Exploring Influential Factors Affecting the Severity of Urban Expressway Collisions: A Study Based on Collision Data

Abstract

When traffic collisions occur on urban expressways, the consequences, including injuries, the loss of lives, and damage to properties, are more serious. However, the existing research on the severity of expressway traffic collisions has not been deeply explored. The purpose of this research was to investigate how various factors affect the severity of urban expressway collisions. The severity of urban expressway collisions was set as the dependent variable, which could be divided into three categories: slight collisions, severe collisions, and fatal collisions. Ten variables, including individual characteristics, collision characteristics, and road environment conditions, were selected as independent factors. Based on 975 valid urban expressway collisions, an ordered logistic regression model was established to evaluate the impacts of influence factors on the severity of these crashes. The results show that gender, collision modality, road pavement conditions, road surface conditions, and visibility are significant factors that affect the severity of urban expressway collisions. Females were more likely to be involved in more severe urban expressway collisions than males. For collisions involving pedestrians and non-motorized vehicles, the risk of more severe injury was 7.508 times higher than that associated with vehicle–vehicle collisions. The probability of more severe collisions on urban expressways with poor pavement conditions and wet surface conditions is greater than that on urban expressways with good pavement conditions and dry surface conditions. In addition, as visibility increases, the probability of more severe collisions on urban expressways gradually decreases. These results provide more effective strategies to reduce casualties as a result of urban expressway collisions.

Leveraging LiDAR-Based Simulations to Quantify the Complexity of the Static Environment for Autonomous Vehicles in Rural Settings

“…The onboard computer equipped by the AVs, which is required to operate in real- time, has the onus of perceiving the environment surrounding the AV, processing the incoming information, and making the most apt decisions to ensure the safe operations of the ego vehicle. The traffic environment poses a big challenge for autonomous systems as they are typically open, non-deterministic, hard to predict, and dynamic [ 20 ]. Identifying the complex situations is essential in advancing AVs’ safety, which would expedite their mass adoption.…”

“…The main contribution of this work is the development of a novel framework that can estimate the AV environment complexity using a transformative function that can translate the dynamicity of the surrounding environment [ 20 ] along with the AV sensor capabilities and the influence of weather conditions into interpretable data rate values via lidar-based simulations using the Vista simulator [ 21 ]. This framework excels in detecting the critical areas within the physical infrastructure for AVs while also quantifying their complexity levels which is an unprecedented feat in the literature.…”

An over-the-horizon potential safety threat vehicle identification method based on ETC big data

“…Therefore, it is crucial to appropriately divide the over-the-horizon identification zone. Whether one vehicle poses a safety threat to another vehicle depends not only on the speed and distance between them but also on the real-time driving environment and the mechanical performance of the vehicles [ [42] , [43] , [44] , [45] ].…”

The Identification of Intersection Entrance Accidents Based on Autoencoder

“…Due to its high detection accuracy and short detection time, the method based on the variation characteristics of traffic parameters is widely used in the accident identification of continuous-flow highways [29] and urban expressways. Using machine learning and deep learning algorithms, this method identifies accidents by primarily analyzing traffic volume, density, and speed data.…”