Predicting Station-Level Peak Hour Ridership of Metro Considering the Peak Deviation Coefficient

Abstract

Subway station-level peak hour ridership (SPR) is a crucial input parameter for multiple applications, including the planning, design, construction, and operation of stations. However, traditional SPR estimation techniques may produce biased results. A unified peak hour factor (PHF) extracted from the line level is generally set for all attributed stations, which ignores the possible peak deviation that arises between the station and line and the wide variation of PHFs in practice. This study presents a comprehensive and refined estimation framework for SPR that accommodates the peak deviation context by introducing the peak deviation coefficient (PDC). Moreover, the estimation of the PDC and PHF variability is improved by constructing spatial regression based relationship models. The empirical results show that the proposed approach exhibits wider applicability and a higher prediction precision across all types of peak periods considered as compared to conventional methods (i.e., MAPE decreases of 0.115–0.351). The findings demonstrate the importance of the consideration of the peak deviation scenario and the spatial dependency in SPR estimation to achieve better decision making. Moreover, the underlying influencing mechanism of the PHF and PDC at distinct peak periods is further revealed using the spatial model. This provides critical theoretical references and policy implications to prudently deploy land-use resources to balance the travel demand between peak and off-peak periods and thus enhance the line operation efficiency.