Identification of Secondary Crashes on a Large-Scale Highway System

Abstract

Freeway incidents are major sources of nonrecurrent congestion, and resultant secondary crashes can prolong traffic impact and increase costs. Research on secondary crashes to support statewide transportation system management has been limited. In this study, a two-phase automated procedure was developed to identify secondary crashes on large-scale regional transportation systems. In the first phase, a crash-pairing algorithm was developed to extract spatially and temporally nearby crash pairs. The accuracy and efficiency of the algorithm were validated by comparing it to an ArcGIS-based program. In the second phase, two filters were proposed to reduce the crash pairs for secondary crash identification: the first filter selected crash pairs whose earlier crashes were on mainline highways; the second filter selected crash pairs whose later crashes happened within the dynamic impact areas (i.e., backup queues) of the earlier crashes. Shockwave theory was used to model the dynamic impact of a primary incident. The two-phase procedure used a linear referencing system for crash localization and can be applied to any regional transportation system with a similar data structure. A case study using 2010 data was conducted on nearly 1,500 mi of freeways in Wisconsin. Among the crash pairs produced by the two-phase procedure, 73 secondary crashes were confirmed with police reports. Preliminary analyses showed that (a) secondary crashes occurring in the same traffic direction as the primary incidents were about three times as frequent as secondary crashes in the opposing direction, and (b) two-vehicle rear-end collisions, multiple-vehicle rear-end collisions, and sideswipes were three major types of secondary crashes (about 84%).