Incentives for Ridesharing: A Case Study of Welfare and Traffic Congestion
Traffic congestion is largely due to the high proportion of solo drivers during peak hours. Ridesharing, in the sense of carpooling, has emerged as a travel mode with the potential to reduce congestion by increasing the average vehicle occupancy rates and reduce the number of vehicles during commuti...
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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/10.1155/2021/6627660 |
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| _version_ | 1832565226647584768 |
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| author | Changle Song Julien Monteil Jean-Luc Ygnace David Rey |
| author_facet | Changle Song Julien Monteil Jean-Luc Ygnace David Rey |
| author_sort | Changle Song |
| collection | DOAJ |
| description | Traffic congestion is largely due to the high proportion of solo drivers during peak hours. Ridesharing, in the sense of carpooling, has emerged as a travel mode with the potential to reduce congestion by increasing the average vehicle occupancy rates and reduce the number of vehicles during commuting periods. In this study, we propose a simulation-based optimization framework to explore the potential of subsidizing ridesharing users, drivers, and riders, so as to improve social welfare and reduce congestion. We focus our attention on a realistic case study representative of the morning commute on Sydney’s M4 Motorway in Australia. We synthesize a network model and travel demand data from open data sources and use a multinomial logistic model to capture users’ preferences across different travel roles, including solo drivers, ridesharing drivers, ridesharing passengers, and a reserve option that does not contribute to congestion on the freeway network. We use a link transmission model to simulate traffic congestion on the freeway network and embed a fixed-point algorithm to equilibrate users’ mode choice in the long run within the proposed simulation-based optimization framework. Our numerical results reveal that ridesharing incentives have the potential to improve social welfare and reduce congestion. However, we find that providing too many subsidies to ridesharing users may increase congestion levels and thus be counterproductive from a system performance standpoint. We also investigate the impact of transaction fees to a third-party ridesharing platform on social welfare and traffic congestion. We observe that increasing the transaction fee for ridesharing passengers may help in mitigating congestion effects while improving social welfare in the system. |
| format | Article |
| id | doaj-art-4268e36632e947a2b5bcbdf95190df44 |
| 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-4268e36632e947a2b5bcbdf95190df442025-02-03T01:08:52ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/66276606627660Incentives for Ridesharing: A Case Study of Welfare and Traffic CongestionChangle Song0Julien Monteil1Jean-Luc Ygnace2David Rey3School of Civil and Environmental Engineering, UNSW Sydney, Sydney, 2052 NSW, AustraliaIBM Research, Ireland Lab, Dublin, IrelandUniversity of Gustave Eiffel, University of Lyon, IFSTTAR, ENTPE, LICIT UMR T9401, Lyon, F-69675, FranceSchool of Civil and Environmental Engineering, UNSW Sydney, Sydney, 2052 NSW, AustraliaTraffic congestion is largely due to the high proportion of solo drivers during peak hours. Ridesharing, in the sense of carpooling, has emerged as a travel mode with the potential to reduce congestion by increasing the average vehicle occupancy rates and reduce the number of vehicles during commuting periods. In this study, we propose a simulation-based optimization framework to explore the potential of subsidizing ridesharing users, drivers, and riders, so as to improve social welfare and reduce congestion. We focus our attention on a realistic case study representative of the morning commute on Sydney’s M4 Motorway in Australia. We synthesize a network model and travel demand data from open data sources and use a multinomial logistic model to capture users’ preferences across different travel roles, including solo drivers, ridesharing drivers, ridesharing passengers, and a reserve option that does not contribute to congestion on the freeway network. We use a link transmission model to simulate traffic congestion on the freeway network and embed a fixed-point algorithm to equilibrate users’ mode choice in the long run within the proposed simulation-based optimization framework. Our numerical results reveal that ridesharing incentives have the potential to improve social welfare and reduce congestion. However, we find that providing too many subsidies to ridesharing users may increase congestion levels and thus be counterproductive from a system performance standpoint. We also investigate the impact of transaction fees to a third-party ridesharing platform on social welfare and traffic congestion. We observe that increasing the transaction fee for ridesharing passengers may help in mitigating congestion effects while improving social welfare in the system.http://dx.doi.org/10.1155/2021/6627660 |
| spellingShingle | Changle Song Julien Monteil Jean-Luc Ygnace David Rey Incentives for Ridesharing: A Case Study of Welfare and Traffic Congestion Journal of Advanced Transportation |
| title | Incentives for Ridesharing: A Case Study of Welfare and Traffic Congestion |
| title_full | Incentives for Ridesharing: A Case Study of Welfare and Traffic Congestion |
| title_fullStr | Incentives for Ridesharing: A Case Study of Welfare and Traffic Congestion |
| title_full_unstemmed | Incentives for Ridesharing: A Case Study of Welfare and Traffic Congestion |
| title_short | Incentives for Ridesharing: A Case Study of Welfare and Traffic Congestion |
| title_sort | incentives for ridesharing a case study of welfare and traffic congestion |
| url | http://dx.doi.org/10.1155/2021/6627660 |
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