Investigating the Impact of Automated Vehicles on the Safety and Operation of Low-Speed Urban Networks
Automated vehicles (AVs) will appear on the road soon and will influence the properties of road traffic networks such as capacity and safety. While the impact of AV on traffic operations has been discussed extensively, most existing literature in this direction focuses on microscopic and mesoscopic...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi-Wiley
2024-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2024/3835831 |
| _version_ | 1797224502372859904 |
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| author | Amir Hossein Karbasi Nan Zheng Steve O’Hern |
| author_facet | Amir Hossein Karbasi Nan Zheng Steve O’Hern |
| author_sort | Amir Hossein Karbasi |
| collection | DOAJ |
| description | Automated vehicles (AVs) will appear on the road soon and will influence the properties of road traffic networks such as capacity and safety. While the impact of AV on traffic operations has been discussed extensively, most existing literature in this direction focuses on microscopic and mesoscopic levels. This study examines the effect of AVs on traffic flow operations and crash risks at a network scale. In addition, this study discusses the implications for designing and operating safe, low-speed urban road networks. Simulation experiments were carried out in a grid road network with AVs and human-driven vehicles operating in mixed flow conditions. To assess traffic performance, the macroscopic fundamental diagram (MFD) relationships were estimated, specifically focusing on speed-density correlations. From this analysis, it was possible to extract key traffic performance indices such as capacity and critical speed. Using time-to-collision surrogate safety measures, macroscopic safety diagrams were generated which associate the level of congestion with the potential crash conflicts among vehicles at an aggregated spatial scale. Utilizing this knowledge, a novel multiobjective optimization based on the NSGA-II algorithm was applied to identify the optimal trade-off between efficiency and safety. The presence of AVs was found to have a positive impact on the capacity, critical density, and average speed on a system level, even in low-speed scenarios. Moreover, AVs can result in increased critical density in the network, which suggests that the road system can serve more vehicles at its capacity, thus improving efficiency, while decreasing the number of conflicts. These findings are useful for both traffic planners and operators. |
| first_indexed | 2024-04-24T13:54:08Z |
| format | Article |
| id | doaj.art-e62e3a469bff47e2b87b4ebfc4d1d756 |
| institution | Directory Open Access Journal |
| issn | 2042-3195 |
| language | English |
| last_indexed | 2024-04-24T13:54:08Z |
| publishDate | 2024-01-01 |
| publisher | Hindawi-Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj.art-e62e3a469bff47e2b87b4ebfc4d1d7562024-04-04T00:00:03ZengHindawi-WileyJournal of Advanced Transportation2042-31952024-01-01202410.1155/2024/3835831Investigating the Impact of Automated Vehicles on the Safety and Operation of Low-Speed Urban NetworksAmir Hossein Karbasi0Nan Zheng1Steve O’Hern2Department of Civil EngineeringMonash UniversityInstitute for Transport StudiesAutomated vehicles (AVs) will appear on the road soon and will influence the properties of road traffic networks such as capacity and safety. While the impact of AV on traffic operations has been discussed extensively, most existing literature in this direction focuses on microscopic and mesoscopic levels. This study examines the effect of AVs on traffic flow operations and crash risks at a network scale. In addition, this study discusses the implications for designing and operating safe, low-speed urban road networks. Simulation experiments were carried out in a grid road network with AVs and human-driven vehicles operating in mixed flow conditions. To assess traffic performance, the macroscopic fundamental diagram (MFD) relationships were estimated, specifically focusing on speed-density correlations. From this analysis, it was possible to extract key traffic performance indices such as capacity and critical speed. Using time-to-collision surrogate safety measures, macroscopic safety diagrams were generated which associate the level of congestion with the potential crash conflicts among vehicles at an aggregated spatial scale. Utilizing this knowledge, a novel multiobjective optimization based on the NSGA-II algorithm was applied to identify the optimal trade-off between efficiency and safety. The presence of AVs was found to have a positive impact on the capacity, critical density, and average speed on a system level, even in low-speed scenarios. Moreover, AVs can result in increased critical density in the network, which suggests that the road system can serve more vehicles at its capacity, thus improving efficiency, while decreasing the number of conflicts. These findings are useful for both traffic planners and operators.http://dx.doi.org/10.1155/2024/3835831 |
| spellingShingle | Amir Hossein Karbasi Nan Zheng Steve O’Hern Investigating the Impact of Automated Vehicles on the Safety and Operation of Low-Speed Urban Networks Journal of Advanced Transportation |
| title | Investigating the Impact of Automated Vehicles on the Safety and Operation of Low-Speed Urban Networks |
| title_full | Investigating the Impact of Automated Vehicles on the Safety and Operation of Low-Speed Urban Networks |
| title_fullStr | Investigating the Impact of Automated Vehicles on the Safety and Operation of Low-Speed Urban Networks |
| title_full_unstemmed | Investigating the Impact of Automated Vehicles on the Safety and Operation of Low-Speed Urban Networks |
| title_short | Investigating the Impact of Automated Vehicles on the Safety and Operation of Low-Speed Urban Networks |
| title_sort | investigating the impact of automated vehicles on the safety and operation of low speed urban networks |
| url | http://dx.doi.org/10.1155/2024/3835831 |
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