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Modeling public transport corridors with aggregate and disaggregate demand

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  • Jara-Díaz, Sergio
  • Tirachini, Alejandro
  • Cortés, Cristián E.

Abstract

Microeconomic public transport models aimed at maximizing social benefits usually consider demand in an aggregate manner. In this paper we examine the effect of this approach on the optimal values of frequency and vehicle size by comparison with models where demand is described in detail as a matrix of flows between every station in a single line service. The theoretical analysis and the numerical examples suggest that the spatially aggregated model underestimates optimal frequency and overestimates vehicle size.

Suggested Citation

  • Jara-Díaz, Sergio & Tirachini, Alejandro & Cortés, Cristián E., 2008. "Modeling public transport corridors with aggregate and disaggregate demand," Journal of Transport Geography, Elsevier, vol. 16(6), pages 430-435.
  • Handle: RePEc:eee:jotrge:v:16:y:2008:i:6:p:430-435
    DOI: 10.1016/j.jtrangeo.2008.06.006
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    References listed on IDEAS

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    1. Mohring, Herbert, 1972. "Optimization and Scale Economies in Urban Bus Transportation," American Economic Review, American Economic Association, vol. 62(4), pages 591-604, September.
    2. Oldfield, R. H. & Bly, P. H., 1988. "An analytic investigation of optimal bus size," Transportation Research Part B: Methodological, Elsevier, vol. 22(5), pages 319-337, October.
    3. Sergio Jara-Díaz & Antonio Gschwender, 2003. "Towards a general microeconomic model for the operation of public transport," Transport Reviews, Taylor & Francis Journals, vol. 23(4), pages 453-469, July.
    4. Delle Site, Paolo & Filippi, Francesco, 1998. "Service optimization for bus corridors with short-turn strategies and variable vehicle size," Transportation Research Part A: Policy and Practice, Elsevier, vol. 32(1), pages 19-38, January.
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    Citations

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    Cited by:

    1. Hadas, Yuval & Shnaiderman, Matan, 2012. "Public-transit frequency setting using minimum-cost approach with stochastic demand and travel time," Transportation Research Part B: Methodological, Elsevier, vol. 46(8), pages 1068-1084.
    2. Bahbouh, Kinan & Wagner, James R. & Morency, Catherine & Berdier, Chantal, 2017. "Travel demand corridors: Modelling approach and relevance in the planning process," Journal of Transport Geography, Elsevier, vol. 58(C), pages 196-208.
    3. Herbon, Avi & Hadas, Yuval, 2015. "Determining optimal frequency and vehicle capacity for public transit routes: A generalized newsvendor model," Transportation Research Part B: Methodological, Elsevier, vol. 71(C), pages 85-99.
    4. Liang Gong & Yinzhen Li & Dejie Xu, 2019. "Combinational Scheduling Model Considering Multiple Vehicle Sizes," Sustainability, MDPI, vol. 11(19), pages 1-14, September.
    5. Cortés, Cristián E. & Jara-Díaz, Sergio & Tirachini, Alejandro, 2011. "Integrating short turning and deadheading in the optimization of transit services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(5), pages 419-434, June.
    6. José Vassallo & Floridea Ciommo & Álvaro García, 2012. "Intermodal exchange stations in the city of Madrid," Transportation, Springer, vol. 39(5), pages 975-995, September.
    7. Alejandro Tirachini & Cristián Cortés & Sergio Jara-Díaz, 2011. "Optimal design and benefits of a short turning strategy for a bus corridor," Transportation, Springer, vol. 38(1), pages 169-189, January.

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