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Smart intersections have the potential to improve road safety with sensing, communication, and edge computing technologies. Perception sensors installed at a smart intersection can monitor the traffic environment in real time and send…

Nowadays, traffic monitoring systems have access to real time data, e.g. through GPS devices. We propose a new traffic model able to take into account these data and, hence, able to describe the effects of unpredictable accidents. The well…

Power outages caused by extreme weather events, such as hurricanes, can significantly disrupt essential services and delay recovery efforts, underscoring the importance of enhancing our infrastructure's resilience. This study investigates…

To minimize property loss and death count in terror attacks and other emergent scenarios, attention given to timely and effective evacuation cannot be enough. Due to limited evacuation resource, i.e., number of available exits, there exists…

To operate in open-ended environments where humans interact in complex, diverse ways, autonomous robots must learn to predict their behaviour, especially when that behavior is potentially dangerous to other agents or to the robot. However,…

Connected vehicles (CVs) can capture and transmit detailed data through vehicle-to-vehicle and vehicle-to-infrastructure communications, which bring new opportunities to improve the safety, mobility, and sustainability of transportation…

Road accidents have a high societal cost that could be reduced through improved risk predictions using machine learning. This study investigates whether telemetric data collected on long-distance trucks can be used to predict the risk of…

Many automotive applications, such as Advanced Driver Assistance Systems (ADAS) for collision avoidance and warnings, require estimating the future automotive risk of a driving scene. We present a low-cost system that predicts the collision…

Principled decision making in emergency response management necessitates the use of statistical models that predict the spatial-temporal likelihood of incident occurrence. These statistical models are then used for proactive stationing…

Motion planning at urban intersections that accounts for the situation context, handles occlusions, and deals with measurement and prediction uncertainty is a major challenge on the way to urban automated driving. In this work, we address…

Cooperative Adaptive Cruise Control (CACC) is an autonomous vehicle-following technology that allows groups of vehicles on the highway to form in tightly-coupled platoons. This is accomplished by exchanging inter-vehicle data through…

Introduction: This paper reviewed current driving automation (DA) and baseline human-driven crash databases and evaluated their comparability. Method: Five sources of DA crash data and three sources of human-driven crash data were reviewed…

Major challenges of assisting passengers to safely and quickly escape from ships when an emergency occurs, include complex realistic features such as human behavior uncertainty, dynamic human traversal times, and the computation and…

Reducing traffic accidents is an important public safety challenge, therefore, accident analysis and prediction has been a topic of much research over the past few decades. Using small-scale datasets with limited coverage, being dependent…

Major disasters such as wildfire, tornado, hurricane, tropical storm, flooding cause disruptions in infrastructure systems such as power outage, disruption to water supply system, wastewater management, telecommunication failures, and…

Speeding has been and continues to be a major contributing factor to traffic fatalities. Various transportation agencies have proposed speed management strategies to reduce the amount of speeding on arterials. While there have been various…

Utilities face the challenge of responding to power outages due to storms and ice damage, but most power grids are not equipped with sensors to pinpoint the precise location of the faults causing the outage. Instead, utilities have to…

Perceived risk is crucial in designing trustworthy and acceptable vehicle automation systems. However, our understanding of its dynamics is limited, and models for perceived risk dynamics are scarce in the literature. This study formulates…

Traditional automated crash analysis systems heavily rely on static statistical models and historical data, requiring significant manual interpretation and lacking real-time predictive capabilities. This research presents an innovative…

This study uses connected vehicle data to analyze speeding behavior on residential roads. A scalable pipeline processes trajectory data and supplements missing speed limits to generate summaries at OpenStreetMap's way ID level. The findings…