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Article

Spatiotemporal Varying Effects of Built Environment on Taxi and Ride-Hailing Ridership in New York City

1
College of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China
2
Department of Geography and Resource Management and Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong 999077, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(8), 475; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9080475
Received: 18 June 2020 / Revised: 23 July 2020 / Accepted: 27 July 2020 / Published: 29 July 2020
The rapid growth of transportation network companies (TNCs) has reshaped the traditional taxi market in many modern cities around the world. This study aims to explore the spatiotemporal variations of built environment on traditional taxis (TTs) and TNC. Considering the heterogeneity of ridership distribution in spatial and temporal aspects, we implemented a geographically and temporally weighted regression (GTWR) model, which was improved by parallel computing technology, to efficiently evaluate the effects of local influencing factors on the monthly ridership distribution for both modes at each taxi zone. A case study was implemented in New York City (NYC) using 659 million pick-up points recorded by TT and TNC from 2015 to 2017. Fourteen influencing factors from four groups, including weather, land use, socioeconomic and transportation, are selected as independent variables. The modeling results show that the improved parallel-based GTWR model can achieve better fitting results than the ordinary least squares (OLS) model, and it is more efficient for big datasets. The coefficients of the influencing variables further indicate that TNC has become more convenient for passengers in snowy weather, while TT is more concentrated at the locations close to public transportation. Moreover, the socioeconomic properties are the most important factors that caused the difference of spatiotemporal patterns. For example, passengers with higher education/income are more inclined to select TT in the western of NYC, while vehicle ownership promotes the utility of TNC in the middle of NYC. These findings can provide scientific insights and a basis for transportation departments and companies to make rational and effective use of existing resources. View Full-Text
Keywords: geographically and temporally weighted regression; taxi; Uber; spatiotemporal analysis geographically and temporally weighted regression; taxi; Uber; spatiotemporal analysis
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MDPI and ACS Style

Zhang, X.; Huang, B.; Zhu, S. Spatiotemporal Varying Effects of Built Environment on Taxi and Ride-Hailing Ridership in New York City. ISPRS Int. J. Geo-Inf. 2020, 9, 475. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9080475

AMA Style

Zhang X, Huang B, Zhu S. Spatiotemporal Varying Effects of Built Environment on Taxi and Ride-Hailing Ridership in New York City. ISPRS International Journal of Geo-Information. 2020; 9(8):475. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9080475

Chicago/Turabian Style

Zhang, Xinxin, Bo Huang, and Shunzhi Zhu. 2020. "Spatiotemporal Varying Effects of Built Environment on Taxi and Ride-Hailing Ridership in New York City" ISPRS International Journal of Geo-Information 9, no. 8: 475. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9080475

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