A comparative analysis of freeway crash incident clearance time using random parameter and latent class hazard-based duration model.

The effects of freeway incident clearance times on the flow of traffic have recently increased interests in understanding what factors influence incident durations. This has particularly become topical due to the financial and economic implications of traffic gridlocks caused by freeway incidents on industries and personal mobility. This paper presents two advanced econometric modeling methods, random parameters duration modeling and latent class duration modeling in understanding the factors that impact freeway incident clearance times in the State of Alabama. These two modeling approaches were further compared to identify which of them provides the best fit for the data with respect to accounting for unobserved heterogeneity. A total of 2206 freeway crash incident data from January 1 to December 31, 2018 were examined in developing the models. The study was based on a unique dataset that involved merging and matching Traffic Incident Management response data from the Alabama Department of Transportation (ALDOT) Traffic Management Center (TMC), freeway crash data from the Center for Advanced Public Safety (CAPS) at the University of Alabama, Alabama Service and Assistance Patrol (ASAP) data from ALDOT and traffic volume from ALDOT's Highway Performance Management System (HPMS). The model estimation results reveal that a total of nineteen variables were found statistically significant with five random variables (on-road, nighttime, rain, AADT, and ASAP existing coverage area) and fourteen fixed effects variables for the random parameters model. For latent class model, a total of eighteen variables were observed statistically significant within two distinct latent classes (Latent Class 1 with class membership probability of 0.23 and Latent Class 2 with class membership probability of 0.77) at a 0.05 significance level. A comparison of the two models reveals that the latent class model provides the better fit for the incident duration data. The findings of this study are expected to contribute to the body of knowledge on incident duration by employing two advanced econometric modeling methods and to inform statewide efforts in significantly reducing the duration of freeway incident clearance time. Moreover, this is to ensure that policy decisions that may arise from the findings of the study are sound and based on data-driven evidence.