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Agent-Based Activity/Travel Microsimulation: What’s Next?

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The Practice of Spatial Analysis

Abstract

This chapter briefly summarizes and reviews the current generation of operational activity/tour-based model systems. These model systems are developed to varying degrees within an agent-based microsimulation (ABM) framework. ABM provides an extremely flexible, powerful, and efficient means for modelling complex spatial-temporal, socio-economic behaviour such as travel. A high-level definition of microsimulation in general and agent-based microsimulation in particular is presented. Overall, currently operational activity/travel model systems represent a sound “first generation” of such methods, but they are far from realizing the full potential of the ABM concept. A wide range of issues and challenges in advancing the ABM-based activity/travel modelling state of the art are discussed, leading to a few suggestions for key “next steps” in model development.

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Notes

  1. 1.

    The same holds true for bicycles, which are also, in principle a “tour-based” travel mode.

  2. 2.

    A notable exception to this general statement is the TASHA model, which is the operational travel demand forecasting model for the City of Toronto.

  3. 3.

    Although deterministic simulation models also exist.

  4. 4.

    Work and school location choices/allocations for workers and students are also a major modelling challenge. For the purpose of this chapter, which is focussing on modelling day-to-day activity/travel, we assume that work and school locations are determined exogenously to the activity/travel model, through longer-term labour market (place of residence—place of work) and school participation (place of residence—place of school) models.

  5. 5.

    Exceptions, of course exist. Service workers (plumbers, etc.) and salespersons may have no fixed place of work, travelling each day to wherever their clients on a given day are located; construction workers move from site to site on a frequent basis, etc. But these special cases should be dealt with as such (and certainly are not in current operational models). For most workers, their primary work location is still determined by a longer-term work participation process.

  6. 6.

    And, in principle, the bicycle and pedestrian networks as well, although bicycle and pedestrian route choices are rarely, if ever, explicitly modelled in operational models to date.

  7. 7.

    Of course, this planning may go down to the level of the minute (or a few minutes), but the overall scale of the problem is at the level of the day.

  8. 8.

    In this discussion, it is assumed that we are only concerned with out-of-home episodes. In-home episodes are briefly discussed in Sect. 4.8.

  9. 9.

    And/or “gap” in the current provisions schedule, as briefly discussed in Sect. 4.4.

  10. 10.

    An episode’s end time equals its start time plus duration, so only two of these three attributes are independently determined. While an ending time may sometimes be the primary consideration for scheduling a given episode (I need to be home by 5 pm), it is generally assumed that start time and duration are generally the more “primary” attributes.

  11. 11.

    As per above, it is assumed herein that work and school locations are known. Locations for non-work/school locations, however, are assumed to be chosen as part of the activity episode generation/scheduling process.

  12. 12.

    In such models, trip start time is usually determined by either subtracting the expected travel time from the desired start time of the activity episode being travelled to, or adding the travel time to the activity episode end time if one is leaving this episode to travel to another location.

  13. 13.

    The occasional “all-nighter” getting a term paper done, “partying ‘till the cows come home”, etc. notwithstanding.

References

  • Arentze, T. A., & Timmermans, H. J. P. (2004). A learning-based transportation oriented simulation system. Transportation Research Part B, 38, 613–633. https://doi.org/10.1016/j.trb.2002.10.001.

    Article  Google Scholar 

  • Auld, J., & Mohammadian, A. (2012). Activity planning processes in the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model. Transportation Research Part A, 46, 1386–1403. https://doi.org/10.1016/j.tra.2012.05.017.

    Article  Google Scholar 

  • Aultman-Hall, L., Harvey, C., LaMondia, J., & Ritter, C. (2015). Design and response quality in a one-year longitudinal survey of overnight and long-distance travel. Transportation Research Procedia, 11, 136–153. https://doi.org/10.1016/j.trpro.2015.12.012.

    Article  Google Scholar 

  • Axhausen, K. W., & Gärling, T. (1992). Activity-based approaches to travel analysis: Conceptual frameworks, models and research problems. Transport Reviews, 12(4), 323–341.

    Article  Google Scholar 

  • Badoe, D. A., & Miller, E. J. (1998). An automatic segmentation procedure for studying variations in mode choice behavior. Journal of Advanced Transportation, 32(2), 190–215.

    Article  Google Scholar 

  • Balmer, M., Axhausen, K., & Nagel, K. (2006). Agent-based demand-modeling framework for large-scale microsimulations. Transportation Research Record, 1985, 125–134. https://doi.org/10.3141/1985-14.

    Article  Google Scholar 

  • Bhat, C., Guo, J., Srinivasan, S., & Sivakumar, A. (2004). Comprehensive econometric microsimulator for daily activity-travel patterns. Transportation Research Record, 1894, 57–66. https://doi.org/10.3141/1894-07.

