Understanding passenger travel choice behaviours under train delays in urban rail transits: a data-driven approach

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.

Automated summary

The analysis of passenger travel choice behaviours under train delays is crucial for urban rail transit operation management. This paper analyzes the travel choices of affected regular passengers using data collected through an automatic fare collection (AFC) system and train delay log records. A data-driven four-stage framework is proposed for studying regular passengers' responses under delays, including data profiling, regular passenger screening, affected regular passenger identification, and affected passenger behaviour prediction modeling. Experiments conducted using the Shenzhen Metro in China validate the proposed framework and provide insights for analyzing passenger behavior and train delay-related tasks through multi-source heterogeneous data mining.