Fuel consumption evaluation of connected automated vehicles under rear-end collisions
Connected automated vehicles (CAV) can increase traffic efficiency, which is considered a critical factor in saving energy and reducing emissions in traffic congestion. In this paper, systematic traffic simulations are conducted for three car-following modes, including intelligent driver model (IDM)...
| Main Authors: | , , , , |
|---|---|
| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/173543 |
| _version_ | 1826113591697211392 |
|---|---|
| author | Liu, Qingchao Ouyang, Wenjie Zhao, Jingya Cai, Yingfeng Chen, Long |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Liu, Qingchao Ouyang, Wenjie Zhao, Jingya Cai, Yingfeng Chen, Long |
| author_sort | Liu, Qingchao |
| collection | NTU |
| description | Connected automated vehicles (CAV) can increase traffic efficiency, which is considered a critical factor in saving energy and reducing emissions in traffic congestion. In this paper, systematic traffic simulations are conducted for three car-following modes, including intelligent driver model (IDM), adaptive cruise control (ACC), and cooperative ACC (CACC), in congestions caused by rear-end collisions. From the perspectives of lane density, vehicle trajectory and vehicle speed, the fuel consumption of vehicles under the three car-following modes are compared and analysed, respectively. Based on the vehicle driving and accident environment parameters, an XGBoost algorithm-based fuel consumption prediction framework is proposed for traffic congestions caused by rear-end collisions. The results show that compared with IDM and ACC modes, the vehicles in CACC car-following mode have the ideal performance in terms of total fuel consumption; besides, the traffic flow in CACC mode is more stable, and the speed fluctuation is relatively tiny in different accident impact regions, which meets the driving desires of drivers. |
| first_indexed | 2024-10-01T03:25:44Z |
| format | Journal Article |
| id | ntu-10356/173543 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:25:44Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1735432024-02-17T16:48:40Z Fuel consumption evaluation of connected automated vehicles under rear-end collisions Liu, Qingchao Ouyang, Wenjie Zhao, Jingya Cai, Yingfeng Chen, Long School of Mechanical and Aerospace Engineering Engineering Fuel Consumption Prediction Traffic Accident Connected automated vehicles (CAV) can increase traffic efficiency, which is considered a critical factor in saving energy and reducing emissions in traffic congestion. In this paper, systematic traffic simulations are conducted for three car-following modes, including intelligent driver model (IDM), adaptive cruise control (ACC), and cooperative ACC (CACC), in congestions caused by rear-end collisions. From the perspectives of lane density, vehicle trajectory and vehicle speed, the fuel consumption of vehicles under the three car-following modes are compared and analysed, respectively. Based on the vehicle driving and accident environment parameters, an XGBoost algorithm-based fuel consumption prediction framework is proposed for traffic congestions caused by rear-end collisions. The results show that compared with IDM and ACC modes, the vehicles in CACC car-following mode have the ideal performance in terms of total fuel consumption; besides, the traffic flow in CACC mode is more stable, and the speed fluctuation is relatively tiny in different accident impact regions, which meets the driving desires of drivers. Published version This work was supported by the National Natural Science Foundation of China (51905223, U20A20331, U20A20333, 52225212, 52072160); China Postdoctoral Science Foundation (2021M690069); Key Research and Development Program of Jiangsu Province (BE2020083-3, BE2019010-2, BE2021011-3); Six Talent Peaks Project of Jiangsu Province(2018-TD-GDZB-022); Transportation Science and Technology Project of Jiangsu Province (2022Y03), and the Young Talent Cultivation Project of Jiangsu University. 2024-02-13T06:46:11Z 2024-02-13T06:46:11Z 2023 Journal Article Liu, Q., Ouyang, W., Zhao, J., Cai, Y. & Chen, L. (2023). Fuel consumption evaluation of connected automated vehicles under rear-end collisions. Promet - Traffic and Transportation, 35(3), 331-348. https://dx.doi.org/10.7307/ptt.v35i3.179 0353-5320 https://hdl.handle.net/10356/173543 10.7307/ptt.v35i3.179 2-s2.0-85169449981 3 35 331 348 en Promet - Traffic and Transportation © 2023 Qingchao Liu, Wenjie Ouyang, Jingya Zhao, Yingfeng Cai, Long Chen. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. application/pdf |
| spellingShingle | Engineering Fuel Consumption Prediction Traffic Accident Liu, Qingchao Ouyang, Wenjie Zhao, Jingya Cai, Yingfeng Chen, Long Fuel consumption evaluation of connected automated vehicles under rear-end collisions |
| title | Fuel consumption evaluation of connected automated vehicles under rear-end collisions |
| title_full | Fuel consumption evaluation of connected automated vehicles under rear-end collisions |
| title_fullStr | Fuel consumption evaluation of connected automated vehicles under rear-end collisions |
| title_full_unstemmed | Fuel consumption evaluation of connected automated vehicles under rear-end collisions |
| title_short | Fuel consumption evaluation of connected automated vehicles under rear-end collisions |
| title_sort | fuel consumption evaluation of connected automated vehicles under rear end collisions |
| topic | Engineering Fuel Consumption Prediction Traffic Accident |
| url | https://hdl.handle.net/10356/173543 |
| work_keys_str_mv | AT liuqingchao fuelconsumptionevaluationofconnectedautomatedvehiclesunderrearendcollisions AT ouyangwenjie fuelconsumptionevaluationofconnectedautomatedvehiclesunderrearendcollisions AT zhaojingya fuelconsumptionevaluationofconnectedautomatedvehiclesunderrearendcollisions AT caiyingfeng fuelconsumptionevaluationofconnectedautomatedvehiclesunderrearendcollisions AT chenlong fuelconsumptionevaluationofconnectedautomatedvehiclesunderrearendcollisions |