Predicting disruptions and their passenger delay impacts for public transport stops

Dados Bibliográficos

ID19337535
AutoresMenno Yap, M D Yap (0000-0002-0508-6064, Delft University of Technology, autor correspondente), Oded Cats (0000-0002-4506-0459, Delft University of Technology)
Ano2021
Volume48
Fascículo4
Páginas1703-1731
Data de publicação2021-08-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoTransportation (JOURNAL)
Identificadores do periódicoISSN: 0049-4488 • E-ISSN: 1572-9435
EditoraSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s11116-020-10109-9
OpenAlexW3021010560
IdiomaEN
Citações recebidas5
Referências citadas23

Disruptions in public transport can have major implications for passengers and service providers. Our study objective is to develop a generic approach to predict how often different disruption types occur at different stations of a public transport network, and to predict the impact related to these disruptions as measured in terms of passenger delays. We propose a supervised learning approach to perform these predictions, as this allows for predictions for individual stations for each time period, without the requirement of having sufficient empirical disruption observations available for each location and time period. This approach also enables a fast prediction of disruption impacts for a large number of disruption instances, hence addressing the computational challenges that rise when typical public transport assignment or simulation models would be used for real-world public transport networks. To improve transferability of our study results, we cluster stations based on their contribution to network vulnerability using unsupervised learning. This supports public transport agencies to apply the appropriate type of measure aimed to reduce disruptions or to mitigate disruption impacts for each station type. Applied to the Washington metro network, we predict a yearly passenger delay of 5.9 million hours for the total metro network. Based on the clustering, five different types of station are distinguished. Stations with high train frequencies and high passenger volumes located at central trunk sections of the network show to be most critical, along with start/terminal and transfer stations. Intermediate stations located at branches of a line are least critical

Cluster analysis · Computer security · Machine learning · Operations research · Public transport · Transferability · Transport engineering · Vulnerability (computing) · Artificial Intelligence · Computer Science · Engineering · Traffic Prediction and Management Techniques · Transportation Planning and Optimization · Urban Transport and Accessibility