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Equilibrium trip distribution/assignment with variable destination costs

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  • Oppenheim, Norbert

Abstract

A "user-equilibrium" trip distribution/assignment model is developed, which includes an endogenous destination cost for the conduct of a given activity at the destination. Destination costs are determined endogenously, as increasing functions of the number of trips to the destination. Link travel costs are increasing functions of link volumes. Travelers are assumed to choose the itinerary of deterministically perceived least cost. Activity performers are assumed to choose among alternative destinations so as to minimize the randomly perceived total cost of activity and travel. It is shown that the model always possesses a unique solution, when destination costs reflect either positive or negative externalities. An algorithm for obtaining that solution is described. The model is well suited to the analysis of a variety of issues concerning the effects of road pricing policies on urban travel demand and, conversely, of toll-based traffic management strategies on economic performance.

Suggested Citation

  • Oppenheim, Norbert, 1993. "Equilibrium trip distribution/assignment with variable destination costs," Transportation Research Part B: Methodological, Elsevier, vol. 27(3), pages 207-217, June.
    • Handle: RePEc:eee:transb:v:27:y:1993:i:3:p:207-217
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    Cited by:

    1. Oppenheim, Norbert, 1995. "On the integrability problem in discrete spatial activity systems with site and network externalities," Regional Science and Urban Economics, Elsevier, vol. 25(1), pages 85-108, February.
    2. Ye, Jiao & Jiang, Yu & Chen, Jun & Liu, Zhiyuan & Guo, Renzhong, 2021. "Joint optimisation of transfer location and capacity for a capacitated multimodal transport network with elastic demand: a bi-level programming model and paradoxes," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    3. Yao, Jia & Chen, Anthony & Ryu, Seungkyu & Shi, Feng, 2014. "A general unconstrained optimization formulation for the combined distribution and assignment problem," Transportation Research Part B: Methodological, Elsevier, vol. 59(C), pages 137-160.
    4. Xu, Meng & Chen, Anthony & Gao, Ziyou, 2008. "An improved origin-based algorithm for solving the combined distribution and assignment problem," European Journal of Operational Research, Elsevier, vol. 188(2), pages 354-369, July.
    5. Nicholas S. Caros & Jinhua Zhao, 2022. "Preparing urban mobility for the future of work," Papers 2201.01321, arXiv.org.
    6. Yang, Hai & Bell, Michael G. H. & Meng, Qiang, 2000. "Modeling the capacity and level of service of urban transportation networks," Transportation Research Part B: Methodological, Elsevier, vol. 34(4), pages 255-275, May.
    7. Yang, Chao & Chen, Anthony, 2009. "Sensitivity analysis of the combined travel demand model with applications," European Journal of Operational Research, Elsevier, vol. 198(3), pages 909-921, November.
    8. Chang, Justin Sueun & Mackett, Roger Laurence, 2006. "A bi-level model of the relationship between transport and residential location," Transportation Research Part B: Methodological, Elsevier, vol. 40(2), pages 123-146, February.
    9. Chen, Anthony & Kasikitwiwat, Panatda, 2011. "Modeling capacity flexibility of transportation networks," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(2), pages 105-117, February.
    10. Ampol Karoonsoontawong & Dung-Ying Lin, 2015. "Combined Gravity Model Trip Distribution and Paired Combinatorial Logit Stochastic User Equilibrium Problem," Networks and Spatial Economics, Springer, vol. 15(4), pages 1011-1048, December.
    11. Tam, M. L. & Lam, William H. K., 2000. "Maximum car ownership under constraints of road capacity and parking space," Transportation Research Part A: Policy and Practice, Elsevier, vol. 34(3), pages 145-170, April.
    12. Heqing Tan & Muqing Du & Xiaowei Jiang & Zhaoming Chu, 2019. "The Combined Distribution and Assignment Model: A New Solution Algorithm and Its Applications in Travel Demand Forecasting for Modern Urban Transportation," Sustainability, MDPI, vol. 11(7), pages 1-18, April.

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