John Bowman Website Archive

Table of Contents

• Theses
• Refereed Publications
• Other Publications and Working Papers
• Presentations
• DaySim Activity-Based Travel Simulator
• The Original Sacramento DaySim Implementation
• Resources about Bicycling from Copenhagen, Denmark

Note: Last update prior to archiving occurred in 2015.

Theses

• Bowman, John L. (1998). The day activity schedule approach to travel demand analysis (Synopsis), 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.

Refereed Publications

• 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, 3(1), pp. 5-31. 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, pp. 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, pp. 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.

Other Publications and Working Papers

• 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.

  • Estimation Results (zipped .xlsx)
  • Metadata (zipped .csv)
  • Presentation Slides (PDF)

• Castiglione, Joe, Brian Grady, John L. Bowman, Mark Bradley and Stephen Lawe (2010). Building 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.

• Bowman, John 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 model 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 synthesizers.

• 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.

  • Presentation Slides (PDF)

  • Presentation Script (PDF)

    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 simultaneously.

• 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.

Presentations

• Guest lectures at the Activity-Based Modelling Symposium, Research Centre for Integrated Transport and Innovation, UNSW, Sydney, Australia, March 10, 2014.

  • Bowman, John L. (2014). DaySim. (PDF)
  • Bowman, John L. (2014). Activity-Based Model Applications. (PDF)
  • Bowman, John L. (2014). Population Synthesis Challenges. (PDF)

• Bowman, John 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 models.

  • Presentation Slides (PDF)
  • Presentation Script (PDF)

• 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.

  • Presentation Slides (PDF)
  • Presentation Script (PDF)

• Bowman, John L. (2009). Activity-Based Models: 1994-2009, presented at the MIT ITS Lab, Cambridge, Massachusetts, March 10, 2009.

  • Presentation Slides (PDF)
  • Presentation Script (PDF)

• 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).

  • Presentation Slides (PDF)
  • Presentation Script (PDF)

• 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.

  • Presentation Slides (PDF)
  • Presentation Script (PDF)

• 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.

  • Presentation Slides (PDF)
  • Presentation Script (PDF)

DaySim Activity-Based Travel Simulator

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 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.

• DaySim Overview (PDF)
• DaySim Implementations (PDF)
• DaySim Features and Version Comparison (PDF)
• DaySim Technical Documentation Examples:
  • Standard (Person) Version Technical Documentation (PDF)
  • Tampa Bay Mode and Path Choice Model Documentation (PDF)
  • Nashville User's Guide (Word)

The Original Sacramento DaySim Implementation

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.

The following documents can be downloaded individually or as a single package.

Design and Planning Documents (PDFs)

• Preliminary design report (2002)
• Addendum (2003)
• 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 (PDFs)

• 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 (PDFs)

• Technical memo 3—Design of Model System Application Software
• Technical memo 10—DaySim05 Documentation

SacSim (PDFs)

• 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

Papers (PDFs)

• 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 3(1), pp. 5-31. Also available at http://www.sciencedirect.com/science/journal/17555345/3/1

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.

English Language Danish Publications

• Good, Better, Best: The City of Copenhagen's Bicycle Strategy 2011-2025 (PDF)
• Copenhagen City of Cyclists: Bicycle Account 2012 (PDF)
• Eco-Metropolis: Our Vision for Copenhagen 2015 (PDF)
• Collection of Cycle Concepts 2012 (PDF)

Materials by John Bowman

• Cycling in Copenhagen (PDF)
• Lessons from Copenhagen (PDF)
• Incorporating Bicycling into Activity-based Regional Travel Forecasting Models in Denmark: Identified Needs and Proposed Solutions (Report) (PDF)