Understanding passenger travel choice behaviours under train delays in urban rail transits: a data-driven approach
Document Type
Journal Article
Publication Date
2023
Subject Area
place - asia, place - urban, mode - rail, mode - subway/metro, ridership - behaviour, ridership - modelling
Keywords
Urban rail transit, travel choice behaviours, train delays, data-driven
Abstract
The analysis of passenger travel choice behaviours under train delays has become a crucial topic in research on urban rail transit operation management. In this paper, we focus on analysing travel choices of affected regular passengers under train delays by utilizing the data collected through an automatic fare collection (AFC) system along with train delay log records. Along this line, we propose a data-driven four-stage framework for studying regular passengers’ responses under delays, consisting of data profiling, regular passenger screening and travel patterns extraction, affected regular passenger identification, and affected passenger behaviour prediction modelling. Using a real-world case of the Shenzhen Metro in China, we conduct extensive experiments for method validation and feature insights analysis. The proposed framework could provide a microscopic view of passenger travel behaviours under train delays for fine prediction and exhibit a possibility for multi-source heterogeneous data mining in passenger behaviour analysis and train delay-related tasks.
Rights
Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.
Recommended Citation
Chen, E., Luo, Q., Chen, J., & He, Y. (2023). Understanding passenger travel choice behaviours under train delays in urban rail transits: a data-driven approach. Transportmetrica B: Transport Dynamics, 11(1), 1496-1524.
