Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/105472
Title: Studying the safety impact of autonomous vehicles using simulation-based surrogate safety measures
Authors: Morando, Mark Mario
Tian, Qingyun
Truong, Long T.
Vu, Hai L.
Keywords: DRNTU::Engineering::Civil engineering
Surrogate Safety Measure
Autonomous Vehicle
Issue Date: 2018
Source: Morando, M. M., Tian, Q., Truong, L. T., & Vu, H. L. (2018). Studying the Safety Impact of Autonomous Vehicles Using Simulation-Based Surrogate Safety Measures. Journal of Advanced Transportation, 2018, 6135183-. doi:10.1155/2018/6135183
Series/Report no.: Journal of Advanced Transportation
Abstract: Autonomous vehicle (AV) technology has advanced rapidly in recent years with some automated features already available in vehicles on the market. AVs are expected to reduce traffic crashes as the majority of crashes are related to driver errors, fatigue, alcohol, or drugs. However, very little research has been conducted to estimate the safety impact of AVs. This paper aims to investigate the safety impacts of AVs using a simulation-based surrogate safety measure approach. To this end, safety impacts are explored through the number of conflicts extracted from the VISSIM traffic microsimulator using the Surrogate Safety Assessment Model (SSAM). Behaviours of human-driven vehicles (HVs) and AVs (level 4 automation) are modelled within the VISSIM’s car-following model. The safety investigation is conducted for two case studies, that is, a signalised intersection and a roundabout, under various AV penetration rates. Results suggest that AVs improve safety significantly with high penetration rates, even when they travel with shorter headways to improve road capacity and reduce delay. For the signalised intersection, AVs reduce the number of conflicts by 20% to 65% with the AV penetration rates of between 50% and 100% (statistically significant at p < 0.05). For the roundabout, the number of conflicts is reduced by 29% to 64% with the 100% AV penetration rate (statistically significant at p < 0.05).
URI: https://hdl.handle.net/10356/105472
http://hdl.handle.net/10220/48708
ISSN: 0197-6729
DOI: 10.1155/2018/6135183
Rights: © 2018 Mark Mario Morando et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Journal Articles

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