Bowman, Ph. D.
Transportation Systems and Decision Sciences
Bowman Research and Consulting
28 Beals Street
activity-based travel simulator
model estimation software
Bowman conducts planning and design
studies for agencies considering the implementation or
improvement of activity-based forecasting models. Such a
study can help you clarify your requirements, identify good ways
of satisfying them, and understand what it will take to do
so. Bowman has conducted studies for the Southern
California Association of Governments (SCAG), the Tampa Bay
region of Florida, and the Danish Road Directorate
(Vejdirektoratet). Here are the reports from those studies
Tampa Bay region (2008): Activity-based model design and workplan
SCAG (2009): Strategy for Activity-Based Travel Demand Model Development with Travel Survey
Danish Road Directorate (Vejdirektoratet) (2014): Requirements and designs for incorporating bicycling into the Copenhagen DaySim Activity-based model.
Bowman conducts development projects
to implement activity-based models on the DaySim software
platform for the client's geographical region. He will
serve as prime contractor, working in partnership with your
technical model developers and any subcontractors as
needed. Or he can serve as a subcontractor to a larger
consulting firm. In either case, he usually provides the
model design services, implements customization and new features
in the DaySim software, and estimates models. In some
cases he also manages the project, processes the input data for
model development, and technically guides the other aspects of
the project. In all cases, he coordinates the DaySim
software implementation with RSG, Inc, which shares copyright of
the DaySim software with him and houses the DaySim code
repository encompassing all DaySim implementations.
One of Bowman's specialties is
researching new model needs that you may have as his
activity-based model client, and then designing model solutions
and incorporating them into DaySim. This is usually done
in conjunction with RSG's Mark Bradley and software
engineers. Examples of DaySim functionality that
Bowman has incorporated in this way include:
--park and ride mode with lot capacity constraints and fill levels that vary by time of day. (Sacramento)
--partially joint half tours (e.g., when a parent drops off a child at school on the way to work.) (Seattle and Copenhagen)
--transit and bicycle mode combinations, including bike-park-ride-walk, bike-park-ride-bike, and bike-on-board (Copenhagen)
Enhancements currently under consideration include:
--automated base year calibration and future year pivoting within DaySim using base year trip matrices
--incorporation of shared mobility services (car share or bike share)
--incorporation of autonomous vehicles
Bowman can provide instruction in
all aspects of activity-based models. This can take the
form of one or more presentations and/or discussions with an
executive, a technician, or a group. The content can be
customized to meet your needs, including such topics as needs
and benefits, data and staff requirements, how to go about a
model development project, technical workings of the AB model,
or model development methods.
As a model estimation coach, Bowman guides and reviews the discrete choice model estimation of clients who want to do their own model development but also want to benefit from his technical knowledge and skills in econometric discrete choice modeling. These services can be provided as part of a major model development contract, or under a retainer or on-call contract. Past clients include Denver Regional Council of Governments, Puget Sound Regional Council and Danish Road Directorate.
Bowman can study, understand and
critique the detailed technical workings of a travel demand
model and, optionally, estimate the uncertainty and bias in the
model's forecasts. The purpose of such evaluations might
be to identify needed model improvements or to satisfy the
requirements of investors. Bowman performed this type of
analysis for potential investors in a major Asian city's
elevated rail public transport system.
Bowman serves as a prime or subcontractor on sponsored research projects related to activity-based models and/or non-motorized transportation. As a subcontractor he can take a major role, or he can bring his technical expertise to bear in an advisory capacity.
Bowman, John L. (1998) The day activity schedule approach to travel demand analysis, Ph.D. Dissertation, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA. 185 pages.
Further develops the activity schedule model (see 1995 Thesis), emphasizing (a) the influence of activity accessibility on activity participation, at-home vs on-tour decisions, trip chaining and inter-tour trade-offs, and (b) the influence of lifestyle on activity and activity pattern utility. Includes an empirical implementation of the model system for Portland, Oregon.
Bowman, John. L. (1995) Activity based travel demand model system with daily activity schedules, Master of Science Thesis in Transportation, Massachusetts Institute of Technology, 92 pages.
Presents an integrated activity based discrete choice model system of an individual's daily activity and travel schedule, intended for use in forecasting urban passenger travel demand. The system is demonstrated using data from the Boston metropolitan area.
Vuk, Goran, John L. Bowman, Andrew Daly and Stephane Hess (2015) Impact of family in-home quality time on person travel demand, Accepted for publication in Transportation.
