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Topological properties of bus transit networks considering demand and service utilization weight measures

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  • Manjalavil, Manju Manohar
  • Ramadurai, Gitakrishnan

Abstract

We present a complex weighted network analysis of a bus transport network. We model the network as graphs in L-space and P-space and evaluate the statistical properties in unweighted and weighted cases. The weights considered include number of overlapping routes and passenger demand between two bus stops. We also introduce a new supply-based edge weight called Service Utilization Factor (SUF) and define it as the passenger demand per service between two stops. We extract the origin and destination of the passenger trips from bus ticket information. In the bus system under study, the tickets are issued between ’stages’ instead of between bus stops. We propose a points of interest based procedure to map the passenger demand between stages to between bus stops. The network has scale-free behaviour with preferential attachment of nodes and is similar to small-world networks. It is well-connected with an average of 1.4 transfers required to travel between any two points in the network. We identify redundant routes in the network from strength values in the SUF weighted networks. Disassortativity in the demand weighted network indicated that bus stops with higher attractiveness are not necessarily situated contiguously along a route. The SUF weighted network is assortative indicating presence of well-serviced stops along the same the route. We identified the stops with higher betweenness centrality, that can be ideal candidates for being developed as hubs. Our conclusions exemplify the importance of considering route overlaps, passenger demand and service utilization in bus network analysis and for building an efficient bus network.

Suggested Citation

  • Manjalavil, Manju Manohar & Ramadurai, Gitakrishnan, 2020. "Topological properties of bus transit networks considering demand and service utilization weight measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 555(C).
  • Handle: RePEc:eee:phsmap:v:555:y:2020:i:c:s037843712030337x
    DOI: 10.1016/j.physa.2020.124683
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    1. Geng, Sunyue & Liu, Sifeng & Fang, Zhigeng, 2022. "An agent-based algorithm for dynamic routing in service networks," European Journal of Operational Research, Elsevier, vol. 303(2), pages 719-734.
    2. Sun, Qipeng & He, Chen & Wang, Yongjie & Liu, Hang & Ma, Fei & Wei, Xiao, 2022. "Reducing violation behaviors of pedestrians considering group interests of travelers at signalized crosswalk," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    3. Abdelaty, Hatem & Mohamed, Moataz & Ezzeldin, Mohamed & El-Dakhakhni, Wael, 2022. "Temporal robustness assessment framework for city-scale bus transit networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    4. Wang, Ying & Zhao, Ou & Zhang, Limao, 2024. "Modeling urban rail transit system resilience under natural disasters: A two-layer network framework based on link flow," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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