Recurrent Neural-Based Vehicle Demand Forecasting and Relocation Optimization for Car-Sharing System: A Real Use Case in Thailand
A car-sharing system has been playing an important role as an alternative transport mode in order to avoid traffic congestion and pollution due to a quick growth of usage of private cars. In this paper, we propose a novel vehicle relocation system with a major improvement in threefolds: (i) data pre...
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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/10.1155/2021/8885671 |
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| author | Peerapon Vateekul Panyawut Sri-iesaranusorn Pawit Aiemvaravutigul Adsadawut Chanakitkarnchok Kultida Rojviboonchai |
| author_facet | Peerapon Vateekul Panyawut Sri-iesaranusorn Pawit Aiemvaravutigul Adsadawut Chanakitkarnchok Kultida Rojviboonchai |
| author_sort | Peerapon Vateekul |
| collection | DOAJ |
| description | A car-sharing system has been playing an important role as an alternative transport mode in order to avoid traffic congestion and pollution due to a quick growth of usage of private cars. In this paper, we propose a novel vehicle relocation system with a major improvement in threefolds: (i) data preprocessing, (ii) demand forecasting, and (iii) relocation optimization. The data preprocessing is presented in order to automatically remove fake demands caused by search failures and application errors. Then, the real demand is forecasted using a deep learning approach, Bidirectional Gated Recurrent Unit. Finally, the Minimum Cost Maximum Flow algorithm is deployed to maximize forecasted demands, while minimizing the amount of relocations. Furthermore, the system is deployed in the real use case, entitled “CU Toyota Ha:mo,” which is a car-sharing system in Chulalongkorn University. It is based on a web application along with rule-based notification via Line. The experiment was conducted based on the real vehicle usage data in 2019. By comparing in real environment in November of 2019, the results show that our model even outperforms the manual relocation by experienced staff. It achieved a 3% opportunity loss reduction and 3% less relocation trips, reducing human effort by 17 man-hours/week. |
| format | Article |
| id | doaj-art-a59f2f55de154af69614f8482d86bb74 |
| 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-a59f2f55de154af69614f8482d86bb742025-08-20T03:38:31ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/88856718885671Recurrent Neural-Based Vehicle Demand Forecasting and Relocation Optimization for Car-Sharing System: A Real Use Case in ThailandPeerapon Vateekul0Panyawut Sri-iesaranusorn1Pawit Aiemvaravutigul2Adsadawut Chanakitkarnchok3Kultida Rojviboonchai4Chulalongkorn University Big Data Analytics and IoT Center (CUBIC), Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandChulalongkorn University Big Data Analytics and IoT Center (CUBIC), Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandChulalongkorn University Big Data Analytics and IoT Center (CUBIC), Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandChulalongkorn University Big Data Analytics and IoT Center (CUBIC), Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandChulalongkorn University Big Data Analytics and IoT Center (CUBIC), Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandA car-sharing system has been playing an important role as an alternative transport mode in order to avoid traffic congestion and pollution due to a quick growth of usage of private cars. In this paper, we propose a novel vehicle relocation system with a major improvement in threefolds: (i) data preprocessing, (ii) demand forecasting, and (iii) relocation optimization. The data preprocessing is presented in order to automatically remove fake demands caused by search failures and application errors. Then, the real demand is forecasted using a deep learning approach, Bidirectional Gated Recurrent Unit. Finally, the Minimum Cost Maximum Flow algorithm is deployed to maximize forecasted demands, while minimizing the amount of relocations. Furthermore, the system is deployed in the real use case, entitled “CU Toyota Ha:mo,” which is a car-sharing system in Chulalongkorn University. It is based on a web application along with rule-based notification via Line. The experiment was conducted based on the real vehicle usage data in 2019. By comparing in real environment in November of 2019, the results show that our model even outperforms the manual relocation by experienced staff. It achieved a 3% opportunity loss reduction and 3% less relocation trips, reducing human effort by 17 man-hours/week.http://dx.doi.org/10.1155/2021/8885671 |
| spellingShingle | Peerapon Vateekul Panyawut Sri-iesaranusorn Pawit Aiemvaravutigul Adsadawut Chanakitkarnchok Kultida Rojviboonchai Recurrent Neural-Based Vehicle Demand Forecasting and Relocation Optimization for Car-Sharing System: A Real Use Case in Thailand Journal of Advanced Transportation |
| title | Recurrent Neural-Based Vehicle Demand Forecasting and Relocation Optimization for Car-Sharing System: A Real Use Case in Thailand |
| title_full | Recurrent Neural-Based Vehicle Demand Forecasting and Relocation Optimization for Car-Sharing System: A Real Use Case in Thailand |
| title_fullStr | Recurrent Neural-Based Vehicle Demand Forecasting and Relocation Optimization for Car-Sharing System: A Real Use Case in Thailand |
| title_full_unstemmed | Recurrent Neural-Based Vehicle Demand Forecasting and Relocation Optimization for Car-Sharing System: A Real Use Case in Thailand |
| title_short | Recurrent Neural-Based Vehicle Demand Forecasting and Relocation Optimization for Car-Sharing System: A Real Use Case in Thailand |
| title_sort | recurrent neural based vehicle demand forecasting and relocation optimization for car sharing system a real use case in thailand |
| url | http://dx.doi.org/10.1155/2021/8885671 |
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