Development of a Secondary Crash Identification Algorithm and occurrence pattern determination in large scale multi-facility transportation network

Abstract

Secondary crash (SC) occurrences are non-recurrent in nature and lead to significant increase in traffic delay and reduced safety. National, state, and local agencies are investing substantial amount of resources to identify and mitigate secondary crashes in order to reduce congestion, related fatalities, injuries, and property damages. Though a relatively small portion of all crashes are secondary, their identification along with the primary contributing factors is imperative. The objective of this study is to develop a procedure to identify SCs using a static and a dynamic approach in a large-scale multimodal transportation networks. The static approach is based on pre-specified spatiotemporal thresholds while the dynamic approach is based on shockwave principles. A Secondary Crash Identification Algorithm (SCIA) was developed to identify SCs on networks. SCIA was applied on freeways using both the static and the dynamic approach while only static approach was used for arterials due to lack of disaggregated traffic flow data and signal-timing information. SCIA was validated by comparison to observed data with acceptable results from the regression analysis. SCIA was applied in the State of Tennessee and results showed that the dynamic approach can identify SCs with better accuracy and consistency. The methodological framework and processes proposed in this paper can be used by agencies for SC identification on networks with minimal data requirements and acceptable computational time.