Traffic Safety Sensitivity Analysis of Parameters Used for Connected and Autonomous Vehicle Calibration

Tasneem Miqdady, Rocío de Oña () and Juan de Oña
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Tasneem Miqdady: TRYSE Research Group, ETSI Caminos, Canales y Puertos, University of Granada, Campus de Fuentenueva, s/n, 18071 Granada, Spain
Rocío de Oña: TRYSE Research Group, ETSI Caminos, Canales y Puertos, University of Granada, Campus de Fuentenueva, s/n, 18071 Granada, Spain
Juan de Oña: TRYSE Research Group, ETSI Caminos, Canales y Puertos, University of Granada, Campus de Fuentenueva, s/n, 18071 Granada, Spain

Sustainability, 2023, vol. 15, issue 13, 1-21

Abstract: Recently, the number of traffic safety studies involving connected and autonomous vehicles (CAVs) has been increasing. Due to the lack of information regarding the real behaviour of CAVs in mixed traffic flow, traffic simulation platforms are used to provide a reasonable approach for testing various scenarios and fleets. It is necessary to analyse how traffic safety is affected when key parameter assumptions are changed. The current study conducts a sensitivity analysis to identify the parameters used in CAV calibration that have the highest influence on traffic safety. Using a microsimulation-based surrogate safety assessment model approach (SSAM), traffic conflicts were identified, and a ceteris paribus analysis was conducted to measure the effect of gradually changing each parameter on the number of conflicts. Afterwards, a two-at-a-time sensitivity analysis was performed to explore the influence of simultaneously varying two parameters. The results revealed that reaction time, clearance, maximum acceleration, normal deceleration, and the sensitivity factor are key parameters. Studying these parameters two at a time revealed that low maximum acceleration, when combined with other parameters, consistently resulted in the highest number of conflicts, while combinations with short reaction time always yielded the best traffic safety results. This investigation broadens the understanding of CAV behaviour for future implementation for both manufacturers and researchers.

Keywords: connected and autonomous vehicles; surrogate safety measures; sensitivity analysis; traffic microsimulation; traffic safety; traffic conflicts (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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