Spatial Variation of Taxi Demand Using GPS Trajectories and POI Data
Taxi as a door-to-door, all-weather way of travel is an important part of the urban transportation system. A fundamental understanding of temporal-spatial variation and its related influential factors are essential for taxi regulation and urban planning. In this paper, we explore the correlation bet...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/7621576 |
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| author | Xinmin Liu Lu Sun Qiuxia Sun Ge Gao |
| author_facet | Xinmin Liu Lu Sun Qiuxia Sun Ge Gao |
| author_sort | Xinmin Liu |
| collection | DOAJ |
| description | Taxi as a door-to-door, all-weather way of travel is an important part of the urban transportation system. A fundamental understanding of temporal-spatial variation and its related influential factors are essential for taxi regulation and urban planning. In this paper, we explore the correlation between taxi demand and socio-economic, transport system and land use patterns based on taxi GPS trajectory and POI (point of interest) data of Qingdao City. The geographically weighted regression (GWR) model is used to describe the influence factors of spatial heterogeneity of the taxi demand and visualize the spatial distributions of parameter estimations. Results indicate that during the peak hours, there are some differences in taxi demand between workdays and weekends. Residential density and housing prices increase the number of taxi trips. Road density, parking lot density and bus station density are positively associated with the taxi demand. It is also found that the higher of the proportion of commercial area and public service area, the greater of the taxi demand, while the proportion of residential area and the land use mix have a negative impact on taxi demand. This paper provides some references for understanding the internal urban environmental factors generating from the taxi travel demand, and provides insights for reducing the taxi vacancy rate, forecasting taxi temporal-spatial demand and urban public transportation system planning. |
| format | Article |
| id | doaj-art-10c121e9fb514bf89544ea8aa38fe823 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-10c121e9fb514bf89544ea8aa38fe8232025-02-03T01:25:26ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/76215767621576Spatial Variation of Taxi Demand Using GPS Trajectories and POI DataXinmin Liu0Lu Sun1Qiuxia Sun2Ge Gao3College of Economics and Management, Shandong University of Science and Technology, Qingdao, ChinaCollege of Economics and Management, Shandong University of Science and Technology, Qingdao, ChinaCollege of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, ChinaCollege of Economics and Management, Shandong University of Science and Technology, Qingdao, ChinaTaxi as a door-to-door, all-weather way of travel is an important part of the urban transportation system. A fundamental understanding of temporal-spatial variation and its related influential factors are essential for taxi regulation and urban planning. In this paper, we explore the correlation between taxi demand and socio-economic, transport system and land use patterns based on taxi GPS trajectory and POI (point of interest) data of Qingdao City. The geographically weighted regression (GWR) model is used to describe the influence factors of spatial heterogeneity of the taxi demand and visualize the spatial distributions of parameter estimations. Results indicate that during the peak hours, there are some differences in taxi demand between workdays and weekends. Residential density and housing prices increase the number of taxi trips. Road density, parking lot density and bus station density are positively associated with the taxi demand. It is also found that the higher of the proportion of commercial area and public service area, the greater of the taxi demand, while the proportion of residential area and the land use mix have a negative impact on taxi demand. This paper provides some references for understanding the internal urban environmental factors generating from the taxi travel demand, and provides insights for reducing the taxi vacancy rate, forecasting taxi temporal-spatial demand and urban public transportation system planning.http://dx.doi.org/10.1155/2020/7621576 |
| spellingShingle | Xinmin Liu Lu Sun Qiuxia Sun Ge Gao Spatial Variation of Taxi Demand Using GPS Trajectories and POI Data Journal of Advanced Transportation |
| title | Spatial Variation of Taxi Demand Using GPS Trajectories and POI Data |
| title_full | Spatial Variation of Taxi Demand Using GPS Trajectories and POI Data |
| title_fullStr | Spatial Variation of Taxi Demand Using GPS Trajectories and POI Data |
| title_full_unstemmed | Spatial Variation of Taxi Demand Using GPS Trajectories and POI Data |
| title_short | Spatial Variation of Taxi Demand Using GPS Trajectories and POI Data |
| title_sort | spatial variation of taxi demand using gps trajectories and poi data |
| url | http://dx.doi.org/10.1155/2020/7621576 |
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