for individuals, using a Monte Carlo

technique with a random number series. However, the population

synthesizer and the auto ownership models perform the microsi-

mulation process using a deterministic “discretizer” procedure to

avoid the variability associated with the Monte Carlo technique.

Table 2 compares the basic elements of the four microsimula-

tion models currently under development. All of the models use

some form of the census at the PUMS level for determining the

characteristics of a synthetic population. Only the TRANSIMS in

Portland, Oregon uses a microsimulation process for traffic

assignment, providing a level of transparency for tracing the

travel patterns of individual household members. This process for

generating individual vehicles is similar to a number of small-

area microsimulation applications, such as INTEGRATION,

CORSIM, and/or PARAMICS. However, the major difference is

development of the traveler characteristics and behaviors associated

with each vehicle on the system in the Portland model.

Another emerging of microsimulation technique incorporates

archived real-time traffic data. For example, researchers at Massa-

chusetts Institute of Technology (MIT) continue to work on their

program, DynaMIT—capable of incorporating archived Intelli-

gent Transportation Systems (ITS) data into a microsimulation

traffic model. (See http://mit.edu/its/dynamit.html.)

According to Barcelo, European concerns over the need to pre-

serve historic city centers and the growing movement of goods has

spawned interest in microsimulation for freight models. The models

are used by planners for evaluating potential locations for city logis-

tics schemes and when consulting with commercial firms that are

keenly concerned about the viability of the analysis.

Barriers and Benefits: Finding a Balance

Any concerted effort to move microsimulation from purely an

experimental exercise to an urban technology used in normal

transportation planning practice faces a number of barriers. For

example, institutional inertia blocks many attempts to “retool”

operations due to the series of decisions and levels of approval

necessary to make changes in the status quo. However, organiz-

ations are now gaining experience with managing “rapid

change” based on their own Information Technology (IT) staff rec-

ommendations. The constant set of software and hardware


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TABLE 2Comparison of Microsimulation Models Currently in Use in the United States

Model Population Travel Demand Assignment to the Network

Portland, Oregon/METRO TRANSIMS

Creates synthetic personfor each householdmember based on PUMSand is tracked individuallythroughout system

Tours for each household member are based onactivity patterns (location and time range), thentravel plans are built with specific activity timeswith the time and path of each travel leg betweeneach activity location.

Second-by-second, agent-basedmovement on a multi-layered grid usingrouter to build paths through thenetwork. Tracks the movement of bothtravelers and vehicles. Simulates themovement of vehicles on the network.

New York MetropolitanTransportation CouncilBest Practices Model

Uses census data tocalculate probabilities forhousehold types in eachzone

Journey frequency is determined by the dailynumber of paired journeys (outbound and inboundtrip pairs) for each person for each purpose in eachhousehold, given the estimated number of autosper household and is then used with mode,destination, and stop-choice model.

Batch file computer runs using time binsfor highway and transit in four timeperiods.

San Francisco Model Creates synthetic“resident” from PUMS,based on estimatedpopulation distribution. Aseparate visitor model isused for non-residents.

Tour-level based on full-day patterns, includingintermediate trips. Random Monte Carloprocedure is used to select a single pattern.

Aggregated to zones prior to assignmentto network in five time periods.

Mid-Ohio RegionalPlanning CommissionTravel ForecastingModel

Creates a synthesized listof all households in theregion using PUMSperson for each household

Daily activity patterns used to develop tours,using joint choice of departure time from home,arrival time back home, and total durationincluding activity and travel with a temporalresolution of one hour between 5:00 AM and11:00 PM.

Uses traditional trip table assignmentsto highway and transit networks.

Micro

simu

lation

for

Urb

anT

ransp

ortatio

nP

lann

ing

73

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upgrades required for word processing and e-mail communi-

cations, both assumed fundamental to routine operations, sets

the stage for more aggressive changes related to the computing

environment.

Another concern is the added costs, both for data collection

and model maintenance. The original electronic map (shapefile)

constructed to the Portland TRANSIMS experiment has an esti-

mated value of $250,000. This dataset includes left-turn

lanes, traffic signals, and road elevations for the entire metropoli-

tan region. The current research team is using the transportation

network used for traditional travel demand forecasting, rather

than the highly-detailed shapefile. It is unclear whether the

entire network (including roads rarely traveled) needs to be

modeled. It may be possible to invest more heavily only in those

areas where traffic is heaviest. It is also conceivable that the elec-

tronic datasets currently being used in the design and construction

of road segments could be transformed electronically into a data

form that could be used for microsimulation. Other emerging

cost-saving data “harvesting” strategies include: using global

positioning systems (GPS), a variety of archived Intelligent Trans-

portation Systems (ITS) data, using activity survey samples from

similarly situated areas, and accessing other administrative

datasets for validation or “seed” information for microsimulation.

