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作者

Pengyu Zhu;Jie Huang;Jiaoe Wang;Yu Liu;Jiarong Li;Mingshu Wang;Wei Qiang

来源

CITIES,Vol.125,Issue1,Article 103637

语言

英文

关键字

Taxi demand;Spatial econometric model;Spatial autocorrelation;Big data;Built environment

作者单位

Division of Public Policy, The Hong Kong University of Science and Technology, China;Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China;School of Geographical & Earth Sciences, University of Glasgow, Glasgow, G12 8QQ, United Kingdom;Department of Geography and Resource Management, The Chinese University of Hong Kong, China;Division of Public Policy, The Hong Kong University of Science and Technology, China;Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China;Institute of Remote Sensing and Geographical Information Systems, School of Earth and Space Sciences, Peking University, Beijing 100871, China;School of Geographical & Earth Sciences, University of Glasgow, Glasgow, G12 8QQ, United Kingdom;Department of Geography and Resource Management, The Chinese University of Hong Kong, China

摘要

In urban transportation systems, taxis are regarded as flexible, convenient, and time-saving. Taxi demand is affected by various built-environment factors and by the time of the day. Although many studies have investigated correlations between taxi demand and the built environment, the direct and spillover effects of built environment factors on taxi demand have not been examined at a fine spatial scale. To address this gap in the literature, this paper employs spatial econometric models using GPS-tracked taxi trips, mobile signaling data, and points of interest (POIs) to study taxi demand in Beijing at a 1-kilometer square grid resolution. The results show that, in the morning and evening peak hours, road network density has the strongest (positive) direct and indirect impact on taxi ridership. A relationship is also found between public transportation and taxi ridership: bus coverage has positive direct effects and insignificant indirect effects on taxi pick-ups and drop-offs, while subway coverage has negative indirect effects, suggesting that it may absorb taxi demand from surrounding grids. Results also indicate that various built-environment factors affect taxi demand differently at morning and evening peak times. This study reveals the complex nature of taxi ridership and has important implications for policymakers, transport planners, and other stakeholders in megacities around the world.