Understanding City-Wide Ride-Sourcing Travel Flow: A Geographically Weighted Regression Approach
The emerging ride-sourcing service has become an important element of urban mobility. A challenging question underlying the provision of such service is how and to what extent the built environment affects origin-destination (OD) travel flows. This paper employs the geographically weighted regressio...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/9929622 |
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| author | Chuqiao Chen Simon Hu Washington Y. Ochieng Na Xie Xiqun (Michael) Chen |
| author_facet | Chuqiao Chen Simon Hu Washington Y. Ochieng Na Xie Xiqun (Michael) Chen |
| author_sort | Chuqiao Chen |
| collection | DOAJ |
| description | The emerging ride-sourcing service has become an important element of urban mobility. A challenging question underlying the provision of such service is how and to what extent the built environment affects origin-destination (OD) travel flows. This paper employs the geographically weighted regression (GWR) model to analyze the OD-based ride-sourcing travel flow. It makes a comparison with the existing ordinary least square (OLS) model and spatial autocorrelation model (SAM). We have collected ride-sourcing order data in Hangzhou, China, to provide an accurate source for acquiring ride-sourcing travel flow. We investigate the effects of the residential area, points of interest (POIs), and transit stations on ride-sourcing travel flow among traffic analysis zones (TAZs). The results show the following: (a) GWR has better goodness-of-fit than SAM and OLS. (b) Residential area, enterprise, and bus stations have positive correlations with ride-sourcing OD flows, but education and subway stations have negative correlations. We have further investigated the issue and found that it is not a causal relationship between the bus station and OD flow, due to collinearity between the two variables. The bus station builds on locations with high demand, but its capacity is not large enough to reduce the ride-sourcing flow to a low level, which results in a positive coefficient. (c) Based on the estimated coefficients, the prediction of ride-sourcing flows is feasible, supporting the impact analysis for urban land use and transportation planning. This paper contributes to understanding OD-based ride-sourcing travel flow distributions and provides a framework of long-term OD flow prediction for urban land use and transportation planning. |
| format | Article |
| id | doaj-art-b4c116d30b404b0bad34368ca0332735 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-b4c116d30b404b0bad34368ca03327352025-02-03T06:05:33ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/99296229929622Understanding City-Wide Ride-Sourcing Travel Flow: A Geographically Weighted Regression ApproachChuqiao Chen0Simon Hu1Washington Y. Ochieng2Na Xie3Xiqun (Michael) Chen4College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, ChinaZJU-UIUC Institute, Zhejiang University, Haining 314400, ChinaDepartment of Civil and Environmental Engineering, Imperial College London, South Kensington Campus, London SW72AZ, UKSchool of Management Science and Engineering, Central University of Finance and Economics, Beijing 100081, ChinaCollege of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, ChinaThe emerging ride-sourcing service has become an important element of urban mobility. A challenging question underlying the provision of such service is how and to what extent the built environment affects origin-destination (OD) travel flows. This paper employs the geographically weighted regression (GWR) model to analyze the OD-based ride-sourcing travel flow. It makes a comparison with the existing ordinary least square (OLS) model and spatial autocorrelation model (SAM). We have collected ride-sourcing order data in Hangzhou, China, to provide an accurate source for acquiring ride-sourcing travel flow. We investigate the effects of the residential area, points of interest (POIs), and transit stations on ride-sourcing travel flow among traffic analysis zones (TAZs). The results show the following: (a) GWR has better goodness-of-fit than SAM and OLS. (b) Residential area, enterprise, and bus stations have positive correlations with ride-sourcing OD flows, but education and subway stations have negative correlations. We have further investigated the issue and found that it is not a causal relationship between the bus station and OD flow, due to collinearity between the two variables. The bus station builds on locations with high demand, but its capacity is not large enough to reduce the ride-sourcing flow to a low level, which results in a positive coefficient. (c) Based on the estimated coefficients, the prediction of ride-sourcing flows is feasible, supporting the impact analysis for urban land use and transportation planning. This paper contributes to understanding OD-based ride-sourcing travel flow distributions and provides a framework of long-term OD flow prediction for urban land use and transportation planning.http://dx.doi.org/10.1155/2021/9929622 |
| spellingShingle | Chuqiao Chen Simon Hu Washington Y. Ochieng Na Xie Xiqun (Michael) Chen Understanding City-Wide Ride-Sourcing Travel Flow: A Geographically Weighted Regression Approach Journal of Advanced Transportation |
| title | Understanding City-Wide Ride-Sourcing Travel Flow: A Geographically Weighted Regression Approach |
| title_full | Understanding City-Wide Ride-Sourcing Travel Flow: A Geographically Weighted Regression Approach |
| title_fullStr | Understanding City-Wide Ride-Sourcing Travel Flow: A Geographically Weighted Regression Approach |
| title_full_unstemmed | Understanding City-Wide Ride-Sourcing Travel Flow: A Geographically Weighted Regression Approach |
| title_short | Understanding City-Wide Ride-Sourcing Travel Flow: A Geographically Weighted Regression Approach |
| title_sort | understanding city wide ride sourcing travel flow a geographically weighted regression approach |
| url | http://dx.doi.org/10.1155/2021/9929622 |
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