Date of Award
Master of Science
Mihalis M Golias
Stephanie S. Ivey
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. There are two major objectives of this study. First, to develop a procedure to identify SCs in a large-scale multimodal transportation network with multiple roadway facility types using a static and a dynamic approach. Second, to develop prediction models to determine primary contributing factors and primary crash characteristics that may induce a SC. Two types of models were developed for identification of SCs: (1) static approach, and (2) dynamic approach. 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 used to identify SCs on Tennessee roadway network and the results were validated using the observed data. The crash prediction models revealed that SCs are more prevalent on major arterials compared to freeways and the primary contributing factors are: number of vehicle involved in a primary crash, crash occurring on a roadway with relatively high Annual Average Daily Traffic (AADT), bad weather condition (e.g. rain fog, snow, sleet etc.) and primary incident type. 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.
dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.
Sarker, Afrid Alavee, "Secondary Crashes: Identification, Visualization and Prediction" (2015). Electronic Theses and Dissertations. 1247.