Traffic management systems play a vital role in ensuring safe and efficient transportation on
roads. However, the use of advanced technologies in traffic management systems has …

Real-time prediction of crash risk is an effective method for enhancing traffic safety, but it is
not fully explored in freeway tunnels. A two-stage deep learning modeling framework …

Despite the recent advancements that Autonomous Vehicles have shown in their potential to
improve safety and operation, considering differences between Autonomous Vehicles and …

As the problems with managing traffic in cities get worse, this paper looks at a way to make it
easier to judge safety and handle risks in the flow of traffic on major roads in cities. By …

Proactive evacuation traffic management largely depends on real-time monitoring and
prediction of traffic flow at a high spatiotemporal resolution. However, evacuation traffic …

Hurricane evacuation modeling is challenging due to a scarcity of evacuation data and the
complexity of human decision-making and travel behavior. We build a system for rapidly …

Large-scale real-time crash prediction is critical to traffic operation and management, but
very challenging, even for machine learning models because the observation data are not …

Natural hazards, such as wildfires, pose a significant threat to communities worldwide. Real-
time forecasting of travel demand during wildfire evacuations is crucial for emergency …

Real-time crash and severity prediction is a complex task, and there is no existing framework
to predict crash likelihood and severity together. Creating such a framework poses …

Until recently, statistical approaches used for real-time crash prediction modeling were
limited to case-control design and “sampling of alternatives” approaches. A recent study has …