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Optimal Public Transport Pricing: Towards an Agent-based Marginal Social Cost Approach

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  • Ihab Kaddoura
  • Benjamin Kickhöfer
  • Andreas Neumann
  • Alejandro Tirachini

Abstract

In this paper, marginal cost-pricing rules are applied to an agent-based model. User-specific optimal bus fares are estimated by simulating user interactions at a microscopic level. We consider external effects of prolonged in-vehicle and waiting times (including the effect of full buses on boarding denials) induced by passengers boarding vehicles. We give a new-look to the relationship between optimal fare and travel distance, which is found to depend crucially on the type of external cost that is considered when calculating fares. Conditions for optimal bus fares that increase or decrease as a function of trip length are numerically found. © 2015 LSE and the University of Bath

Suggested Citation

  • Ihab Kaddoura & Benjamin Kickhöfer & Andreas Neumann & Alejandro Tirachini, 2015. "Optimal Public Transport Pricing: Towards an Agent-based Marginal Social Cost Approach," Journal of Transport Economics and Policy, University of Bath, vol. 49(2), pages 200-218, April.
    • Handle: RePEc:tpe:jtecpo:v:49:y:2015:i:2:p:200-218
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    Cited by:

    1. Adnan, Muhammad & Nahmias Biran, Bat-hen & Baburajan, Vishnu & Basak, Kakali & Ben-Akiva, Moshe, 2020. "Examining impacts of time-based pricing strategies in public transportation: A study of Singapore," Transportation Research Part A: Policy and Practice, Elsevier, vol. 140(C), pages 127-141.
    2. Hörcher, Daniel & Tirachini, Alejandro, 2021. "A review of public transport economics," Economics of Transportation, Elsevier, vol. 25(C).
    3. Ihab Kaddoura & Kai Nagel, 2018. "Simultaneous internalization of traffic congestion and noise exposure costs," Transportation, Springer, vol. 45(5), pages 1579-1600, September.
    4. Antonín Pavlíček & František Sudzina, 2020. "Intergroup Comparison of Personalities in the Preferred Pricing of Public Transport in Rush Hours: Data Revisited," Sustainability, MDPI, vol. 12(12), pages 1-9, June.
    5. Conny Grunicke & Jan Christian Schluter & Jani-Pekka Jokinen, 2020. "Implementation of a cost-benefit analysis of Demand-Responsive Transport with a Multi-Agent Transport Simulation," Papers 2011.12869, arXiv.org, revised Mar 2021.
    6. Xudong Li & Zhongzhen Yang & Feng Lian, 2023. "Optimizing On-Demand Bus Services for Remote Areas," Sustainability, MDPI, vol. 15(9), pages 1-20, April.

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