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A Bus Network Design Model under Demand Variation: A Case Study of the Management of Rome’s Bus Network

Author

Listed:
  • Andrea Gemma

    (Department of Civil, Computer Science and Aeronautical Technologies Engineering, Roma Tre University, 00146 Rome, Italy)

  • Ernesto Cipriani

    (Department of Civil, Computer Science and Aeronautical Technologies Engineering, Roma Tre University, 00146 Rome, Italy)

  • Umberto Crisalli

    (Department of Enterprise Engineering, Tor Vergata University of Rome, 00133 Rome, Italy)

  • Livia Mannini

    (Department of Civil, Computer Science and Aeronautical Technologies Engineering, Roma Tre University, 00146 Rome, Italy)

  • Marco Petrelli

    (Department of Civil, Computer Science and Aeronautical Technologies Engineering, Roma Tre University, 00146 Rome, Italy)

Abstract

This paper proposed a methodology to design bus transit networks that can be consistently adjusted according to demand variations both in level and distribution. The methodology aims to support the activities of service providers in optimizing the service capacity of the bus network according to a system-wide analysis. It stems from the changes imposed by the COVID-19 pandemic. Such an experience has imposed a rethinking of the methodology used for the optimal design of robust transit network services that are easy-to-adapt to demand variations without redesigning the whole network every time. Starting from an existing model, this design methodology is articulated in two parts: the first part for solving the problem with the maximum level of transit demand, aiming at giving an upper bound to the solution, and the second part, where the network is optimized for other specific transit demands. This method has been applied to a real context in the city of Rome, considering two levels of demand taken from COVID-19 experiences. They are characterized by the application of different policies regarding different timings for shopping and schools’ openings as well as by policies on smart working. The results show the effectiveness of the proposed methodology to design robust transit networks suited to comply with large demand variations. Moreover, the procedure is suitable and easy to implement, in order to adapt quickly to changes in demand without having to modify line routes, but adapting them in an optimal way, even when dealing with realistic-sized transit networks.

Suggested Citation

  • Andrea Gemma & Ernesto Cipriani & Umberto Crisalli & Livia Mannini & Marco Petrelli, 2024. "A Bus Network Design Model under Demand Variation: A Case Study of the Management of Rome’s Bus Network," Sustainability, MDPI, vol. 16(2), pages 1-13, January.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:2:p:803-:d:1320855
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    References listed on IDEAS

    as
    1. Spiess, Heinz & Florian, Michael, 1989. "Optimal strategies: A new assignment model for transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 23(2), pages 83-102, April.
    2. Szeto, W.Y. & Jiang, Y., 2014. "Transit route and frequency design: Bi-level modeling and hybrid artificial bee colony algorithm approach," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 235-263.
    3. Farahani, Reza Zanjirani & Miandoabchi, Elnaz & Szeto, W.Y. & Rashidi, Hannaneh, 2013. "A review of urban transportation network design problems," European Journal of Operational Research, Elsevier, vol. 229(2), pages 281-302.
    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. Javier Durán-Micco & Pieter Vansteenwegen, 2022. "A survey on the transit network design and frequency setting problem," Public Transport, Springer, vol. 14(1), pages 155-190, March.
    6. Wang, David Z.W. & Lo, Hong K., 2010. "Global optimum of the linearized network design problem with equilibrium flows," Transportation Research Part B: Methodological, Elsevier, vol. 44(4), pages 482-492, May.
    7. An, Kun & Lo, Hong K., 2016. "Two-phase stochastic program for transit network design under demand uncertainty," Transportation Research Part B: Methodological, Elsevier, vol. 84(C), pages 157-181.
    8. G. F. Newell, 1979. "Some Issues Relating to the Optimal Design of Bus Routes," Transportation Science, INFORMS, vol. 13(1), pages 20-35, February.
    9. Erfan Hassannayebi & Seyed Hessameddin Zegordi & Mohammad Reza Amin-Naseri & Masoud Yaghini, 2018. "Optimizing headways for urban rail transit services using adaptive particle swarm algorithms," Public Transport, Springer, vol. 10(1), pages 23-62, May.
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