Introducing novel statistical-based method of screening and combining currently well-known surrogate safety measures

Abstract

ABSTRACT Until now various surrogate safety measures (SSM) have been developed and research in this area is ongoing. However, it is unclear which indicators are able to reflect danger more accurately. This paper proposes a novel method of performance comparison by taking the advantage of statistical analyses of SSMs data. This analytical approach is inspired by the dominant behavior of drivers in car-following scenarios. Afterward, those indicators that previously identified as eligible are merged using a recently developed safety indicator, named collision probability (CP). The proposed method is applied to microscopic traffic data collected from the I-80 freeway in Next Generation Simulation (NGSIM) project. The results indicated that the proposed framework helps screen SSMs and calculate the probability of rear-end collisions using a combination of the selected SSMs. The results might be helpful for improving the precision and accuracy of in-vehicle collision avoidance warning system and safety evaluations without crash data.