Abstract
Hägerstrand’s original framework of time geography and the subsequent time–space prism computational methods form the foundation of a new computational method for potential path areas (PPA) in a realistic representation of dynamic urban environments. In this paper the time–space prism framework is used to assess sensitivity of PPA size to different parameters and to build choice sets for regional destination choice models. We explain the implication of different parameters to choice set formation in a step-wise manner and illustrate not only the complexity of the idea and the high computational demand but also behavioral realism. In this context, this paper tests the feasibility of using constraint-based time–space prism to find the choice sets for a large-scale destination choice model, and identifies a variety of implementation issues. Computational demand is estimated based on a household travel survey for the Southern California Association of Government, and the feasibility of using time–space prisms for destination choice models is assessed with different levels of information on the network and destinations available. The implications of time of day effects and flexibility in scheduling on choice set development due to varying level of service on the network and availability of activity opportunities are discussed and numerically assessed.
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Acknowledgments
Funding for this project was provided by the University of California Transportation Center, the United States Department of Transportation Eisenhower Fellowship program, the University of California Office of the President UC Lab Fees Program, the University of California Office of the President Multicampus Research Program Initiative Sustainable Transportation, and the Southern California Association of Governments. The contents of this paper do not constitute a policy at any level of government.
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Yoon, S.Y., Deutsch, K., Chen, Y. et al. Feasibility of using time–space prism to represent available opportunities and choice sets for destination choice models in the context of dynamic urban environments. Transportation 39, 807–823 (2012). https://doi.org/10.1007/s11116-012-9407-8
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DOI: https://doi.org/10.1007/s11116-012-9407-8