Injury severity influence factors and collision prediction - A case study on Kuwait highways. (March 2021)

• Record Type: Journal Article
• Authors: AlRukaibi, Fahad; AlKheder, Sharaf; Sayed, Tarek; Alburait, Abdulaziz
• Is Part Of: Journal of transport & health. Volume 20(2021)
• Publication Date: 2021-03
• Subjects: Driver injury severity -- Mixed logit model -- Collision prediction models -- Regression-to-mean -- Collision blackspot prediction -- Bayesian statistics; Transportation -- Health aspects -- Periodicals; Transportation -- Periodicals; Public Health -- Periodicals; Noise, Transportation -- Periodicals; Air Pollutants -- Periodicals; 388
• DOI: 10.1016/j.jth.2021.101025
• Languages: English
• ISSNs: 2214-1405

Abstract

Abstract: Traffic collisions are considered a serious problem worldwide that cause severe losses. Gulf Cooperation Council (GCC) Countries have high rates of collisions, which requires urgent proactive strategies and attention to solve such problem. This study aimed to enhance traffic safety conditions and reduce collisions' severity levels at several locations in Kuwait City, Kuwait. Consequently, the study importance raised in four folds; reducing the total number of collisions on Kuwait highways, predicting the future numbers of collisions, identifying and managing risk factors contributing to collision's severity, and developing new strategies to enhance traffic safety condition in Kuwait. Three-year crash dataset from 2016 to 2018 including 4028 road collisions in Kuwait City were used to analyse the driver injury severity influence factors and to predict the future collision counts. Statistical indices were used to evaluate the mixed logit model performance, which are the MCFadden Pseudo R-Squared statistics and the two-log likelihood. Eight covariates were tested for significance in the mixed logit model estimation. Results showed that female drivers, driving during night-time, driving outside the city, and not using seatbelt produced the highest possibility of having incapacitating injuries and fatalities by 46.6% and 31.6%, respectively. Contrastingly, male drivers, driving during daytime, driving inside the city, and using seatbelt had resulted in the lowest probability of getting incapacitating injuries and fatalities by 4.7% and 0.2%, respectively. Furthermore, Bayesian hierarchical model was used to investigate the future road collision counts and to identify the collision blackspots (CBS) on Kuwait City highways. Six covariates were considered in the model. Log-linear regression model (CPM) was used to estimate the vector of means μ j ( t ) . By applying regression to mean (RTM) and predicting the trend in the mean collision rate λj (t), results showed that the average model accuracy for year 2018 was 45.76% (for a 95% confidence interval estimation using the collision dataset of previous years). It was also found that the highest number of collisions had occurred on the second ring road. Basing the results on year 2018 helped in predicting more accurate future collision counts, and in justifying collision rates. Additionally, further covariates can be added to the models for any additional recent crash data to increase the model accuracy. This work is anticipated to help Kuwaiti decision makers in designing accurate countermeasures to improve traffic safety condition. Highlights: Three-year collision dataset from 2016 to 2018 including 4028 road collision s in Kuwait City were used to analyse the driver injury severity influence factors and to predict the future road collision counts. Statistical indices were used to evaluate the mixed logit model performance, which are the MCFadden Pseudo R-Squared statistics and the two-log likelihood. Eight covariates were tested for significance in the mixed logit model estimation, which are gender (X1), age (X2), light condition (X3), seatbelt use (X4), nationality (X5), time period (X6), vehicle type (X7), and road location (X8). Results indicates that Gender, Light Condition, Seatbelt use, and Road Location are significant to crash injury severity based on the likelihood ratio test at significant level of 5%. Further results show that female drivers, driving during night-time, outside the city, and not using seatbelt produce the highest possibility of having Incapacitating injuries and fatalities by 46.6% and 31.6%, respectively. Furthermore, Bayesian hierarchical model was applied to investigate the future road collision counts and to identify collisions black spot in Kuwait City highways which help to improve road safety level and eliminate collision s death rate. Six covariates that affect collisions existence where considered in the model, which are gender, road type, speed limit, number of lanes, time, seatbelt use. Log-linear regression model (APM) was used to estimate the vector of means μ_j (t). By applying regression to mean (RTM) and predicting the trend in the mean collision rate λj (t), results show that average model accuracy for year 2018 have reached 45.76% with a high percentage of 95% of the confidence interval estimation due to the provided collision dataset of previous years, where the highest number of collisions occurred on the second ring road.