Quantifying the cascading effects of passenger delays. (August 2021)

• Record Type: Journal Article
• Authors: Cats, Oded; Hijner, Anne Mijntje
• Is Part Of: Reliability engineering & system safety. Volume 212(2021)
• Publication Date: 2021-08
• Subjects: Network reliability -- Bayesian network -- Passenger delay -- Informativity Indicators -- Cascading; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2021.107629
• Languages: English
• ISSNs: 0951-8320

Abstract

Highlights: Analysing network-wide passenger delays by estimating a Bayesian Network. Original informativity indicators for quantifying the spatial extent of delays. Empirically examine network-wide relations among metro passenger delays. Analyze relation between informativity metrics and network topological properties. Few stations are directly informative of delays occurring at many other stations. Abstract: Delays in transport networks has adverse implications for infrastructure and service managers as well as travellers. While it is widely acknowledged that delays occurring across the transport networks may be related, there are is lack of knowledge on the underlying properties of these relations and means for quantifying them. To this end, we develop a network-wide data-driven delay analysis method. First, we construct a Bayesian network to represent the relations between delays associated with different transport network elements and assess the reliability of critical infrastructure elements. Second, we propose a series of original metrics denominated informativity indicators for quantifying the spatial extent of the delays observed based on the Bayesian network obtained. The proposed approach is applied to the Washington DC metro network. Time-dependent passenger waiting and transferring delays inferred over more than a year from smartcard data are utilized as input to the Bayesian network. Our findings indicate that passenger delays at few selected stations are directly informative of delays occurring at many other stations. We also examine the relation between the proposed informativity metrics and the topological properties of metro stations, concluding that the latter have a limited value in approximating network-wide delay correlations.