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Optimizing Bus Frequencies under Uncertain Demand: Case Study of the Transit Network in a Developing City

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  • Zhengfeng Huang
  • Gang Ren
  • Haixu Liu

Abstract

Various factors can make predicting bus passenger demand uncertain. In this study, a bilevel programming model for optimizing bus frequencies based on uncertain bus passenger demand is formulated. There are two terms constituting the upper-level objective. The first is transit network cost, consisting of the passengers’ expected travel time and operating costs, and the second is transit network robustness performance, indicated by the variance in passenger travel time. The second term reflects the risk aversion of decision maker, and it can make the most uncertain demand be met by the bus operation with the optimal transit frequency. With transit link’s proportional flow eigenvalues (mean and covariance) obtained from the lower-level model, the upper-level objective is formulated by the analytical method. In the lower-level model, the above two eigenvalues are calculated by analyzing the propagation of mean transit trips and their variation in the optimal strategy transit assignment process. The genetic algorithm (GA) used to solve the model is tested in an example network. Finally, the model is applied to determining optimal bus frequencies in the city of Liupanshui, China. The total cost of the transit system in Liupanshui can be reduced by about 6% via this method.

Suggested Citation

  • Zhengfeng Huang & Gang Ren & Haixu Liu, 2013. "Optimizing Bus Frequencies under Uncertain Demand: Case Study of the Transit Network in a Developing City," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-10, May.
  • Handle: RePEc:hin:jnlmpe:375084
    DOI: 10.1155/2013/375084
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    Cited by:

    1. Tao Zhang & Yibo Yan & Qi Chen & Ze Liu, 2022. "Evaluation Method of Composite Development Bus Terminal Using Multi-Source Data Processing," Land, MDPI, vol. 11(10), pages 1-14, October.
    2. Qingyun Tian & Yun Hui Lin & David Z. W. Wang, 2021. "Autonomous and conventional bus fleet optimization for fixed-route operations considering demand uncertainty," Transportation, Springer, vol. 48(5), pages 2735-2763, October.
    3. Tian, Qingyun & Wang, David Z.W. & Lin, Yun Hui, 2021. "Service operation design in a transit network with congested common lines," Transportation Research Part B: Methodological, Elsevier, vol. 144(C), pages 81-102.
    4. Yuan Liu & Heshan Zhang & Tao Xu & Yaping Chen, 2022. "A Heuristic Algorithm Based on Travel Demand for Transit Network Design," Sustainability, MDPI, vol. 14(17), pages 1-17, September.
    5. Tian, Qingyun & Wang, David Z.W. & Lin, Yun Hui, 2022. "Optimal deployment of autonomous buses into a transit service network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 165(C).
    6. David Schmaranzer & Roland Braune & Karl F. Doerner, 2020. "Population-based simulation optimization for urban mass rapid transit networks," Flexible Services and Manufacturing Journal, Springer, vol. 32(4), pages 767-805, December.

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