相关论文: Impact of risk factors on work zone crashes using …

This study introduces a deep learning-based framework for forecasting weather-related traffic crash risk using heterogeneous spatiotemporal data. Given the complex, non-linear relationship between crash occurrence and factors such as road…

To date, hundreds of crashes have occurred in open road testing of automated vehicles (AVs), highlighting the need for improving AV reliability and safety. Pre-crash scenario typology classifies crashes based on vehicle dynamics and…

Password security plays a crucial role in cybersecurity, yet traditional password strength meters, which rely on static rules like character-type requirements, often fail. Such methods are easily bypassed by common password patterns (e.g.,…

This research proposes a machine learning-based attack detection model for power systems, specifically targeting smart grids. By utilizing data and logs collected from Phasor Measuring Devices (PMUs), the model aims to learn system…

Existing neural network-based autonomous systems are shown to be vulnerable against adversarial attacks, therefore sophisticated evaluation on their robustness is of great importance. However, evaluating the robustness only under the…

Understanding and mitigating driving stress is vital for preventing accidents and advancing both road safety and driver well-being. While vehicles are equipped with increasingly sophisticated safety systems, many limits exist in their…

Freight truck-related crashes pose significant challenges, leading to substantial economic losses, injuries, and fatalities, with pronounced spatial disparities across different regions. This study adopts a transport geography perspective…

Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to 'mine' variables of interest…

Traffic accidents have been a severe issue in metropolises with the development of traffic flow. This paper explores the theory and application of a recently developed machine learning technique, namely Import Vector Machines (IVMs), in…

This study explores using different machine learning techniques and workflows to predict crime related statistics, specifically crime type in Philadelphia. We use crime location and time as main features, extract different features from the…

Trajectory prediction is significant for intelligent vehicles to achieve high-level autonomous driving, and a lot of relevant research achievements have been made recently. Despite the rapid development, most existing studies solely focused…

Density of the reachable states can help understand the risk of safety-critical systems, especially in situations when worst-case reachability is too conservative. Recent work provides a data-driven approach to compute the density…

This study bridges the knowledge gap on how personal factors affect building occupants' responses in active shooter situations by applying interpretable machine learning methods to data from 107 participants. The personal factors studied…

The paper introduces a new dataset to assess the performance of machine learning algorithms in the prediction of the seriousness of injury in a traffic accident. The dataset is created by aggregating publicly available datasets from the UK…

Understanding the key factors shaping environmental hazard exposures and their associated environmental injustice issues is vital for formulating equitable policy measures. Traditional perspectives on environmental injustice have primarily…

The occurrence of workplace accidents is described within the context of self-organized criticality, a theory from statistical physics that governs a wide range of phenomena across physics, biology, geosciences, economics, and the social…

The advance towards higher levels of automation within the field of automated driving is accompanied by increasing requirements for the operational safety of vehicles. Induced by the limitation of computational resources, trade-offs between…

Road fatalities pose significant public safety and health challenges worldwide, with pedestrians being particularly vulnerable in vehicle-pedestrian crashes due to disparities in physical and performance characteristics. This study employs…

In the last four years, the number of distinct autonomous vehicles platforms deployed in the streets of California increased 6-fold, while the reported accidents increased 12-fold. This can become a trend with no signs of subsiding as it is…

This study attempts to investigate the relationship between crash occurrence at signalized intersections and real-time traffic, signal timing, and weather characteristics based on 23 signalized intersections in Central Florida. The…