Integrating activity-based travel-demand models with land-use and other long-term lifestyle decisions
Rachel Katoshevski
Geography and Environmental Development, Ben-Gurion University of the Negev, Beersheba, Israel
Inbal Glickman
Faculty of Civil and Environmental Engineering , Technion
Robert Ishaq
Faculty of Civil and Environmental Engineering , Technion
Yoram Shiftan
Faculty of Civil and Environmental Engineering , Technion
DOI: https://doi.org/10.5198/jtlu.2015.658
Keywords: Activity-based model, Land use, Urban planning
Abstract
This paper extends and integrates the general activity-based model framework to present the complex relationship between long-term individual decisions, such as residential location, and daily activity and travel-behavior decisions. More specifically, it demonstrates the use of an activity-based accessibility (ABA) measure as an important variable in residential zone choice, thus serving as the link between short-term activity and travel decisions and long-term residential choice decisions. We develop a partial activity-based model accounting for the interrelationship of the main activity type, travel destination and mode choice. The log-sum at the top of the hierarchy of this model is the ABA measure capturing the overall utility of all travel alternatives. The results show that this measure is a highly significant variable in the residential-choice model, clearly indicating the great influence of activity accessibility, short-term opportunities, and travel decisions on residential area choice. All other log-sums were also significant, showing the interrelationships of all choices. Specifically, the destination-choice log-sum in the main activity-choice model demonstrates that as accessibility increases, people are more likely to participate in out-of-home activities.References
Algers, S, M. Beser. 2000. SAMPERS- the new Swedish national travel demand forecasting tool. IATBR 2000 Conference, Gold Coast, Australia.
Aretze, T,A, H. J. P. Timmermans. 2000 Albatross: A learning-Based Transportation Oriented Simulation System, European Institute of Retailing and Services studies, Eindhoven, The Netherlands.
Arentze, T,A., H,J,P. Timmermans. A micro-simulator of urban land use dynamics integrating a multi-agent model of land development and an activity-based model of transport demand. Proceedings of the 83rd Annual Meeting of the Transportation Research Board, (CD-Rom), Jan. 2004, Washington DC, USA.
Bhat, C.R, A. Guo, J.Y, Srinivasan, A., 2004. Comprehensive econometric microsimulator for daily activity-travel patterns, Transp. Res. Rec., 1894: 57–66.
Bowman, J.L., M. Ben-Akiva. 2000. Activity-based disaggregate travel demand model system with activity schedules. Transp. Res. A, 35(1):1–28.
Gunn, H,F, Van der Hoorn, 1998. The predictive power of operational demand models. Proceedings of the Seminar Transportation Planning Methods, ISBN 0-86050-313-5.
Katoshevski-Cavari, R. 2007. A Multi-Agent Planning Support System for Assessing Externalities of Urban Form Scenarios; Development and Application in an Israeli Case Study. PhD Thesis, Eindhoven University Press, The Netherlands.
Katoshevski-Cavari, R, T. Arentze, and H. Timmermans. 2009 A computerized tailor made plan- can that be a tool for achieving public interest in planning, GRF, 29:26-47.
Kitamura, R, Pas, E,L, Lula, C,V, Lawton, T,k, Benson, P,E Ben-Akiva. 1996. The sequenced activity mobility simulator (SAMS): an integrated approach to modeling transportation, land-use and air quality. Transp, 23(3):267-291.
Miller, E,J, Hunt, J,D, Abraham J,E, Salvini, P,A. 2004. Microsimulation urban systems. Comp Environ Urban Syst, 28:9-44.
Miller. E.J. 2005. An integrated framework for modeling short and long-run household decision-making, in : KKOS immermans, H. (ed) Progress in Activity based Analysis, Elsevier, Amsterdam, pp. 175-201.
Nagel, K., M., 2001, Ricker. Parallel implementation of the TRANSIMS microsimulation. parallel comput. 27: 1611–1639.
Roorda, M,J, Carrasco, J, A, Miller. 2009. An integrated model of vehicle transactions, activity scheduling and mode choice. Transportation Research Part B, 43:217-229.
Roorda, M.J., Ruiz, T. 2008. Long- and short-term dynamics in activity scheduling: a structural equations approach, Transportation Research Part A: Policy and Practice, 42 (3): 545-562.
Rossi, T, and Shiftan, Y. 1997. Tour-based travel demand modeling in the U.S., proceedings of the 8th IFAC/IFIP/IFORS Symposium on Transportation Systems, Chania, Greece, June.
Salvini, P, Miller, E,J. 2005. ILUTE: An operational prototype of comprehensive microsimulation model of urban systems, Networks and Spatial Economics. 5:217-234.
Shiftan, Y. 1999. A practical approach to model trip chaining. Transportation Research Record, 1945:17-23.
Shiftan, Y. 2008. The use of activity-based modeling to analyze the effect of land-use policies on travel behavior, Ann Reg Sci, 42:79-97.
Shiftan, Y, M. Ben-Akiva. 2008. A Practical Policy Sensitive Activity-Based Model, 3rd Israeli-Netherland Workshop in Regional Science, Jerusalem.
Shiftan, Y., M. Ben-Akiva. 2011. A practical policy-sensitive, activity-based, travel-demand model, The Annals of Regional Science, 47 (3): 517-541.
Timmermans, H.J.P. 2006. Costs of travel time uncertainty and benefits of travel time information: Conceptual model and numerical examples. Transportation Research. Part C: Emerging Technologies. 14 (5):335-350.
Van Acker, V., Witlox, F. 2010. Car ownership as a me- diating variable in car travel behavior research using a structural equation modeling approach to identify its dual relationship. Journal of Transport Geography, 18(1): 65–74.