Introduces the concept of Primary Family Priority Time (PFPT), which represents a high priority household decision to spend time together for in-home activities. PFPT is incorporated into a fully specified and operational activity based (AB) discrete choice model system for Copenhagen, called COMPAS, using the DaySim software platform. Structural tests and estimation results identify two important findings. First, PFPT has a place high in the model hierarchy, and second, strong interactions exist between PFPT and the other day level activity components of the model system. Forecasts are generated for a road pricing and congestion scenario by COMPAS and a comparison version of the model system that excludes PFPT. COMPAS with PFPT exhibits less mode changing and time-of-day shifting in response to pricing and congestion than the comparison version.
Bradley, Mark, John L. Bowman and Bruce
Griesenbeck (2010) SACSIM: An
applied activity-based model system with fine-level spatial
and temporal resolution, Journal of Choice Modeling
Vol. 3 No. 1: 5-31, 2010. Also available at http://www.sciencedirect.com/science/journal/17555345/3/1
Presents the regional travel forecasting model system (SACSIM) being used by the Sacramento (California) Area Council of Governments (SACOG). The paper explains the model system structure and components, the integration with the traffic assignment model, calibration and validation, sensitivity tests, model application and Federal peer review results.
Dong, Xiaojing, Moshe E. Ben-Akiva, John L. Bowman and Joan Walker (2006) Moving from Trip-Based to Activity-Based Measures of Accessibility, Transportation Research Part A, 40(2006), pages 163-180.
Studies the properties and performance of an accessibility measure derived from the Day Activity Schedule (DAS) model system, comparing it with traditional trip-based measures, including isochrones, gravity-based measures and simpler utility-based measures.
Bowman, J. L. and M. E. Ben-Akiva (2001) Activity-based disaggregate travel demand model system with activity schedules, Transportation Research Part A, 35(2001), pages 1-28.
A refined and shortened version of Bowman's Master's thesis (see above).
Bowman, John L., Mark A. Bradley, Yoram Shiftan, T. Keith Lawton and Moshe E. Ben-Akiva (1998) Demonstration of an activity based model system for Portland, 8th World Conference on Transport Research, July 12-17, 1998, Antwerp, Belgium.
Reports the first operational implementation, in Portland, Oregon, of the activity-based travel demand model system proposed in 1994 by Ben-Akiva, Bowman and Gopinath.
Ben-Akiva, Moshe E., and John L. Bowman (1998) Activity based travel demand model systems, in Equilibrium and Advanced Transportation Modeling, P Marcotte and S Nguyen, ed., Kluwer Academic Publishers, 20 pages.
Traces the evolution of disaggregate discrete choice travel demand models toward an activity basis.
Ben-Akiva, Moshe E. and John L. Bowman (1998) Integration of an activity-based model system and a residential location model, Urban Studies, 35(7), pp. 1231-1253.
Presents an integrated discrete choice model system of a household’s residential location choice and its members’ activity and travel schedules.
Ben-Akiva, Moshe., John L. Bowman and Dinesh Gopinath. (1996) Travel demand model system for the information era, Transportation(23), pp. 241-266.
Proposes a comprehensive travel demand modeling framework to identify and model the urban development decisions of firms and developers and the mobility, activity and travel decisions of individuals and households.
Abstract (pdf) Pre-publication draft of paper (not currently available)
Bowman, John L. (2014) Incorporating Bicycling into Activity-based Regional Travel Forecasting Models in Denmark: Identified Needs and Proposed Solutions, Report prepared for Danish Road Directorate (Vejdirektoratet).
Bowman, John L. Mark Bradley
and Joe Castiglione (2013) Making
advanced travel forecasting models affordable through model
transferability, FHWA Report.
Joe, Brian Grady, John L. Bowman, Mark Bradley and Stephen Lawe
an Integrated Activity-Based and Dynamic Network Assignment
Model, Submitted for presentation at the 3rd
Transportation Research Board Conference on Innovations in
Travel Modeling, May 9-12, 2010, Tempe, Arizona, USA.
L. (2009) Historical
Development of Activity Based Model Theory and Practice,
Traffic Engineering and Control, Vol. 50 No. 2: 59-62 (part 1),
Vol. 50 No. 7: 314-318 (part 2).
Bradley, Mark, John L. Bowman and
Bruce Griesenbeck (2009) Activity-Based
for a medium sized city: Sacramento, Traffic
Engineering and Control, Vol. 50 No. 2: 73-79.
Bowman, John L. (2009) Population Synthesizers, Traffic Engineering and Control, Vol. 49 No. 9: 342.
A brief note explaining population
Bowman, John L. and Mark A. Bradley (2008) Activity-Based Models: Approaches used to achieve integration among trips and tours throughout the day, presented at the 2008 European Transport Conference, Leeuwenhorst, The Netherlands, October, 2008.
