In road safety, real-time crash prediction may play a crucial role in preventing such traffic events. However, much of the research in this line generally uses data aggregated every five or ten minutes. This article proposes a new image-inspired data...
It is well known that pedestrians are vulnerable road users. Their risk of being injured or killed in road traffic crashes is even higher as vehicle drivers often violate traffic rules and do not slow down or yield in front of crosswalks. In order to...
An innovative approach for real-time road safety analysis is presented in this work. Unlike traditional real-time crash prediction models (RTCPMs), in which crash data are used in the training phase, a real-time conflict prediction model (RTConfPM) i...
Vulnerable road users (VRUs) are exposed to the highest risk in the road traffic environment. Analyzing contributing factors that affect injury severity facilitates injury severity prediction and further application in developing countermeasures to g...
The primary objective of this study was to evaluate the impacts of traffic states on crash risk in the vicinities of Type A weaving segments. A deep convolutional embedded clustering (DCEC) was developed to classify traffic flow into nine states. The...
Crash data analysis is commonly subjected to imbalanced data. Varied by facility and control types, some crash types are more frequent than others. However, uncommon crash types are routinely more severe and associated with higher economic and societ...
Identifying factors that are associated with the probability of roadside work zone collisions enables decision makers to better assess and control the risk of scheduling a particular maintenance or construction activity by modifying the characteristi...
Hit-and-run crashes not only degrade the morality, but also result in delays of medical services provided to victims. However, class imbalance problem exists as the number of hit-and-run crashes is much smaller than that of non-hit-and-run crashes. T...
Highway work zones are most vulnerable roadway segments for congestion and traffic collisions. Hence, providing accurate and timely prediction of the severity of traffic collisions at work zones is vital to reduce the response time for emergency unit...
Vehicle automation safety must be evaluated not only for market success but also for more informed decision-making about Automated Vehicles' (AVs) deployment and supporting policies and regulations to govern AVs' unintended consequences. This study i...