Published
2021-02-14
Issue
Section
Articles
License
Copyright (c) 2021 Ayad Hammadi, Eric J Miller
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with JTLU agree to the following terms: 1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution-Noncommercial License 4.0 that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. 2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal. 3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
How to Cite
An agent-based transportation impact sketch planning (TISP) model system
Ayad Hammadi
University of Toronto
Eric J Miller
University of Toronto
DOI: https://doi.org/10.5198/jtlu.2021.1863
Keywords: Transportation Planning, Sketch Planning, Land Use, Population Synthesis
Abstract
A traffic impact sketch planning (TISP) model is presented for the estimation of the likely travel demand generated by a major land-use development or redevelopment project. The proposed approach overcomes the problems with the non-behavioral transportation-related studies used in practice for assessing the development design impacts on the local transportation system. The architectural design of the development, in terms of the number and type of dwellings, by number of bedrooms per unit, and the land-use categories of the non-residential floorspace, are reflected in the TISP model through an integrated population and employment synthesis approach. The population synthesis enables the feasible deployment of an agent-based microsimulation (ABM) model system of daily activity and travel demand for a quick, efficient, and detailed assessment of the transportation impacts of a proposed neighborhood or development. The approach is not restricted to a certain type of dataset of the control variables for the geographic location of the development. Datasets for different geographic dimensions of the study area, with some common control variables, are merged and cascaded into a synthesized, disaggregate population of resident persons, households and jobs.
The prototype implementation of the TISP model is for Waterfront Toronto’s Bayside Development Phase 2, using the operational TASHA-based GTAModel V4.1 ABM travel demand model system. While the conventional transportation studies focus on the assessment of the local traffic impacts in the immediate surroundings of the development, the TISP model investigates and assesses many transportation related impacts in the district, city, and region, for both residents and non-residents of the development. TISP model analysis includes the overall spatiotemporal trips distribution generated by the residents and non-residents of the development for the auto and non-auto mobility systems and the simulated agents diurnal peaking travel times. The model results are compared with the trips estimates by a prior project traffic impact study and the Institute of Transportation Engineers (ITE) Trip Generation Manual (TGM) rates of weekday trips for the relevant land uses. Future extensions and improvements of the model including the generalization and full automation of the model, and the bi-level macro-micro representation of the transportation network are also discussed.
References
Beckman, R. J., Baggerly, K. A., & McKay, M. D. (1996). Creating synthetic baseline populations. Transportation Research Part A: Policy and Practice, 30(6), 415–429.
Becker, U. J., Schneider, R., & Schwartzmann, R. (1991). Transportation planning: Microscopic approach. In M. Papageorgiou (Ed.), Concise encyclopedia of traffic and transportation systems (pp. 569–574). Oxford, UK: Pergamon Press.
Brand, D., & Manheim, M. L. (Eds.) (1973). Proceedings of the conference on urban travel demand forecasting. Special report 143. Williamsburg, Virginia, December 3-7, 1972. Retrieved from http://onlinepubs.trb.org/Onlinepubs/sr/sr143/sr143.pdf
Deming, W. E., & Stephan, F. F. (1940). On a least squares adjustment of a sampled frequency table when the expected marginals are known. The Annals of Mathematical Statistics, 11(4), 427–444.
Diogu, W. (2019). Towards the implementation of an activity-based travel demand model for emerging cities: Integrating TASHA and MATSim (master’s thesis). Department of Civil & Mineral Engineering, University of Toronto, Toronto, Ontario.
DMG. (2018). TTS 2016 data guide. Toronto: Data Management Group, University of Toronto Transportation Research Institute. Retrieved from http://dmg.utoronto.ca/pdf/tts/2016/2016TTS_DataGuide.pdf
Farooq, B., Bierlaire, M., Hurtubia, R., & Flötteröd, G. (2013). Simulation based population synthesis. Transportation Research Part B, 58, 243–263.
Geman, S., & Geman, D. (1984). Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Transactions of Pattern Analysis and Machine Intelligence, 6(6), 721–741.
Hammadi, A. (2020). Modelling transportation system impacts of housing supply dynamics (doctoral dissertation). Department of Civil & Mineral Engineering, University of Toronto, Toronto, Ontario.