Compares various integration techniques used by four activity-based models that have been used for travel forecasting in the US, providing conceptual understanding and reasoned discussion of their strengths and weaknesses..
Bradley, Mark.A., John L. Bowman and Bruce Griesenbeck (2007) Development and application of the SACSIM activity-based model system, presented at the 11th World Conference on Transport Research, Berkeley, California, USA, June, 2007.
A condensed version of the 2005 and 2006 ETC papers, with an additional section on application issues.
Bowman, John L., Mark A. Bradley and John Gibb (2006) The Sacramento Activity-Based Travel Demand Model: Estimation And Validation Results, presented at the European Transport Conference, September 18-20, 2006, Strasbourg, France.
A sequel to the 2005 ETC SACOG paper, this paper focuses on several aspects of the model system, including the time-of-day models, equilibration of demand and assignment, base year calibration, and sensitivity tests.
Bowman, John L. and Mark A. Bradley (2006) Upward Integration of Hierarchical Activity-based Models, working paper.
Discusses the importance and difficulty of achieving upward vertical integration in activity based models, and a few techniques used by Bowman and Bradley to achieve it.
Bowman, J.L. and G. Rousseau (2006) Validation of the Atlanta (ARC) Population Synthesizer (PopSyn), white paper presented at the TRB Conference on Innovations in Travel Modeling, May 21-23, 2006, Austin, Texas.
Presents the results of initial base year and backcast validation of the ARC Population Synthesizer (PopSyn).
Bradley, Mark A. and John L. Bowman (2006) A Summary of Design Features of Activity-Based Microsimulation Models for U.S. MPOs, white paper presented at the TRB Conference on Innovations in Travel Demand Modeling, May 21-23, 2006, Austin, Texas.
This short paper provides a concise summary of important design features of various activity-based model systems that had been implemented or recently designed as of May, 2006, for planning agencies in the U.S. The models described are those for Portland, San Francisco, New York, Columbus, Atlanta, Sacramento, Bay Area, and Denver.
Bowman, John L. and Mark A. Bradley (2005) Disaggregate treatment of purpose, time of day and location in an activity-based regional travel forecasting model, European Transport Conference, October 2005, Strasbourg, France.
Presents model system design, data, and partial estimation results of the activity based regional travel forecasting model system for the Sacramento (California) Area Council of Governments (SACOG), as it stood while under development in September, 2005. Emphasis is placed on the techniques employed for effectively disaggregating the treatment of purpose, time and space.
Bowman, John L. (2004) A comparison of population synthesizers used in microsimulation models of activity and travel demand, working paper.
Microsimulation models that forecast the activities and travel of urban populations create synthetic populations and then use them to simulate the behavior of the households and persons in that synthetic population. The features of eight population synthesizers are compared, and suggestions are made for incorporating the best features into future population synthesizers.
Vovsha, Peter, Mark A. Bradley and John L. Bowman (2004) Activity-based travel forecasting models in the United States: Progress since 1995 and Prospects for the Future , presentation at the EIRASS Conference on Progress in Activity-Based Analysis, May 28-31, 2004, Vaeshartelt Castle, Maastricht, The Netherlands.
Describes activity-based travel forecasting model systems implemented or under development in Portland, San Francisco, New York, Columbus and Atlanta, explaining attempts to incorporate behavioral realism, discussing issues that interfere with their acceptance in practice, and suggesting a research agenda relevant to implementation of practical activity-based models.
Bowman, John L. (2003) Logit kernel (or mixed logit) models for large multidimensional choice problems: identification and estimation, presented at the European Transport Conference, October 3-5, 2005, Strasbourg, France, 2005, and at Transportation Research Board Annual Meeting, Washington, D.C., January, 2004.
Presents an identification rule and
insights for estimation of the class of error component logit
kernel models. These are logit
kernel (or mixed logit) models involving heteroscedasticity and
subsets of alternatives with shared unobserved attributes. A case study demonstrates the
specification, identification and estimation of the type of
model for which EC is useful—one with large choice set and a
choice outcome consisting of two or more variables considered
Bowman, John L., Dinesh Gopinath and Moshe Ben-Akiva (2002) Estimating the probability distribution of a travel demand forecast, working paper.
Presents a practical method for estimating the probability distribution of a travel demand forecast. Given a forecast of any variable of interest, such as revenue or ridership, the approach identifies independent sources of uncertainty, estimates a probability distribution of each source, estimates the sensitivity of the variable to each source, and then combines the effects. A case study is presented in which the probability distribution of a revenue forecast is developed for a new transit system.
