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Fare Optimality Analysis of Urban Rail Transit under Various Objective Functions

Author

Listed:
  • Lianbo Deng
  • Zhao Zhang
  • Kangni Liu
  • Wenliang Zhou
  • Junfeng Ma

Abstract

Urban rail transit fare strategies include fare structures and fare levels. We propose a rail transit line fare decision based on an operating plan that falls under elastic demand. Combined with the train operation plan, considering flat fare and distance-based fare, and based on the benefit analysis of both passenger flow and operating enterprises, we construct the objective functions and build an optimization model in terms of the operators’ interests, the system’s efficiency, system regulation goals, and the system costs. The solving algorithm based on the simulated annealing algorithm is established. Using as an example the Changsha Metro Line 2, we analyzed the optimized results of different models under the two fare structures system. Finally the recommendations of fare strategies are given.

Suggested Citation

  • Lianbo Deng & Zhao Zhang & Kangni Liu & Wenliang Zhou & Junfeng Ma, 2014. "Fare Optimality Analysis of Urban Rail Transit under Various Objective Functions," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-8, August.
    • Handle: RePEc:hin:jnddns:910736
    DOI: 10.1155/2014/910736
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    Cited by:

    1. Sun, S. & Szeto, W.Y., 2019. "Optimal sectional fare and frequency settings for transit networks with elastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 127(C), pages 147-177.
    2. Canca, David & Andrade-Pineda, José Luis & De-Los-Santos, Alicia & González-R, Pedro Luis, 2021. "A quantitative approach for the long-term assessment of Railway Rapid Transit network construction or expansion projects," European Journal of Operational Research, Elsevier, vol. 294(2), pages 604-621.
    3. Jin Qin & Wenxuan Qu & Xuanke Wu & Yijia Zeng, 2019. "Differential Pricing Strategies of High Speed Railway Based on Prospect Theory: An Empirical Study from China," Sustainability, MDPI, vol. 11(14), pages 1-17, July.

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