Examining driver injury severity outcomes in rural non-interstate roadway crashes using a hierarchical ordered logit model

Gaweesh

Transportation Research Record: Journal of the Transportation R

2020

Crash severity of a hazardous material (HAZMAT) transporting truck increases manyfold compared with normal truck crash because of the possible exposure to dangerous substances. Crashes which involve a HAZMAT truck might result in a catastrophic incident causing horrendous damage to individuals involved in the crash. In-transit HAZMAT crashes in Wyoming caused a total damage of $3.1 million from 2015 to 2018. HAZMAT crashes on interstate roads represented 22% of the total HAZMAT crashes causing a total damage of $2.2 million, representing 71% of the cost of total damage. Previous studies in Wyoming investigated all vehicle crashes, including large truck crashes, but none has analyzed HAZMAT-related crashes or accounted for its type as a contributing factor. This study fills the gap by analyzing crash injury severity of HAZMAT-related crashes on all interstate freeways in Wyoming. Furthermore, the study introduces the No-U-Turn (NUT) Hamiltonian Monte Carlo (HMC) method of hierarchical Bayesian analysis into HAZMAT crash injury severity analysis. In recent developments, NUT HMC has been proven to be the most efficient Markov Chain Monte Carlo (MCMC) sampling method. The results showed that 30% of the unobserved heterogeneity arises from variation in summer and winter crashes which justifies the use of hierarchical model. Among the other covariates investigated, the population-averaged effects showed that number of trucks involved, hit-and-run crashes, animal-vehicle crashes, work-zone-related crashes, collision type, percentage of females involved, drivers’ drug/alcohol use, seat-belt use, crash location, roadway curves, and surface conditions significantly impact HAZMAT crash injury severity.

Section: Introductionmentioning

confidence: 99%

Exploration of Hazardous Material Truck Crashes on Wyoming’s Interstate Roads using a Novel Hamiltonian Monte Carlo Markov Chain Bayesian Inference

Gaweesh

Transportation Research Record: Journal of the Transportation R

2020

Period. Polytech. Civil Eng.

“…Schneider et al [7] involved the development of multinomial logit models to assess driver injury severity resulting from single-vehicle crashes on such roads. Chen et al [8] used ordered logit model to examine the significant factors in predicting driver injury severities in rural non-interstate crashes. Results indicate that the model employed in this study outperforms ordinary ordered logit model in model fit and parameter estimation.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Road Shoulder and Weather Conditions on the Occurrence of Rollover Crashes in Two-lane Highways

Kordani¹,

Shirini²,

Yazdani³

2019

Rollover crash is a type of dangerous crash that occurs often in two-lane highways. Therefore, this study evaluates the effect of road shoulder and weather condition on rollover crashes. Crash data show that 8,609 crashes were recorded from 2006 to 2009 on six two-lane highways located at the center of Iran. This data contains 1860 rollover crashes. Rollover crash in this paper not included the head on crashes and fixed object crashes. Therefore the rollover crashes are only single vehicle crashes. Binary logit was selected for modelling since there were two possible outcomes: rollover crashes and other crash types. The variables of the final model include highway class, road shoulder width, as well as rainy, snowy, foggy and night conditions. The modeling results show that rainy and foggy variables, with coefficients of 0.731 and 0.719 respectively, had the greatest effects on rollover crash occurrence. Also, road shoulder width and night conditions, with coefficients of 0.221 and 0.184 respectively, had the least effects on rollover crash occurrence. Afterward, sensitivity analysis was performed on all the independent variables, and the results show variable variation and indicate that the probability of rollover crash occurrence is 21.29 % on the mentioned highway.

“…Due to the discrete nature of injury severity classes, it is often analyzed by discrete outcome statistical models such as binary, multinomial, probit, and logit models [ 26 , 27 , 28 , 29 ]. It is widely agreed that crash data may exhibit unobserved heterogeneity, which may be tackled by adopting other advanced statistical models such as ordered logit models [ 26 , 30 , 31 , 32 ], bivariate/multivariate models [ 33 , 34 , 35 , 36 ], random parameter model, [ 37 , 38 ], nested logit model [ 39 , 40 ], and Bayesian hierarchical models [ 41 , 42 ].…”

Section: Related Workmentioning

confidence: 99%

Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network

Jamal

Umer

2020

IJERPH

A better understanding of circumstances contributing to the severity outcome of traffic crashes is an important goal of road safety studies. An in-depth crash injury severity analysis is vital for the proactive implementation of appropriate mitigation strategies. This study proposes an improved feed-forward neural network (FFNN) model for predicting injury severity associated with individual crashes using three years (2017–2019) of crash data collected along 15 rural highways in the Kingdom of Saudi Arabia (KSA). A total of 12,566 crashes were recorded during the study period with a binary injury severity outcome (fatal or non-fatal injury) for the variable to be predicted. FFNN architecture with back-propagation (BP) as a training algorithm, logistic as activation function, and six number of hidden neurons in the hidden layer yielded the best model performance. Results of model prediction for the test data were analyzed using different evaluation metrics such as overall accuracy, sensitivity, and specificity. Prediction results showed the adequacy and robust performance of the proposed method. A detailed sensitivity analysis of the optimized NN was also performed to show the impact and relative influence of different predictor variables on resulting crash injury severity. The sensitivity analysis results indicated that factors such as traffic volume, average travel speeds, weather conditions, on-site damage conditions, road and vehicle type, and involvement of pedestrians are the most sensitive variables. The methods applied in this study could be used in big data analysis of crash data, which can serve as a rapid-useful tool for policymakers to improve highway safety.