The purpose of this study is to explore nonlinear and threshold effects of traffic statuses and road geometries, as well as their interactions, on traffic accident severity. In contrast to earlier research that primarily defined road alignment qualit...
INTRODUCTION: Urban green space (GS) exposure is recognized as a nature-based strategy for addressing urban challenges. However, the stress relieving effects and mechanisms of GS exposure are yet to be fully explored. The development of machine learn...
With the increasing global population and escalating ecological and farmland degradation, challenges to the environment and livelihoods have become prominent. Coordinating urban development, food security, and ecological conservation is crucial for f...
Examining the relationship between streetscape features and road traffic accidents is pivotal for enhancing roadway safety. While previous studies have primarily focused on the influence of street design characteristics, sociodemographic features, an...
OBJECTIVES: The paper develops a machine learning-based safety index for classifying traffic conflicts that can be used to estimate the frequency of signalized intersection crashes, with a focus on the more severe ones that result in fatal and severe...
This study addresses the issue of wrong-way driving (WWD) incidents at partial cloverleaf (parclo) interchange terminals in the United States. These incidents are a safety concern, often attributed to geometric design features and inadequate traffic ...
Examining the relationship between streetscape features and road traffic crashes is vital for enhancing roadway safety. Traditional field surveys are often inefficient and lack comprehensive spatial coverage. Leveraging street view images (SVIs) and ...
Predicting the duration of traffic incidents is challenging due to their stochastic nature. Accurate predictions can greatly benefit end-users by informing their route choices and safety warnings, while helping traffic operation managers more effecti...
OBJECTIVES: This study aims to develop and validate a novel deep-learning model that predicts the severity of pedestrian-vehicle interactions at unsignalized intersections, distinctively integrating Transformer-based models with Multilayer Perceptron...