A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership. (July 2018)

• Record Type: Journal Article
• Authors: Ma, Xiaolei; Zhang, Jiyu; Ding, Chuan; Wang, Yunpeng
• Is Part Of: Computers, environment and urban systems. Volume 70(2018)
• Page Start: 113
• Page End: 124
• Publication Date: 2018-07
• Subjects: Transit ridership -- Built environment -- Spatio-temporal analysis -- Traffic analysis zone; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2018.03.001
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: Understanding the influence of the built environment on transit ridership can provide transit authorities with insightful information for operation management and policy making, and ultimately, increase the attractiveness of public transportation. Existing studies have resorted to either traditional ordinary least squares (OLS) regression or geographically weighted regression (GWR) to unravel the complex relationship between ridership and the built environment. Time is a critical dimension that traditional GWR cannot recognize well when performing spatiotemporal analysis on transit ridership. This study addressed this issue by introducing temporal variation into traditional GWR and leveraging geographically and temporally weighted regression (GTWR) to explore the spatiotemporal influence of the built environment on transit ridership. An empirical study conducted in Beijing using one-month transit smart card and point-of-interest data at the traffic analysis zone (TAZ) level demonstrated the effectiveness of GTWR. Compared with those of the traditional OLS and GWR models, a significantly better goodness-of-fit was observed for GTWR. Moreover, the spatiotemporal pattern of coefficients was further analyzed in several TAZs with typical land use types, thereby highlighting the importance of temporal features in spatiotemporal data. Transit authorities can develop transit planning and traffic demand management policies with improved accuracy by utilizing the enhanced precision and spatiotemporal modeling of GTWR to alleviate urban traffic problems. Highlights: Spatiotemporal influence of the built environment on hourly transit ridership was explored. Geographically and temporally weighted regression model was applied. Hourly transit ridership was obtained by smart card data from one month. Built environment variables were calculated for each traffic analysis zone.