Bradley, Mark A. , John L. Bowman and T. Keith Lawton (1999) A Comparison of Sample Enumeration and Stochastic Microsimulation for Application of Tour-Based and Activity-Based Travel Demand Models, European Transport Conference, September 1999, Cambridge, UK.
Bowman, John L., and Moshe E. Ben-Akiva (1999) Incorporating Activity Utility, At-home Activities and Lifestyle in an Activity-based Travel Demand Model, working paper.
A shortened version of Bowman's Ph.D. dissertation (see above).
Bowman, John L., and Moshe E. Ben-Akiva (1997) Activity based travel forecasting, in Activity-Based Travel Forecasting Conference, June 2-5, 1996: Summary, Recommendations and Compendium of Papers, New Orleans, Louisiana. USDOT report #DOT-T-97-17, 32 pages.
An examination of the theory underlying activity based travel forecasting models, and the classification of the differences among modeling approaches, provide a framework that is used to compare six important examples.
Guest lectures at the Activity-Based Modelling Symposium, Research Centre for Integrated Transport and Innovation, UNSW, Sydney, Australia, March 10, 2014.
L. (2014) DaySim. Presentation
Bowman, John L. (2014) Activity-Based Model Applications. Presentation slides (pdf)
Bowman, John L. (2014) Population Synthesis Challenges. Presentation slides (pdf)
L. , Mark A. Bradley, Joe Castiglione, Supin Yoder (2014)
Making advanced travel
forecasting models affordable through model transferability,
TRB 93rd Annual Meeting, Washington, D.C., January 12-16, 2014
Bowman, John L. (2013) Activity-Based Model Systems, MIT Advanced Demand Modeling Class Guest Lecture, November 22, 2013.
Bowman, John L. (2013) Activity-Based Models: What, Why and How, Institute for Transport Studies, University of Leeds Guest Lecture, August 6, 2013.
Bowman, John L. (2012) Activity-Based Models 1993-2012: One Developer’s Perspective, UC Berkeley Guest Lecture, September 14, 2012.
An updated history of Activity-based
Bowman, John L. (2009) Activity Model Development Experiences, TMIP Webinar, June 18, 2009.
This presentation is for those who
are considering a move to activity-based models. It
describes an activity-based model, starting from the familiar
trip-based model framework. Then it explains the basic
development approaches, tasks and roles; mentions keys to
success; and offers suggestions for proceeding.
Bowman, John L. (2009) Activity-Based Models: 1994-2009, presented at the MIT ITS Lab, Cambridge, Massachusetts, March 10, 2009.
Bowman, John L. (2008) How is an Activity-Based Model Set Developed? presented at the Chicago Metropolitan Agency for Planning Symposium on Developing and Implementing an Activity-Based Travel Demand Model, August 27, 2008.
A pre-cursor of the TMIP webinar on activity model development (see above).
Bowman, John L. (2008) The Day Activity Schedule Approach of Bowman, Ben-Akiva and Bradley: 1994-2008, presented at the Transportation Research Board Innovations in Travel Modeling Conference, June 22-24, 2008.
Traces the development of the day activity schedule approach from its birth at MIT in 1994 through its real-world implementations as of 2008. Includes slides from early presentations. Emphasizes the original concepts and findings, as well as enhancements that have occurred since then.
Bowman, John L. (2008) From Theory To Practice: What can we learn from our U.S. experience? presented at the Transportation Research Board Annual Meeting Task Force on Moving Activity-based Approaches into Practice, January 13, 2008.
A retrospective examination of the activity-based model development projects sponsored by regional planning agencies in the United States. The presentation takes a project by project look at the innovations that occurred, then considers why some projects were more successful than others.
DaySim is software that simulates a
day of activity and travel for each person in each household of
a synthetic population distributed throughout a given
geographical area. It does this using an integrated set of
econometric discrete choice models. DaySim uses nine
activity purposes, represents activity locations as land parcels
or microzones, and schedules activity and travel to the
minute. DaySim works iteratively with any standard or
custom software that is able to route the trips that DaySim
generates between origins and destinations and provide back to
DaySim matrices of travel times and costs.
DaySim is currently available as open source software without a license fee through a consulting business model. That is, if you engage one of the copyright holders (John L. Bowman, Mark A. Bradley or Resource Systems Group) for consulting services for its implementation, then you will be granted an open source license to the code. The following materials provide information about DaySim. If you are considering acquiring DaySim and have questions, please email John_L_Bowman@alum.mit.edu.
