Traffic Safety Sensitivity Analysis of Parameters Used for Connected and Autonomous Vehicle Calibration
Metadatos
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MDPI
Materia
Connected and autonomous vehicles Surrogate safety measures Sensitivity analysis Traffic microsimulation Traffic safety Traffic conflicts
Fecha
2023-06-23Referencia bibliográfica
Miqdady, T.; de Oña, R.; de Oña, J. Traffic Safety Sensitivity Analysis of Parameters Used for Connected and Autonomous Vehicle Calibration. Sustainability 2023, 15, 9990. [https://doi.org/10.3390/ su15139990]
Patrocinador
Research Project PID2019-110741RA-I00, funded by the Spanish State Research Agency under Grant MCIN/AEI /10.13039/501100011033Resumen
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.