Driver Route Planning Method Based on Accident Risk Cost Prediction
The number of cars on roadways around the world continues to increase year over year. However, the imbalance between traffic supply and demand has not only brought traffic congestion but also caused serious safety problems. To reduce travel risk, this study proposes a driver route planning method ba...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/5023052 |
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| _version_ | 1832559392712556544 |
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| author | Xiaoleng Liao Tong Zhou Xu Wang Rongjian Dai Xuehui Chen Xiangmin Zhu |
| author_facet | Xiaoleng Liao Tong Zhou Xu Wang Rongjian Dai Xuehui Chen Xiangmin Zhu |
| author_sort | Xiaoleng Liao |
| collection | DOAJ |
| description | The number of cars on roadways around the world continues to increase year over year. However, the imbalance between traffic supply and demand has not only brought traffic congestion but also caused serious safety problems. To reduce travel risk, this study proposes a driver route planning method based on accident risk cost prediction for connected and automated vehicles. According to the entropy weight method and an improved algorithm of K shortest paths, a route planning model with accident risk as the main optimization objective was established. Firstly, an accident risk evaluation system was built based on traffic accident data, and a quantitative prediction model of accident risk cost based on driver-, vehicle-, road-, and environment-related factors was constructed. Secondly, the entropy weight method was used to calculate the weights of each indicator to determine accident risk considering the aforementioned factors. Then, the route planning model was established, and the solution algorithm based on K shortest paths was designed to solve the optimal route by comprehensively considering accident risk cost and travel time. The accident risk index of each road section in the example road network was assigned, and the risk of the road section was quantified according to the accident risk cost model. Three candidate paths were calculated by using the path planning algorithm proposed in this study; the total risk cost is 6.19, 6.26, and 6.39, respectively; and the total travel time is 29, 29, and 31, respectively. After comparison, the optimal path and two alternative paths are obtained. The results show that the accident risk cost prediction model based on historical accident data can be used to quantify driving risk. The proposed method can help drivers in the connected and automated environment choose the optimal travel route with the lowest risk and shortest travel time and improve overall traffic safety and efficiency. |
| format | Article |
| id | doaj-art-011f8030bcc247c9a477a1a1c91bd96d |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-011f8030bcc247c9a477a1a1c91bd96d2025-02-03T01:30:02ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/5023052Driver Route Planning Method Based on Accident Risk Cost PredictionXiaoleng Liao0Tong Zhou1Xu Wang2Rongjian Dai3Xuehui Chen4Xiangmin Zhu5School of Qilu TransportationSchool of Qilu TransportationSchool of Qilu TransportationSchool of Qilu TransportationShandong Hi-Speed Company LimitedShandong Hi-Speed Company LimitedThe number of cars on roadways around the world continues to increase year over year. However, the imbalance between traffic supply and demand has not only brought traffic congestion but also caused serious safety problems. To reduce travel risk, this study proposes a driver route planning method based on accident risk cost prediction for connected and automated vehicles. According to the entropy weight method and an improved algorithm of K shortest paths, a route planning model with accident risk as the main optimization objective was established. Firstly, an accident risk evaluation system was built based on traffic accident data, and a quantitative prediction model of accident risk cost based on driver-, vehicle-, road-, and environment-related factors was constructed. Secondly, the entropy weight method was used to calculate the weights of each indicator to determine accident risk considering the aforementioned factors. Then, the route planning model was established, and the solution algorithm based on K shortest paths was designed to solve the optimal route by comprehensively considering accident risk cost and travel time. The accident risk index of each road section in the example road network was assigned, and the risk of the road section was quantified according to the accident risk cost model. Three candidate paths were calculated by using the path planning algorithm proposed in this study; the total risk cost is 6.19, 6.26, and 6.39, respectively; and the total travel time is 29, 29, and 31, respectively. After comparison, the optimal path and two alternative paths are obtained. The results show that the accident risk cost prediction model based on historical accident data can be used to quantify driving risk. The proposed method can help drivers in the connected and automated environment choose the optimal travel route with the lowest risk and shortest travel time and improve overall traffic safety and efficiency.http://dx.doi.org/10.1155/2022/5023052 |
| spellingShingle | Xiaoleng Liao Tong Zhou Xu Wang Rongjian Dai Xuehui Chen Xiangmin Zhu Driver Route Planning Method Based on Accident Risk Cost Prediction Journal of Advanced Transportation |
| title | Driver Route Planning Method Based on Accident Risk Cost Prediction |
| title_full | Driver Route Planning Method Based on Accident Risk Cost Prediction |
| title_fullStr | Driver Route Planning Method Based on Accident Risk Cost Prediction |
| title_full_unstemmed | Driver Route Planning Method Based on Accident Risk Cost Prediction |
| title_short | Driver Route Planning Method Based on Accident Risk Cost Prediction |
| title_sort | driver route planning method based on accident risk cost prediction |
| url | http://dx.doi.org/10.1155/2022/5023052 |
| work_keys_str_mv | AT xiaolengliao driverrouteplanningmethodbasedonaccidentriskcostprediction AT tongzhou driverrouteplanningmethodbasedonaccidentriskcostprediction AT xuwang driverrouteplanningmethodbasedonaccidentriskcostprediction AT rongjiandai driverrouteplanningmethodbasedonaccidentriskcostprediction AT xuehuichen driverrouteplanningmethodbasedonaccidentriskcostprediction AT xiangminzhu driverrouteplanningmethodbasedonaccidentriskcostprediction |