Investigation of Passengers’ Perceived Transfer Distance in Urban Rail Transit Stations Using XGBoost and SHAP

Abstract

Providing high-quality public transport services and enhancing passenger experiences require efficient urban rail transit connectivity; however, passengers’ perceived transfer distance at urban rail transit stations may differ from the actual transfer distance, resulting in inconvenience and dissatisfaction. To address this issue, this study proposed a novel machine learning framework that measured the perceived transfer distance in urban rail transit stations and analyzed the significance of each influencing factor. The framework introduced the Ratio of Perceived Transfer Distance Deviation (R), which was evaluated using advanced XGBoost and SHAP models. To accurately evaluate R, the proposed framework considered 32 indexes related to passenger personal attributes, transfer facilities, and transfer environment. The study results indicated that the framework based on XGBoost and SHAP models can effectively measure the R of urban rail transit passengers. Key factors that affected R included the Rationality of Signs and Markings, Ratio of Escalators Length, Rationality of Traffic Organization outside The Station, Ratio of Stairs Length, and Degree of Congestion on Passageways. These findings can provide valuable theoretical references for designing transfer facilities and improving transfer service levels in urban rail transit stations.