The maintenance costs of the modeling system are tied to the

cost of workforce training and the retraining of staff. New teaching

tools available on the Internet could reduce these costs consider-

ably, while ensuring a constant source for information on improve-

ments in the software processes. While hardware costs continue to

decline, there remain some issues with the operating systems and

software requirements.

Additional research may reveal an opportunity to use

“hybrid” models that incorporate elements of traditional travel

demand forecasting models (aggregate models), archived ITS

data, and “hot spot” microsimulations—all in a regional or corri-

dor context, using synthetic households and activity data as a foun-

dation. Strategically targeted surveys could be used to acquire

information from areas or individuals not well understood. This

strategy could accommodate the development of economic beha-

vior models, such as home production or other models currently

under development in Europe2.

For those concerned with the compatibility of microsimula-

tion outputs with current federal regulations and/or programs

(e.g., air quality compliance regulations or the FTA’s New

Lawson 1998

2See European Council on

Modelling and Simulation for

additional information available

at http://www.scs-europe.net/

info/index-a.html.


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Starts program), the move towards open source software (OSS)

applications is perhaps the most exciting and productive means

of addressing such issues. Okunieff and Neuerburg, however,

point out there are still risks associated with OSS including:

code fragmentation, lack of compatible/interoperable appli-

cations, version control, inability to build a viable community of

developers, and/or costs of training staff for support and service.

Some of these risks could be mitigated through partnerships

between planning agencies and local universities and/or the estab-

lishment of other non-profit information organizations.

The use of OSS also offers the opportunity for those using

microsimulation in a broad range of unrelated applications (e.g.,

biological processes, cell tower operations, etc.) to contribute to

software development strategies. In the same fashion as Lakshma-

nan and Anderson’s hyperarchy, ideas and techniques could

migrate into use at a rapid pace. OSS is uniquely suited for evalu-

ation and review by other contributors not available through con-

ventional in-house development or vendor products. As a result, it

may be possible to disseminate many new functions and oper-

ations to all state and local transportation departments and

agencies in the near term. Advances made on land use and other

urban microsimulation systems can also be incorporated into

emerging models in a manner more likely to capture the true

nature of relationships in an urban setting. The use of temporal

aggregation techniques holds the promise of allowing for the

analysis of change, whether in seconds (e.g., important to staff

in traffic management centers) to longer term changes for

periods used more commonly by the planning community (e.g.,

20-year horizons).

Other professionals in the urban arena, including real estate,

construction, and development community members who have

long recognized the value of information, the inherent risks of

the market place, and the need for improved accuracy in regional

and local forecasts have had little opportunity to work closely with

the transportation community in the implementation of microsi-

mulation models. While there have been previous research

attempts to use microsimulation for land-use purposes, the contin-

ued use of aggregate travel demand forecasting models and little

understanding of the latent demand for travel reduce their viability

for widespread use.

Most of the microsimulation models share a common pro-

cedure for creating a synthetic population from census data.

Only the original TRANSIMS models and computer science

Waddell


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extensions (i.e., MATSIM) appear to be using the vast capacity of

an agent-based, decision-making framework and second-by-

second time-steps. Some emerging research suggests decision

making encompasses factors not incorporated in either of these

approaches, including human behavior motivators of altruism or

the self-serving nature of personal decision making. The opportu-

nity for members of various research communities to make great

strides in the near future is being enhanced with the development

of open source tools, new partnerships and pan-disciplinary

exchanges.

Conclusions

The TRANSIMS experiments, while monumental in their forward

thinking strategies, are only the beginning of what needs to be

done to fully exploit the advantages of microsimulation for

urban research. Practicing planners are often constrained by

local decision makers and resource allocations. It is far more

likely that partnerships with universities capable of conducting

experiments will make the transition in the near future. Univer-

sities are able to promote open source options and provide linkages

to a larger community of researchers to share lessons learned the

“hard way.” Commercial firms may be less inclined to share

these lessons or even to attempt more risky ventures (i.e., explor-

ing decision-making patterns) that may or may not have a ready

market in the early stages of development.

More consideration for open source software development

and creative partnerships will provide benefits to the greater com-

munity of researchers while at the same time meeting the current

needs for understanding urban transportation systems. In the end,

for the transportation planning community, only a thorough and

systematic evaluation process can answer the question of

whether the costs and benefits associated with microsimulation

models justify a transition from the current use of traditional

travel demand forecasting models. In other words, is microsimula-

tion a miracle or a mirage? For the broader community of

researchers who are exploring new technologies using a pan-

disciplinary approach, progress will continue and perhaps

accelerate with ever-expanding electronic communications and

opportunities for the cross-fertilization of ideas. It remains to be

seen whether the transportation community gets “on board” in

time to benefit from this surge in research activity.

Balmer et al. 2006

Goulias and Henson


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microsimulation for urban transportation planning: miracle or mirage?

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