DaySim technical documentation examples
DaySim was originally developed in Sacramento and then used for several years until it was upgraded to the current standard DaySim version. The following documentation describes the original implementation and is provided here for reference purposes only. Although the current DaySim software is based very heavily on the original version, these are historical documents and they differ in some cases from the current implementation.
SACSIM is a regional travel forecasting model system, developed in 2005 and implemented in 2006 for the Sacramento (California) Area Council of Governments (SACOG). The system features an integrated econometric microsimulation of personal activities and travel (DAYSIM) with a highly disaggregate treatment of the purpose, time of day and location dimensions of the modeled outcomes. Here are various technical documents produced during the original development and implementation of SacSim and DaySim. They provide a very detailed description of the model system.
Package of all the following documents (3000K Zip file)
Design and planning documents (pdf
Preliminary design report (2002)
Technical memo 1—Model System Design (early 2005) The best design overview of the original DaySim
Phase 2 Working Paper 2.1 (June 2005)
Models (pdf files)
Technical memo 2—Population Synthesizer
Technical memo 4—Mode Choice
Technical memo 5—Intermediate Stop Location
Technical memo 6—Day Pattern Activity Generation
Technical memo 7—Time of Day/Activity Scheduling
Technical memo 8—Usual and Tour Destination
Technical memo 9—Household Auto Availability
Technical memo 11—Impedance and Accessibility Effects
DaySim program (pdf files)
Technical memo 3—Design of Model System Application Software
Technical memo 10—DaySim05 Documentation
SacSim (pdf files)
Application of an Activity-Based Travel Model of the Sacramento Region (SacSim) September 21, 2006 draft
Sacramento Activity-Based Travel Simulation Model (SACSIM07): Model Reference Report November 2008 review draft
Report of the SacSim Improvement Program Peer Review Panel November 2008
Papers (pdf files)
Bowman, John L. and Mark A. Bradley (2005) Disaggregate treatment of purpose, time of day and location in an activity-based regional travel forecasting model, European Transport Conference, October 2005, Strasbourg, France.
Bowman, John L., Mark A. Bradley and John Gibb (2006) The Sacramento activity-based travel demand model: estimation and validation results, European Transport Conference, September 2006, Strasbourg, France.
Bradley, Mark, John L. Bowman and Bruce Griesenbeck (2010) SACSIM: An applied activity-based model system with fine-level spatial and temporal resolution, Journal of Choice Modeling Vol. 3 No. 1: 5-31, 2010. Also available at http://www.sciencedirect.com/science/journal/17555345/3/1
ALOGIT model estimation software
The discrete response models in the above papers and projects were all estimated with ALOGIT software. ALOGIT can be used to estimate very large MNL and nested logit models required in practical transportation and other choice model applications. The latest version, ALOGIT 4, can use simulation methods to estimate logit models with random parameters (ie, mixed logit, error-components logit, logit kernel). For information about ALOGIT see http://www.alogit.com. For information about potential discounts, email John_L_Bowman@alum.mit.edu.
together with ALOGIT for purposes of model estimation.
When it is run in estimation mode, DaySim generates ALOGIT
control and data files instead of simulating outcomes.
ALOGIT is then used to estimate the model coefficients, and the
resulting coefficient files are read and used by DaySim when it
runs in application mode.
Resources about bicycling from Copenhagen, Denmark
Bowman lived and worked in Copenhagen, Denmark, for eleven months during 2013 and 2014, helping his Danish colleagues implement an activity-based model that uses the DaySim software and handles bicycling as a transport mode more effectively than prior Danish models. While there he experienced the world famous Danish bicycling environment. The following materials include English language versions of key Danish publications that reveal how the Danes, and Copenhagen in particular, have achieved such desirable results through sustained thoughtful investment of money and effort. Also included here are photo presentations by Bowman of the Copenhagen bicycling environment, as well as his report to the Danes on methods for incorporating bicycling more effectively into their activity-based traffic demand model.
English language Danish publications
Good, Better, Best: The City of Copenhagen's Bicycle Strategy 2011-2025 6.8MB pdf 3.8MB zip
Copenhagen City of Cyclists: Bicycle Account 2012 (7.6MB)
Eco-Metropolis: Our Vision for Copenhagen 2015
Collection of Cycle Concepts 2012 13MB pdf 7.9MB zip
John Bowman (pdf files):
Cycling in Copenhagen (2.8MB photo presentation)
Lessons from Copenhagen (3.2MB photo presentation)
Incorporating Bicycling into Activity-based Regional Travel Forecasting Models in Denmark: Identified Needs and Proposed Solutions (Report)