Traffic crashes are a particular concern in urban areas, where the occurrence of a collision heightens the risk of subsequent secondary crashes upstream, particularly under high levels of traffic congestion. There is considerable difficulty in estimating the number of such crashes, and in identifying roadway locations and circumstances where the risks of such crashes are most pronounced. In light of these concerns, there is significant value in advancing our understanding of these issues, including our ability to predict and mitigate the potential for secondary crashes on freeways. A significant challenge in this regard is the ability to effectively identify a secondary crash with respect to the both the spatial temporal thresholds within which secondary crashes occur. Contemporary approaches are often based on static spatiotemporal impact windows, or on dynamic approaches that consider traffic flow conditions. Both methods are subject to important limitations that are investigated as a part of this research. As a part of this study, crash data from the Michigan interstate system was used to identify secondary crashes. A detailed review of police crash reports is conducted to verify which crashes are secondary in nature by examining standard fields on the report form, as well as information from the narrative section completed by the investigating officer. The influence of spatiotemporal window sizing (relative to the time and location of the primary crash) is explored with respect to the sensitivity and specificity of secondary crash detection in order to determine thresholds that yield minimal error. A static approach based on a large number of predefined window sizes was used to compare the rate of secondary crash identification. The static method was shown to consistently overestimate secondary crash occurrence and these results varied across thresholds sizes. Subsequent efforts used a dynamic approach, where the window size was varied based upon changes in speed profiles on the associated road segments. Real-time traffic and speed data were used to identify secondary crashes and the results vary considerably based upon the method employed. The research also identified contextual environments where the risks of secondary crashes are most pronounced through the estimation of a series of regression models, culminating in guidance to assist road agencies in effectively monitoring and clearing crashes and other incidents to minimize the potential for secondary crashes.
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