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Identification and interpretation of spatial–temporal mismatch between taxi demand and supply using global positioning system data
| 题名： | Identification and interpretation of spatial–temporal mismatch between taxi demand and supply using global positioning system data |
| 正文语种： | 英文 |
| 作者： | Juanyu Tang, Yi Zhu, Yizhe Huang, Zhong-Ren Peng; Zhanyong Wang |
| 作者单位： | Center for Intelligent Transportation Systems and Unmanned Aerial Systems Applications, State Key Laboratory of Ocean Engineering, School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai, China; School of Public Economics and Administration, Shanghai University of Finance and Economics, Shanghai, China; China Institute for Urban Governance, Shanghai Jiao Tong University, Shanghai, China; School of Landscape Architecture and Planning, College of Design, Construction, and Planning, University of Florida, International Center for Adaptation Planning and Design (iAdapt), Gainesville, FL, USA; College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou, China |
| 关键词： | Demand and supply mismatch; global positioning system data; multinomial logistic regression; spatio–temporal distribution; taxi service |
| 摘要： | Taxis provide a type of on-demand mobility service in urban transportation. There are growing concerns regarding the level of service of taxis in satisfying residents’ mobility demand. However, the mismatch between demand and supply of taxi services has not been resolved even with the introduction of app-based vehicle services. In this study, three indicators are constructed using the Shanghai taxi global positioning system (GPS) dataset to reveal the spatial–temporal mismatch pattern. The issue is further analyzed via a case study, in which seven regions with frequent mismatch patterns are identified in the time period from 21:00 to 22:00. Then, a multinomial logistic regression model is employed to identify the demographic and built-environment factors contributing to the taxi mismatch problem. The study results show that population density, residential areas, the number of points of interest, and road density have significant relationships with the taxi undersupply. In contrast, the areas of commercial lands, as well as the other two transportation-related factors, that is, the number of bus stops and the distance to nearest subway station, are observed to be statistically significant for both “oversupply” and “undersupply” conditions. This study provides valuable insights for identifying mismatch patterns and interpreting the mismatch problem as a function of spatially explicit factors that are of great use for urban governance, especially in the improvement of taxi efficiency, taxi management, and urban planning. |
| 出版年： | 2019 |
| 期刊名称： | Journal of Intelligent Transportation Systems Technology Planning and Operations |
| 卷： | 23 |
| 期： | 4 |
| 页码： | 403-415 |
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