Hao, J. (2009) TASHA-MATSim integration and its application in emissions modelling. (master’s thesis). Department of Civil & Mineral Engineering, University of Toronto, Toronto, Ontario.
Hensher, D. A., & Button, K. J. (2000). Introduction. In D. A. Hensher, & K. J. Button (Eds.), Handbook of transport modelling (pp. 1–33). Oxford, UK: Pergamon Press.
Horowitz, A. J. (2005). Tests of a family of trip table refinements for long-range, quick-response travel forecasting. Transportation Research Record, Journal of the Transportation Research Board, 1921, 19–26.
ITE. (2017). Trip generation manual, 10th edition. Washington, DC: Institute of Transportation Engineers.
Kaplan, M. P., Gur, Y., & Vyas, A. D. (1984). Sketch-planning model for urban transportation policy analysis. Transportation Research Record, 952, 32–39.
Koppelman, F. S. (1975). Travel prediction with models of individual choice behavior. Retrieved from https://dspace.mit.edu/handle/1721.1/27389
Martin, W. A., & Mcguckin, N. A. (1998). Travel estimation techniques for urban planning (Planning and Administration No. 365). Chicago: Barton–Aschman Associates, Inc.
Miller, E. J. (2020). Chapter 2: Travel demand models, the next generation: Boldly going where no-one has gone before. In K.G. Goulias & A.W. Davis (Eds.), Mapping the travel behavior genome: The role of disruptive technologies, automation and experimentation (pp. 29-46). Amsterdamn: Elsevier.
Miller, E. J., & Roorda, M. J. (2002). A prototype model of household activity/travel scheduling. Transportation Research Record, Journal of the Transportation Research Board, 1831, 114–121.
Miller, E. J., Roorda, M. J., & Carrasco, J. A. (2005). A tour-based model of travel mode choice. Transportation, 32(4), 399–422.
Moeckel, R. (2018). Integrated transportation and land use models. Munich: Technical University of Munich.
Morlok, E. K. (1978). Introduction to transportation engineering and planning. New York: McGraw-Hill.
Müller, K., & Axhausen, K. W. (2010). Population synthesis for microsimulation: State of the art. Paper presented at the Institute for Economic Research’s 10th Swiss Transport Research Conference (STRC), September 1–4, Ascona, Switzerland.
Ortùzar, J. D., & Wilumsen., L. (1990). Modelling transport. Hoboken, NJ: John Wiley & Sons.
Roorda, M. J., Miller, E. J., & Nurul Habib, K. (2008). Validation of TASHA: A 24-hour activity scheduling microsimulation mode. Transportation Research A, 42, 360–375.
Roorda, M. J., & Miller, E. J. (2006). Assessing transportation policy using an activity-based microsimulation model of travel demand. (Winner, ITE past presidents’ award 2006). ITE Journal 76, 16–21.
Schneider, R. J., Hu, L., & Stefanich, J. (2019). Development of a neighborhood commute mode share using nationally available data. Transportation, 46(3), 909–929.
Sosslau, A. B., Hassam, A. B., Carter, M. M., & Wickstorm, G. V. (1978). Quick–response urban travel estimation techniques and transferable parameters: User’s guide (Techichal report No. 186,187). Wheaton, MD: Comsis Corporation.
Statistics Canada. (2019). 2016 census of population (Canada) public use microdata file (PUMF): Individuals file. Study documentation. Kingston, Ontario: Data Services, Queen’s University.
WSP. (2018). Lower Yonge Precinct. Municipal class environmental assessment study. Toronto: Waterfront Toronto and the City of Toronto.
Zhuge, C., Li, X., Ku, C.-A., Gao, J., & Zhang, H. (2017). A heuristic-based population synthesis method for micro-simulation in transportation. KSCE Journal of Civil Engineering, 21(6), 2373–2383.
Zhang, L., Chang, G.-L., Zhu, S., Xiong, C., Du, L., Mollanejad, M., & Mahapatra, S. (2013, June). Integrating an agent-based travel behavior model with large-scale microscopic traffic simulation for corridor-level and subarea transportation operations and planning applications. Journal of Urban Planning and Development, 139(2), 94–103
