Sensitivity analysis on new simulation-based conflict metrics

Abstract

The Conflict Propensity Metric (CPM) and the Aggregate Conflict Propensity Metric (ACPM) are two simulation-based conflict metrics recently proposed as surrogate safety measures. The two metrics are derived through a stochastic process incorporating distributions of driver reaction time (RT) and vehicle maximum braking rates (MABR). This paper presents sensitivity analyses on the two metrics, by altering the parameters (i.e. mean and standard deviation) of RT distributions. Both RT mean and standard deviation affect the estimates of CPM for the three conflict types examined here (i.e. crossing, rear-end and lane change), and the impacts vary by conflict types, indicating the need of carefully evaluating and considering both RT distributions and conflict types when developing simulation-based conflict metrics. A sensitivity analysis based on field data showed that different RT distributions have an impact on ACPM and could affect the reliability of ACPM in identifying relative safety and its correlation with actual crashes. The analysis here identified the potential existence of different “realistic” RT distributions for different conflict types and the suggested values are considered reasonable or consistent with prior findings. ACPM has proved to have potential to further improve its accuracy using more suitable RT distributions. However, dedicated RT distributions for specific conflict types are lacking, especially a joint RT distribution conditional various factors, impeding the improvement of ACPM. In general, sensitivity analyses have shown the strength of the process of deriving CPM and ACPM by providing reasonable findings, as well as pointing out interesting and important future research directions, such as finding actual RT distributions for different conflict types.