    Article  Google Scholar 

  • Bowman, J. L., & Ben-Akiva, M. (1997). Activity based travel forecasting. In Activity-based travel forecasting conference, June 2–5, 1996: summary, recommendations and compendium of papers, New Orleans, LA (USDOT Report #DOT-T-97-17).

    Google Scholar 

  • Bradley, M., Bowman, J. L., & Griesenbeck, B. (2010). SACSIM: An applied activity-based model system with fine-level spatial and temporal resolution. Journal of Choice Modelling, 3, 5–31. https://doi.org/10.1016/S1755-5345(13)70027-7.

    Article  Google Scholar 

  • Castiglione, J., Bradley, M., & Gleibe, J. (2015). Activity-based travel demand models: A primer, report S2-C46-RR-1. Washington, DC: Transportation Research Board.

    Google Scholar 

  • Clarke, M. C., Keys, P., & Williams, H. C. W. L. (1980). Micro-analysis and simulation of socio-economic systems: Progress and prospects. In R. J. Bennett & N. Wrigely (Eds.), Quantitative geography in Britain: Retrospect and prospect. London: Routledge and Kegan Paul.

    Google Scholar 

  • Dianat, L., Habib, K. N., & Miller E. J. (2018a, January). Investigating the influence of assigning a higher priority to scheduling work and school activities in the activity-based models on the simulated travel/activity patterns. Presented at the 97th annual meeting of the Transportation Research Board, Washington, DC.

    Google Scholar 

  • Dianat, L., Habib, K. N., & Miller, E. J. (2018b, January). Modeling and forecasting daily non-work/school activity patterns in an activity-based model using skeleton schedule constraints. Presented at the 97th Annual Meeting of the Transportation Research Board, Washington, DC.

    Google Scholar 

  • Doherty, S. T., Miller, E. J., Axhausen, K. W., & Gärling, T. (2001). A conceptual model of the weekly household activity-travel scheduling process. In E. Stern, I. Salomon, & P. Bovy (Eds.), Travel behaviour: Patterns, implications and modelling (pp. 148–165). Cheltenham: Elgar Publishing Ltd.

    Google Scholar 

  • Downey, A. B. (2012). Think complexity. Needham: Green Tea Press. Retrieved from http://greenteapress.com/complexity/thinkcomplexity.pdf.

    Google Scholar 

  • Ettema, D., Borgers, A., & Timmermans, H. (1993). Simulation model of activity scheduling behavior. Transportation Research Record, 1413, 1–11.

    Google Scholar 

  • Gleick, J. (1987). Chaos. New York: Viking Penguin.

    Google Scholar 

  • Goulias, K. G., & Kitamura, R. (1992). Travel demand forecasting with dynamic microsimulation. Transportation Research Record, 1357, 8–17.

    Google Scholar 

  • Habib, K. N. (2018). A comprehensive utility-based system of activity travel scheduling options modelling (CUSTOM) for worker’s daily activity scheduling processes. Transportmetreica A: Transport Science, 14(4), 292–315.

    Google Scholar 

  • Hensher, D. A., & Stopher, P. R. (1979). Behavioral travel demand modelling (editorial). London: Croom Helm.

    Google Scholar 

  • Jones, P. M. (1979). New approaches to understanding travel behaviour: The human activity approach. In D. Hensher & P. Stopher (Eds.), Behavioral travel modeling (pp. 55–80). London: Redwood Burn Ltd.

    Google Scholar 

  • Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus and Giroux.

    Google Scholar 

  • Kitamura, R., & Fuji, S. (1998). Two computational process models of activity-travel behavior. In T. Garling, T. Laitila, & K. Westin (Eds.), Theoretical foundations of travel choice modeling (pp. 251–279). Oxford: Pergamon.

    Google Scholar 

  • Kreibich, V. (1979). Modeling car availability, modal split and trip distribution by Monte-Carlo simulation: A short way to integrated models. Transportation, 8, 153–166.

    Article  Google Scholar 

  • LaMondia, J. J., Moore, M., & Aultman-Hall, L. (2016). Matching household lifecycle characteristics to clustered annual schedules of long-distance and overnight travel. Transportation Research Record, 2594, 11–17. https://doi.org/10.3141/2594-02.

    Article  Google Scholar 

  • Mackett, R. L. (1985). Micro-analytical simulation of locational and travel behaviour. In Proceedings PTRC Summer Annual Meeting, Seminar L: Transportation Planning Methods (pp. 175–188). London: PTRC.

    Google Scholar 

  • Manheim, M. L. (1978). Fundamentals of transportation systems analysis, Volume 1: Basic concepts. Cambridge: MIT Press.

    Google Scholar 

  • Märki, F., Charypar, D., & Axhausen, K. W. (2014). Agent-based model for continuous activity planning with an open planning horizon. Transportation, 41, 905–922. https://doi.org/10.1007/s11116-014-9512-y.

