Detecting traffic accidents as rapidly as possible is essential for traffic safety. In this study, we use eXtreme Gradient Boosting (XGBoost)-a Machine Learning (ML) technique-to detect the occurrence of accidents using a set of real time data compri...
Crash Detection is essential in providing timely information to traffic management centers and the public to reduce its adverse effects. Prediction of crash risk is vital for avoiding secondary crashes and safeguarding highway traffic. For many years...
Real-time crash risk prediction is expected to play a crucial role in preventing traffic accidents. However, most existing studies only focus on freeways rather than urban arterials. This paper proposes a real-time crash risk prediction model on arte...
The continuous motorization of traffic has led to a sustained increase in the global number of road related fatalities and injuries. To counter this, governments are focusing on enforcing safe and law-abiding behavior in traffic. However, especially ...
Predicting crash propensity helps study safety on urban expressways in order to implement countermeasures and make improvements. It also helps identify and prevent crashes before they happen. However, collecting real-time wide-coverage traffic inform...
Detecting accidents is of great importance since they often impose significant delay and inconvenience to road users. This study compares the performance of two popular machine learning models, Support Vector Machine (SVM) and Probabilistic Neural Ne...
As vehicles with automated functions become more prevalent on U.S. roadways, maintaining driver attention while the vehicle is engaged in automation will be an important consideration for safe operation of these vehicles. The objective of this paper ...
The advent of autonomous vehicles (AVs) has gained increasing attention in China. Although auto manufacturers and innovators have attempted to confirm that AVs are safe and have introduced them on public roads, it is vital to understand end-users' ac...
This study presents the work in predicting crash risk on expressways with consideration of both the impact of safety critical events and traffic conditions. The traffic resilience theory is introduced to learn safety problems from the standpoint of 1...
Negotiations among drivers and pedestrians are common on roads, but it is still challenging for a self-driving vehicle to negotiate for its right of way with other human road users, especially pedestrians. Currently, the self-driving vehicles are pro...