Abstract: Secondary crashes or crashes that occur in the wake of a preceding or primary crash are among the most critical incidents occurring on highways, due to the exceptional danger they present to the first responders and victims of the primary crash. In this work, we developed a self-exciting temporal point process to analyze crash events data and classify it into primary and secondary crashes. Our model uses a self-exciting function to describe secondary crashes while primary crashes are modeled using a background rate function. We fit the model to crash incidents data from the Florida Department of Transportation on Interstate-4 (I-4) highway for the years 2015–2017 to determine the model parameters. These are used to estimate the probability that a given crash is a secondary crash and to find queue times. To represent the periodically varying traffic levels and crash incidents, we model the background rate as a stationary function, a sinusoidal non-stationary function, and a piecewise non-stationary function. We show that the sinusoidal non-stationary background rate fits the traffic data better and replicates the daily and weekly peaks in crash events due to traffic rush hours.

Sirish Namilae is a Professor in the Aerospace Engineering Department at the Embry-Riddle Aeronautical University. His research has focused on the areas of composite materials and complex systems and multiscale modeling. He has authored about 100 journal and conference publications in these research areas and has trained eight Ph.D. and twenty M.S. thesis students. He has generated more than $6M in research funding from NSF, NIH, DoT and NASA over the last few years. Dr. Namilae was awarded ERAU’s Abbas–Sivjee Outstanding Researcher award in 2022. He is an AIAA associate fellow and a Fulbright–Nehru research fellow.