    Article  Google Scholar 

  • Maslow, A. H. (1970). Motivation and personality (2nd ed.). New York: Harper & Row.

    Google Scholar 

  • Meyer, M. D., & Miller E. J. (2013). Urban transportation planning, a decision-oriented approach (3rd ed.). web-published: http://mtsplan.com/services.html.

  • Miller, E. J.. (1996). Microsimulation and activity-based forecasting. In Texas Transportation Institute (Ed.) Activity-Based Travel Forecasting Conference, June 2-5, 1996 Summary, Recommendations, and Compendium of Papers, (pp. 151–172). Washington, DC: Travel Model Improvement Program, U.S. Department of Transportation and U.S. Environmental Protection Agency.

    Google Scholar 

  • Miller, E. J. (2003). Microsimulation, Chapter 12. In K. G. Goulias (Ed.), Transportation systems planning methods and applications (pp. 2-1–12-22). Boca Raton: CRC Press.

    Google Scholar 

  • Miller, E. J. (2004). The trouble with intercity travel demand models. Transportation Research Records, Journal of the Transportation Research Board, 1895, 94–101.

    Article  Google Scholar 

  • Miller, E. J. (2017). Modeling the demand for new transportation services & technologies. Transportation Research Record, Journal of the Transportation Research Board, 2658, 1–7.

    Article  Google Scholar 

  • Miller, E. J. (2018). The case for microsimulation frameworks for integrated urban models. Journal of Transport and Land Use (submitted for publication).

    Google Scholar 

  • Miller, E. J., & Roorda, M. J. (2003). Prototype model of household activity-travel scheduling. Transportation Research Record, 1831, 114–121. https://doi.org/10.3141/1831-13.

    Article  Google Scholar 

  • Miller, E. J., & Salvini, P. A. (2002). “Activity-based travel behavior modeling in a microsimulation framework”, invited resource paper, Chapter 26. In H. S. Mahmassani (Ed.), In perpetual motion, travel behavior research opportunities and application challenges (pp. 533–558). Amsterdam: Pergamon.

    Google Scholar 

  • Miller, E. J., Roorda, M. J., & Carrasco, J. A. (2005). A tour-based model of travel mode choice. Transportation, 32(4), 399–422.

    Article  Google Scholar 

  • Miller, E. J., Vaughan, J., King, D., & Austin, M. (2015). Implementation of a “next generation” activity-based travel demand model: The Toronto Case. In: Presentation at the Travel Demand Modelling and Traffic Simulation Session of the 2015 Conference of the Transportation Association of Canada, Charlottetown, PEI, Canada.

    Google Scholar 

  • O’Donoghuel, C. (2014). “Introduction”, Chapter 1. In C. O’Donoghuel (Ed.), Handbook of microsimulation modelling (pp. 13–31). Bingley: Emerald Group.

    Chapter  Google Scholar 

  • Orcutt, G. H. (1957). A new type of socio-economic system. Review of Economic Studies, 39(2), 116–123.

    Google Scholar 

  • Orcutt, G. H. (1960). Simulation of economic systems. The American Economic Review, 50(5), 894–907.

    Google Scholar 

  • Paleti, R., Vovsha, P., Vyas, G., Anderson, R., & Giaimo, G. (2017). Activity sequencing, location, and formation of individual non-mandatory tours: Application to the activity-based models for Columbus, Cincinnati, and Cleveland, OH. Transportation, 44, 615–640.

    Article  Google Scholar 

  • Pendyala, R., Kitamura, R., Kikuchi, A., Yamamoto, T., & Fuji, S. (2005). Florida activity mobility simulator: Overview and preliminary validation results. Transportation Research Record, 1921, 123–130. https://doi.org/10.3141/1921-14.

    Article  Google Scholar 

  • Vovsha, P., Freedman, J., Livshits, V., & Sun, W. (2011). Design features of activity-based models in practice. Transportation Research Record, 2254, 19–27. https://doi.org/10.3141/2254-03.

    Article  Google Scholar 

  • Wang, J. A., & Miller, E. J. (2014). A prism- and gap-based approach to shopping destination choice. Environment & Planning B, 41, 977–1005.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Civil Engineering, University of Toronto, Toronto, ON, Canada

    Eric J. Miller

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  1. Eric J. Miller
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Correspondence to Eric J. Miller .

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Editors and Affiliations

  1. Department of Geography, University of the Aegean, Mytilini, Lesvos, Greece

    Helen Briassoulis

  2. Department of Geography, University of the Aegean, Mytilini, Lesvos, Greece

    Dimitris Kavroudakis

  3. Department of Geography, University of the Aegean, Mytilini, Lesvos, Greece

    Nikolaos Soulakellis

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Miller, E.J. (2019). Agent-Based Activity/Travel Microsimulation: What’s Next?. In: Briassoulis, H., Kavroudakis, D., Soulakellis, N. (eds) The Practice of Spatial Analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-89806-3_6

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