Journal of Transport and Land Use

The case for microsimulation frameworks for integrated urban models

Eric J. Miller

University of Toronto

DOI: https://doi.org/10.5198/jtlu.2018.1257

Keywords: Integrated urban models, transportation, land use, microsimulation

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Abstract

The primary objective of this paper is to “make the case” for adoption of microsimulation frameworks for development of integrated urban models. Similar to the case of activity-based travel models, microsimulation in integrated urban models enables such models to deal better with: heterogeneity and non-linearity in behavior; identification of the detailed spatial and socioeconomic distribution of impacts, benefits and costs; tracing complex interactions across agents and over time; providing support for modelling memory, learning and adaptation among agents; computational efficiency; and emergent behavior. The paper discusses strengths, weaknesses and challenges in microsimulating urban regions, including the extent to which microsimulation models are still subject to Lee’s famous “seven sins of large-scale modelling,” as well as the extent to which they may help alleviate or reduce these sins in operational models. The paper concludes with a very brief discussion of future prospects for such models.

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Author Biography

Eric J. Miller, University of Toronto

Professor, Department of Civil Engineering Director, University of Toronto Transportation Research Institute

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References

Abraham, J. E., & Hunt, J. D. (2007). Random utility location, production, and exchange choice; additive logit model; and spatial choice microsimulations. Transportation Research Record: Journal of the Transportation Research Board, 2003, 1–6.

Batty, M. (2005). Cities and complexity: Understanding cities with cellular automata, agent-based models and fractals. Cambridge MA: MIT Press.

Batty, M. (2008). The size, scale and shape of cities. Science, 319, 769–771.

Batty, M. (2013). The new science of cities. Cambridge MA: MIT Press.

Bettencourt, L. M. A., Lobo, J., Helbing, D., Kuhnert, C., & West, G. B. (2007). Growth, innovation, scaling, and the pace of life in cities. Proceedings of the National Academy of Science, 104(17), 7301–7306.

Bettencourt, L. M. A., & West, G. B. (2010). A Unified Theory of Urban Living. Nature, 467, 912–913.

Bettencourt, L. M. A. (2013). The origins of scaling in cities. Science, 340, 1438–1441.

Bonsall, P. W. (1982). Microsimulation: Its application to car sharing. Transportation Research A, (15), 421–429.

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

Chapin, F. S., & Weiss, S. F. (1968). A probabilistic model for residential growth. Transportation Research, 2, 375–390.

Chingcuanco, F., & Miller, E. (2018). The ILUTE demographic microsimulation model for the Greater Toronto-Hamilton Area: Current operational status and historical validation. In J.C. Thill & S. Dragicevic (Eds), Geocomputational analysis and modeling of regional science, advances in geographic information science (pp. 139–159). Basel, Switzerland: Springer International Publishing. doi: 10.1007/978-3-319-59511-5_10.

Downey, A. B. (2012) Think complexity. Needham, MA: Green Tea Press.

Farooq, B., Miller, E., Chingcuanco, F., & Giroux-Cook, M. (2013). Microsimulation framework for urban price-taker markets. Journal of Transport and Land Use, 6(1), 41–51.

Farooq, B., & Miller, E. (2012). Towards integrated land use and transportation: A dynamic disequilibrium based microsimulation framework for built space markets. Transportation Research A, 46(7), 1030–1053.

Foot, D. K. (1996). Boom, bust and echo, profiting from the demographic shift in the 21st century. Toronto: Macfarlane Walter & Ross.

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

Goulias, K. G., & Kitamura, R. (1996). A dynamic model system for regional travel demand forecasting. In T. Golob, R. Kitamura, & L. Long (Eds.), Panels for transportation planning: Methods and applications (pp. 321–348). Norwell, MA: Kluwer Academic Publishers.

Hatzopoulou, M., Hao, J. Y., & Miller, E. J. (2011). Simulating the impacts of household travel on greenhouse gas emissions, urban air quality, and population exposure. Transportation, 38(6), 871–887.

Hao, J. Y., Hatzopoulou, M., & Miller, E. J. (2010). Integrating an activity-based travel demand model with dynamic traffic assignment and emissions models: An implementation in the Greater Toronto Area. Transportation Research Record: Journal of the Transportation Research Board, 2176, 1–13.

Horni, A., Nagel, K., & Axhausen, K. W. (2016). The multi-agent transport simulation MATSim. Retrieved from http://matsim.org/the-book.

Hunt, J. D., & Abraham, J. E. (2005). Design and implementation of PECAS: A generalized system for the allocation of economic production, exchange and consumption quantities. In M. Lee-Gosselin & S. Doherty (Eds), Integrated land-use and transportation models: Behavioral foundations (pp. 253–274). Amsterdam: Elsevier.

Kennedy, C. A. (2011). The evolution of great world cities, urban wealth and economic growth. Toronto: University of Toronto Press.

Lee, D. B. (1973). Requiem for large-scale models. Journal of the American Institute of Planners, 39, 163–178.

Lee, D. B. (1994). Retrospective on large-scale urban models. Journal of the American Planning Association, 60, 35–40.

Mackett, R. L. (1985). Micro-analytical simulation of locational and travel behavior. Proceedings PTRC summer annual meeting, Seminar L: Transportation planning methods (pp. 175–188). London: PTRC.

