Attributes of backward moving kinematic waves which signal changes in traffic conditions have long been investigated in an effort to understand how they influence the likelihood of traffic collisions. Backward moving kinematic waves can accompany abrupt reduction in spacing and when the approaching traffic do not adjust their speed at opportune time, it can lead to rear-end type of collisions.

Zheng et al. (2010) reported that traffic oscillation measured by standard deviation of speed could be a significant variable for explaining crash occurrences. Xu et al. (2012) divided traffic flow into five different states using K-mean clustering, and showed that transition state from free-flow to congested flow and highly congested flow where backward moving wave occurs has the greatest impact on crash occurrences. Chung et al. (2010) investigated the attributes of kinematic waves preceding traffic collision by measuring the amplitude and speed of kinematic waves that were emanated from near recurrent bottleneck. Their finding suggested that fast backward moving wave with high amplitude increases the risk of traffic collisions. Li et al. (2014) proposed rear-end collision risk index (RCRI), which is defined by speed variance and occupancy of adjacent upstream and downstream detectors to evaluate factor affecting rear-end type of collisions and their findings indicate that RCRI and standard deviation of occupancy affect the rear-end collision likelihood.

These prior studies have shown that the effect of backward moving kinematic waves through various surrogate safety measure. However, because of the limitation of loop detectors which only collect the traffic data at specific points rather than over space, most of the studies assume that traffic conditions are homogeneous within adjacent detectors. Therefore, this assumption leads to biased effect on traffic state according to distance from the detectors, and it also make bias to traffic collision model.

In this study, we estimated traffic states between adjacent detectors using spatiotemporal wave reconstruction to overcome this limitation. This process reduces the bias from the homogeneous assumption on traffic flow, and allow us to evaluate the unbiased impact of kinematic wave to rear-end collision.