Potential Benefits of Animal-Detecting Automatic Emergency Braking Systems Based on U.S. Driving Data

In 2015, there were 319,195 police reported vehicle-animal crashes, resulting in 275 vehicle occupant fatalities. Animal-detecting automatic emergency braking (AEB) systems are a promising active safety measure which could potentially avoid or mitigate many of these crashes by warning the driver, utilizing automatic braking, or both. The purpose of this study was to develop and characterize a target population of vehicle-animal crashes applicable to AEB systems and to analyze the potential benefits of an animal-detecting AEB system. The study was based on two nationally representative databases, Fatality Analysis Reporting System and the National Automotive Sampling System’s General Estimates System, and a naturalistic driving study, SHRP 2. The target population was restricted to vehicle-animal crashes that were forward impacts or road departures and involved cars and light trucks, with no loss of control. Crash characteristics which may influence the performance of AEB such as lighting, weather, pre-crash movement, relation to junction, and first and worst harmful events, were analyzed. The study found that the major influences on the effectiveness of animal AEB systems were: weather, lighting, pre-crash movements, and the crash location. Six potential target populations were used to analyze the potential effectiveness of an animal AEB system, with effectiveness ranging between 21.6% and 97% of police reported crashes and between 4.1% and 50.8% of fatal vehicle-animal crashes. An AEB system’s ability to function in low light and poor weather conditions may enable it to avoid a substantially higher proportion of crashes.

Analysis of Severe Injuries in Crashes Involving Large Trucks Using K-Prototypes Clustering-Based GBDT Model

The unobserved heterogeneity in traffic crash data hides certain relationships between the contributory factors and injury severity. The literature has been limited in exploring different types of clustering methods for the analysis of the injury severity in crashes involving large trucks. Additionally, the variability of data type in traffic crash data has rarely been addressed. This study explored the application of the k-prototypes clustering method to countermeasure the unobserved heterogeneity in large truck-involved crashes that had occurred in the United States between the period of 2016 to 2019. The study segmented the entire dataset (EDS) into three homogeneous clusters. Four gradient boosted decision trees (GBDT) models were developed on the EDS and individual clusters to predict the injury severity in crashes involving large trucks. The list of input features included crash characteristics, truck characteristics, roadway attributes, time and location of the crash, and environmental factors. Each cluster-based GBDT model was compared with the EDS-based model. Two of the three cluster-based models showed significant improvement in their predicting performances. Additionally, feature analysis using the SHAP (Shapley additive explanations) method identified few new important features in each cluster and showed that some features have a different degree of effects on severe injuries in the individual clusters. The current study concluded that the k-prototypes clustering-based GBDT model is a promising approach to reveal hidden insights, which can be used to improve safety measures, roadway conditions and policies for the prevention of severe injuries in crashes involving large trucks.

Exploring the Impacts of Driving Environment on Crashes in Tunnel–Bridge–Tunnel Groups: An Eight-Zone Analytic Approach

Tunnel–bridge–tunnel groups (TBTGs) are emerging roads that often involve simple road alignments, but complex driving environments. Investigating crashes occurred in TBTGs is essential for revealing the driving environment–adaptability relationships for such roads. This study seeks to analyze the crash characteristics of component sections in TBTGs with different driving environments and compare the impact of differences in the key factor on the crashes. After TBTGs were defined through a proposed safety-critical distance metric determined via visual theory and actual crash analyses, an eight-zone analytical method considering road types and lighting was developed to probe into crashes in TBTGs. The results show that the proper safety-critical distances for bridge–tunnel and tunnel–tunnel groups are 150 and 500 m, respectively. In TBTGs, the crash rate in ordinary sections is higher than that in bridges and tunnels, particularly in the access zone. The first passed tunnel witnesses a higher proportion of crashes at the access zone and transition zone than the second tunnel. The influence of bridge and tunnel ratios on crashes is related to the ratio and type of bridges and tunnels. The findings presented herein can provide evidence-based guidance for the safety design and management of TBTGs.

Traffic Crash Characteristics in Shenzhen, China from 2014 to 2016

Road traffic crashes cause fatalities and injuries of both drivers/passengers in vehicles and pedestrians outside, thus challenge public health especially in big cities in developing countries like China. Previous efforts mainly focus on a specific crash type or causation to examine the crash characteristics in China while lacking the characteristics of various crash types, factors, and the interplay between them. This study investigated the crash characteristics in Shenzhen, one of the biggest four cities in China, based on the police-reported crashes from 2014 to 2016. The descriptive characteristics were reported in detail with respect to each of the crash attributes. Based on the recorded crash locations, the land-use pattern was obtained as one of the attributes for each crash. Then, the relationship between the attributes in motor-vehicle-involved crashes was examined using the Bayesian network analysis. We revealed the distinct crash characteristics observed between the examined levels of each attribute, as well the interplay between the attributes. This study provides an insight into the crash characteristics in Shenzhen, which would help understand the driving behavior of Chinese drivers, identify the traffic safety problems, guide the research focuses on advanced driver assistance systems (ADASs) and traffic management countermeasures in China.

Classification of road traffic injury collision characteristics using text mining analysis: Implications for road injury prevention

Road traffic injuries are a leading cause of morbidity and mortality globally. Understanding circumstances leading to road traffic injury is crucial to improve road safety, and implement countermeasures to reduce the incidence and severity of road trauma. We aimed to characterise crash characteristics of road traffic collisions in Victoria, Australia, and to examine the relationship between crash characteristics and fault attribution. Data were extracted from the Victorian State Trauma Registry for motor vehicle drivers, motorcyclists, pedal cyclists and pedestrians with a no-fault compensation claim, aged > = 16 years and injured 2010–2016. People with intentional injury, serious head injury, no compensation claim/missing injury event description or who died < = 12-months post-injury were excluded, resulting in a sample of 2,486. Text mining of the injury event using QDA Miner and Wordstat was used to classify crash circumstances for each road user group. Crashes in which no other was at fault included circumstances involving lost control or avoiding a hazard, mechanical failure or medical conditions. Collisions in which another was predominantly at fault occurred at intersections with another vehicle entering from an adjacent direction, and head-on collisions. Crashes with higher prevalence of unknown fault included multi-vehicle collisions, pedal cyclists injured in rear-end collisions, and pedestrians hit while crossing the road or navigating slow traffic areas. We discuss several methods to promote road safety and to reduce the incidence and severity of road traffic injuries. Our recommendations take into consideration the incidence and impact of road trauma for different types of road users, and include engineering and infrastructure controls through to interventions targeting or accommodating human behaviour.