Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model
Much of the taxi route-planning literature has focused on driver strategies for finding passengers and determining the hot spot pick-up locations using historical global positioning system (GPS) trajectories of taxis based on driver experience, distance from the passenger drop-off location to the ne...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-11-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/6/11/373 |
| _version_ | 1819013794078130176 |
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| author | Liang Wu Sheng Hu Li Yin Yazhou Wang Zhanlong Chen Mingqiang Guo Hao Chen Zhong Xie |
| author_facet | Liang Wu Sheng Hu Li Yin Yazhou Wang Zhanlong Chen Mingqiang Guo Hao Chen Zhong Xie |
| author_sort | Liang Wu |
| collection | DOAJ |
| description | Much of the taxi route-planning literature has focused on driver strategies for finding passengers and determining the hot spot pick-up locations using historical global positioning system (GPS) trajectories of taxis based on driver experience, distance from the passenger drop-off location to the next passenger pick-up location and the waiting times at recommended locations for the next passenger. The present work, however, considers the average taxi travel speed mined from historical taxi GPS trajectory data and the allocation of cruising routes to more than one taxi driver in a small-scale region to neighboring pick-up locations. A spatio-temporal trajectory model with load balancing allocations is presented to not only explore pick-up/drop-off information but also provide taxi drivers with cruising routes to the recommended pick-up locations. In simulation experiments, our study shows that taxi drivers using cruising routes recommended by our spatio-temporal trajectory model can significantly reduce the average waiting time and travel less distance to quickly find their next passengers, and the load balancing strategy significantly alleviates road loads. These objective measures can help us better understand spatio-temporal traffic patterns and guide taxi navigation. |
| first_indexed | 2024-12-21T02:05:36Z |
| format | Article |
| id | doaj.art-0d9d4c53a221485c8e8868a310376f46 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-12-21T02:05:36Z |
| publishDate | 2017-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-0d9d4c53a221485c8e8868a310376f462022-12-21T19:19:31ZengMDPI AGISPRS International Journal of Geo-Information2220-99642017-11-0161137310.3390/ijgi6110373ijgi6110373Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory ModelLiang Wu0Sheng Hu1Li Yin2Yazhou Wang3Zhanlong Chen4Mingqiang Guo5Hao Chen6Zhong Xie7School of Information Engineering, China University of Geosciences, Wuhan 430074, ChinaSchool of Information Engineering, China University of Geosciences, Wuhan 430074, ChinaDepartment of Urban and Regional Planning, State University of New York, Buffalo, NY 14214, USANational Engineering Research Center for GIS, Wuhan 430074, ChinaSchool of Information Engineering, China University of Geosciences, Wuhan 430074, ChinaSchool of Information Engineering, China University of Geosciences, Wuhan 430074, ChinaNational Engineering Research Center for GIS, Wuhan 430074, ChinaSchool of Information Engineering, China University of Geosciences, Wuhan 430074, ChinaMuch of the taxi route-planning literature has focused on driver strategies for finding passengers and determining the hot spot pick-up locations using historical global positioning system (GPS) trajectories of taxis based on driver experience, distance from the passenger drop-off location to the next passenger pick-up location and the waiting times at recommended locations for the next passenger. The present work, however, considers the average taxi travel speed mined from historical taxi GPS trajectory data and the allocation of cruising routes to more than one taxi driver in a small-scale region to neighboring pick-up locations. A spatio-temporal trajectory model with load balancing allocations is presented to not only explore pick-up/drop-off information but also provide taxi drivers with cruising routes to the recommended pick-up locations. In simulation experiments, our study shows that taxi drivers using cruising routes recommended by our spatio-temporal trajectory model can significantly reduce the average waiting time and travel less distance to quickly find their next passengers, and the load balancing strategy significantly alleviates road loads. These objective measures can help us better understand spatio-temporal traffic patterns and guide taxi navigation.https://www.mdpi.com/2220-9964/6/11/373trajectory data miningtaxi planningspatio-temporal trajectory modelload balance |
| spellingShingle | Liang Wu Sheng Hu Li Yin Yazhou Wang Zhanlong Chen Mingqiang Guo Hao Chen Zhong Xie Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model ISPRS International Journal of Geo-Information trajectory data mining taxi planning spatio-temporal trajectory model load balance |
| title | Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model |
| title_full | Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model |
| title_fullStr | Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model |
| title_full_unstemmed | Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model |
| title_short | Optimizing Cruising Routes for Taxi Drivers Using a Spatio-Temporal Trajectory Model |
| title_sort | optimizing cruising routes for taxi drivers using a spatio temporal trajectory model |
| topic | trajectory data mining taxi planning spatio-temporal trajectory model load balance |
| url | https://www.mdpi.com/2220-9964/6/11/373 |
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