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.

Pedestrian injury severity in motor vehicle crashes: An integrated spatio-temporal modeling approach.

Traffic crashes are outcomes of human activities interacting with the diverse cultural, socio-economic and geographic contexts, presenting a spatial and temporal nature. This study employs an integrated spatio-temporal modeling approach to untangle the crashed injury correlates that may vary across the space and time domain. Specifically, this study employs Geographically and Temporally Weighted Ordinal Logistic Regression (GTWOLR) to examine the correlates of pedestrian injury severity in motor vehicle crashes. The method leverages the space- and time-referenced crash data and powerful computational tools. This study performed non-stationarity tests to verify whether the local correlates of pedestrian injury severity have a significant spatio-temporal variation. Results showed that some variables passed the tests, indicating they have a significantly varying spatio-temporal relationship with the pedestrian injury severity. These factors include the pedestrian age, pedestrian position, crash location, motorist age and gender, driving under the influence (DUI), motor vehicle type and crash time in a day. The spatio-temporally varying correlates of pedestrian injury severity are valuable for researchers and practitioners to localize pedestrian safety improvement solutions in North Carolina. For example, in near future, special attention may be paid to DUI crashes in the city of Charlotte and Asheville, because in such areas DUI-involved crashes are even more likely to cause severe pedestrian injuries that in other areas. More implications are discussed in the paper.

Latent class analysis of factors that influence weekday and weekend single-vehicle crash severities.

This paper investigates factors that influence the severity of single-vehicle crashes that happen on weekdays and weekends. Crash data from 2012 to 2016 for the State of Alabama was used for this study. Latent class logit models were developed as alternative to the frequently used random parameters models to account for unobserved heterogeneity across crash-severity observations. Exploration of the data revealed that a high proportion of severe injury injury crashes happened on weekends. The study examined whether single-vehicle crash contributing factors differ between weekdays and weekends. The model estimation results indicate a significant association of severe injury crashes to risk factors such as driver unemployment, driving with invalid license, no seatbelt use, fatigue, driving under influence, old age, and driving on county roads for both weekdays and weekends. Research findings show a strong link between human factors and the occurrence of severe injury single-vehicle crashes, as it has been observed that many of the factors associated with severe-injury outcome are driver behavior related. To illustrate the significance of the findings of this study, a third model using the combined data was developed to explore the merit of using sub-populations of the data for improved and detailed segmentation of the crash-severity factors. It has also been shown that generally, the factors that influence single-vehicle crash injury outcomes were not very different between weekdays and weekends. The findings of this study show the importance of investigating sub-populations of data to reveal complex relationships that should be understood as a necessary step in targeted countermeasure application.