How Determinants Affect Transfer Ridership between Metro and Bus Systems: A Multivariate Generalized Poisson Regression Analysis Method

Pan Wu, Jinlong Li, Yuzhuang Pian, Xiaochen Li (), Zilin Huang, Lunhui Xu, Guilin Li and Ruonan Li
Additional contact information
Pan Wu: Department of Civil and Transportation Engineering, South China University of Technology, Guangzhou 510641, China
Jinlong Li: Department of Civil and Transportation Engineering, South China University of Technology, Guangzhou 510641, China
Yuzhuang Pian: School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510006, China
Xiaochen Li: Department of Civil and Transportation Engineering, South China University of Technology, Guangzhou 510641, China
Zilin Huang: Center for Connected and Automated Transportation (CCAT), Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
Lunhui Xu: Department of Civil and Transportation Engineering, South China University of Technology, Guangzhou 510641, China
Guilin Li: Chongqing Dajiang-Jiexin Forging Co., Ltd., Chongqing 401321, China
Ruonan Li: School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China

Sustainability, 2022, vol. 14, issue 15, 1-31

Abstract: Understanding the determinants of transfer ridership is important for providing insights into improving the attractiveness of transit systems and building reliable and resilient metro stations. This study focuses on the transfer ridership between bus and metro systems under different dates and severe weather conditions to quantify the impacts of various attributes on the transfer ridership of different transfer modes (metro-to-bus and bus-to-metro). A multivariate generalized Poisson regression (GPR) model is applied to investigate the effects of critical factors on the transfer ridership of different transfer modes on weekdays, holidays, and typhoon days, respectively. The results indicate that the transfer-related variables, real-time weather, socioeconomic characteristics, and built environment significantly affect the transfer ridership. Concretely, the influence of socioeconomic and demographic factors on transfer ridership is the most significant on different types of dates, which is approximately 1.19 to 9.28 times that of the other variables. Weather variables have little effect on transfer ridership on weekdays, but they have a more significant impact on the transfer ridership on holidays and typhoon days. Specifically, during typhoons, transfer ridership is more affected by the weather factors: the coefficients are about 2.36 to 4.74 times higher than that in the other periods. Moreover, under strong wind speed, heavy rain, and high-temperature conditions, transfer ridership of the metro-to-bus mode significantly increases. In contrast, transfer ridership of the bus-to-metro mode rapidly decreases. Additionally, the peak hours have a strong positive influence on the transfer ridership, and the average hourly transfer ridership during peak hours is 1.16 to 4.02 times higher than that during the other periods. These findings indicate that the effect of each factor on transfer ridership varies with dates and transfer modes. This can also provide support for improving metro stations and increasing the attractiveness of public transport.

Keywords: metro and bus system; transfer ridership; generalized Poisson regression model; intelligent transportation systems; connectivity of different transportation systems; statistical analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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