A Bayesian spatial random parameters Tobit model for analyzing crash rates on roadway segments
Critical factors and paths influencing construction workers’ safety risk tolerances
Critical factors and paths influencing construction workers' safety risk tolerances
Analyzing pedestrian crash injury severity at signalized and non-signalized locations
Comparison of four statistical and machine learning methods for crash severity prediction
Driver drowsiness detection based on non-intrusive metrics considering individual specifics
The influence of daily sleep patterns of commercial truck drivers on driving performance
Macroscopic hotspots identification: A Bayesian spatio-temporal interaction approach
A multivariate spatial crash frequency model for identifying sites with promise based on crash types
The effects of texting on driving performance in a driving simulator: the influence of driver age
Augmented reality warnings in vehicles: Effects of modality and specificity on effectiveness
A multivariate random-parameters Tobit model for analyzing highway crash rates by injury severity
Understanding the role of sleep quality and sleep duration in commercial driving safety
Effects of fog, driver experience and gender on driving behavior on S-curved road segments
Evaluating the safety impact of adaptive cruise control in traffic oscillations on freeways
Child pedestrian safety knowledge, behaviour and road injury in Cape Town, South Africa
Comparing spatially static and dynamic vibrotactile take-over requests in the driver seat
The economic burden of injury: Health care and productivity costs of injuries in the Netherlands
Cost and benefit estimates of partially-automated vehicle collision avoidance technologies
Rule extraction from an optimized neural network for traffic crash frequency modeling
Effectiveness of low speed autonomous emergency braking in real-world rear-end crashes
Validation of the Driver Behaviour Questionnaire in a representative sample of drivers in Australia
Prioritizing Highway Safety Manual’s crash prediction variables using boosted regression trees