Real-Time Return Demand Prediction Based on Multisource Data of One-Way Carsharing Systems
One-way carsharing system has been widely adopted in the carsharing field due to its flexible services. However, as one of the main limitations of the one-way carsharing system, the imbalance between supply and demand needs to be solved. Predicting pick-up demand has been studied to achieve the goal...
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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/10.1155/2021/6654909 |
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| _version_ | 1832560040140079104 |
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| author | Dongbo Liu Jian Lu Wanjing Ma |
| author_facet | Dongbo Liu Jian Lu Wanjing Ma |
| author_sort | Dongbo Liu |
| collection | DOAJ |
| description | One-way carsharing system has been widely adopted in the carsharing field due to its flexible services. However, as one of the main limitations of the one-way carsharing system, the imbalance between supply and demand needs to be solved. Predicting pick-up demand has been studied to achieve the goal, but using returned vehicles to reduce unnecessary relocation is also one of the important methods. Nowadays, trajectory data and other data are available for real-time prediction for return demand. Based on the return demand prediction, the relocation response can be more reasonable. Thus, the balance of demand and supply can be largely improved. The multisource data include trajectory data, user application log data, order data, station data, and user characteristic data. Based on these data, a return demand prediction model was used to predict whether the user will return the vehicle in 15 min in real time, and a destination station prediction model was applied to forecast which station the user will park at. Finally, a case study using ten stations’ one-week field data was conducted to test the benefit of the dynamic return demand prediction. The results showed that the return demand prediction improves the efficiency of the relocations by mitigating the condition that the station parking space is full or empty. The potential application of this study would effectively reduce unnecessary relocation and further formulate an active operation optimization strategy to reduce the system’s operational cost and improve the service quality of the system. |
| format | Article |
| id | doaj-art-70370ca65a5e42ba93e47e69f3c2b05d |
| 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-70370ca65a5e42ba93e47e69f3c2b05d2025-02-03T01:28:30ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/66549096654909Real-Time Return Demand Prediction Based on Multisource Data of One-Way Carsharing SystemsDongbo Liu0Jian Lu1Wanjing Ma2School of Transportation, Southeast University, Nanjing, ChinaSchool of Transportation, Southeast University, Nanjing, ChinaJiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Road #2, Nanjing, ChinaOne-way carsharing system has been widely adopted in the carsharing field due to its flexible services. However, as one of the main limitations of the one-way carsharing system, the imbalance between supply and demand needs to be solved. Predicting pick-up demand has been studied to achieve the goal, but using returned vehicles to reduce unnecessary relocation is also one of the important methods. Nowadays, trajectory data and other data are available for real-time prediction for return demand. Based on the return demand prediction, the relocation response can be more reasonable. Thus, the balance of demand and supply can be largely improved. The multisource data include trajectory data, user application log data, order data, station data, and user characteristic data. Based on these data, a return demand prediction model was used to predict whether the user will return the vehicle in 15 min in real time, and a destination station prediction model was applied to forecast which station the user will park at. Finally, a case study using ten stations’ one-week field data was conducted to test the benefit of the dynamic return demand prediction. The results showed that the return demand prediction improves the efficiency of the relocations by mitigating the condition that the station parking space is full or empty. The potential application of this study would effectively reduce unnecessary relocation and further formulate an active operation optimization strategy to reduce the system’s operational cost and improve the service quality of the system.http://dx.doi.org/10.1155/2021/6654909 |
| spellingShingle | Dongbo Liu Jian Lu Wanjing Ma Real-Time Return Demand Prediction Based on Multisource Data of One-Way Carsharing Systems Journal of Advanced Transportation |
| title | Real-Time Return Demand Prediction Based on Multisource Data of One-Way Carsharing Systems |
| title_full | Real-Time Return Demand Prediction Based on Multisource Data of One-Way Carsharing Systems |
| title_fullStr | Real-Time Return Demand Prediction Based on Multisource Data of One-Way Carsharing Systems |
| title_full_unstemmed | Real-Time Return Demand Prediction Based on Multisource Data of One-Way Carsharing Systems |
| title_short | Real-Time Return Demand Prediction Based on Multisource Data of One-Way Carsharing Systems |
| title_sort | real time return demand prediction based on multisource data of one way carsharing systems |
| url | http://dx.doi.org/10.1155/2021/6654909 |
| work_keys_str_mv | AT dongboliu realtimereturndemandpredictionbasedonmultisourcedataofonewaycarsharingsystems AT jianlu realtimereturndemandpredictionbasedonmultisourcedataofonewaycarsharingsystems AT wanjingma realtimereturndemandpredictionbasedonmultisourcedataofonewaycarsharingsystems |