Use of sensor data to identify secondary crashes on freeways

Nonrecurring traffic incidents, such as motor vehicle crashes, increase not only travel delays but also the risk of secondary crashes. Secondary crashes can cause additional traffic delays and reduce safety. Implementation of effective countermeasures to prevent or reduce secondary crashes requires that their characteristics be investigated. However, the related research has been limited, largely because of the lack of detailed incident and traffic data necessary to identify secondary crashes. Existing approaches, such as static methods employed to identify secondary crashes, cannot fully capture potential secondary crashes because of fixed spatiotemporal identification criteria. Improved approaches are needed to categorize secondary crashes accurately for further analysis. This paper develops an enhanced approach for identifying secondary crashes that uses the existing crash database and archived traffic data from highway sensors. The proposed method is threefold: (a) defining secondary crashes, (b) examining the impact range of primary crashes that possibly relate to secondary crashes, and (c) identifying secondary crashes. The proposed methodology establishes a practical framework for mining secondary crashes from existing sensor data and crash records. A case study was performed on a 27-mi segment of a major highway in New Jersey to illustrate the performance of the proposed approach. The results show that the proposed method provides a more reliable and efficient categorization of secondary crashes than commonly used approaches.