Mackett, R. L. (1990). Exploratory analysis of long-term travel demand and policy impacts using micro-analytical simulation. In P. Jones (Ed.), Developments in dynamic and activity-based approaches to travel analysis (pp. 38–45). Aldershot, UK: Avebury.

Martínez, F., & Donoso, P. (2010). The MUSSA II land-use auction equilibrium model. In F. Pagliara, J. Preston, & D. Simmonds (Eds.), Residential location choice (pp. 99–113). Berlin: Springer.

Miller, E. (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.

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

Miller, E. (2009). Integrated urban models: Theoretical prospects, invited resource paper. In R. Kitamura, T. Yoshii & T. Yamamoto (Eds.). The expanding sphere of travel behavior research: Selected papers from the 11th international conference on travel behavior research (pp. 351–384). Bingley, UK: Emerald Group Publishing.

Miller, E. (2014). Transportation. In C. O’Donoghue (Ed.), Handbook of microsimulation modelling (pp. 385–420). Bingley: Emerald Group Publishing.

Miller, E. (2017). Integrated urban modelling: Past, present & future. Keynote address presented at the 2017 worldwide symposium on transport and land use research (WSTLUR), July 3, Brisbane, Australia.

Miller, E., Farooq, B., Chingcuanco, F., & Wang, D. (2011). Historical validation of an integrated transport – land-use model system. Transportation Research Record: Journal of the Transportation Research Board, 2255, 91–99.

Miller, E., Kriger, D. S., & Hunt, J. D. (1998). Integrated urban models for simulation of transit and land-use policies (final report, Transit Cooperative Research Project H-12). Toronto: University of Toronto Joint Program in Transportation.

Miller, E., & Roorda, M. J. (2003). A prototype model of household activity/travel scheduling. Transportation Research Record: Journal of the Transportation Research Board, 1831, 114–121.

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

Miller, E., Noehammer, P. J., & Ross, D. R. (1987). A micro-simulation model of residential mobility. Proceedings of the international symposium on transport, communications and urban form, Vol. 2: Analytical techniques and case studies (pp. 217–234). Melbourne: Monash University.

Moeckel, R. (2017). Constraints in household relocation: Modeling land-use/transport interactions that respect time and monetary budgets. The Journal of Transport and Land Use, 10(2), 1–18.

Moeckel, R., Schwarze, B., Spiekermann, K., & Wegener, M. (2007). Simulating interactions between land use, transport and environment. Proceedings of the 11th World Conference on Transport Research. Berkeley, CA: University of California at Berkeley.

O’Donoghuel, C. (2014). Introduction. In C. O’Donoghuel (Ed.), Handbook of microsimulation modelling (pp. 13–31). Bingley, UK: Emerald Group Publishing.

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

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

Psarra, I., Arentze, T. A., & Timmermans, H. J. P. (2016). Short-term adaptations as a response to travel time: Results of a stated adaptation experiment. Transportation Research Record: Journal of the Transportation Research Board, 2565, 48–56.

Roorda, M . J., Carrasco, J. A., & Miller, E. (2009). A joint model of vehicle transactions, activity scheduling and mode choice. Transportation Research B, 43(2), 217–229.

Rosenfield, A., Chingcuanco, F., & Miller, E. (2013). Agent-based housing microsimulation for integrated land use, transportation, environment model system. Procedia Computer Science, 19, 841–846.

Spiekermann, K., & Wegener, M. (2007). The PROPOLIS model for assessing urban sustainability. In M. Deakin, G. Mitchell, P. Nijkamp, & R. Vreeker (Eds.), Sustainable urban development, volume 2. The environmental assessment methods (pp. 306–326). London: Routledge.

Strauch, D., Moeckel, R., Wegener, M., Gräfe, J., Mühlhans, H., Rindsfüser, G., & Beckmann, K.-J. (2005). Linking transport and land-use planning: The microscopic dynamic simulation model ILUMASS. In P. M. Atkinson, G. M. Foody, S. E. Darby, & F. Wu. (Eds.), Geodynamics (pp 295–311). Boca Raton, Florida: CRC Press.

Timmermans, H. (2003). The saga of integrated land use-transport modeling: How many more dreams before we wake up? Conference keynote paper presented at the 10th international conference on travel behavior research, Lucerne, Switzerland.

Waddell, P., Borning, A., Noth, M., Freier, N., & Becke, M. (2003). Microsimulation of urban development and location choice: Design and implementation. Networks and Spatial Economics, 3, 43–67.

Waddell, P. (2011). Integrated land-use and transportation planning and modelling: Addressing challenges in research and practice. Transport Reviews, 31(2), 209–229.

Wagner, P., & Wegener, M. (2007). Urban land use, transport and environment models: Experiences with an integrated mircroscopic approach. disP, 170(3), 45–56.

Wegener, M. (1995). Current and future land-use models. In G. A. Shunk, P. L. Bass, C. A. Weatherby, & L. J. Engelke (Eds.), Travel model improvement program land-use modeling conference proceedings (pp 13–40). Washington, DC: Travel Model Improvement Program.

Wegener, M. (2011a). The IRPUD model. Working paper. Dortmund, Germany: Spiekermann and Wegener Urban and Regional Research.

Wegener, M. (2011b). From macro to micro – how much micro is too much? Transport Reviews 31(2), 161–177.