Tuscaloosa , AL
United States
University of Alabama
shared mobility, Shared Autonomous Vehicles, Small Urban Area, Road Network, Synthetic Trip Data
Police reported crash data, Data quality, Road safety improvement, International comparability, High-income countries, Low- and middle-income countries
Speeding, Injury severity, Spatial Machine Learning, Geographically Weighted Neural Network (GWNN)
Latent class analysis, Pedestrian crash, Low- and middle-income countries, Injury severity, Road safety
AI Infrastructure, Highways, Cultural morphology, Data Centers
Pedestrian crashes, Urban Road Safety, Crash severity, Random-parameters model, Systemic Safety
Indigenous knowledge, Road safety perceptions, Beliefs, Experiential knowledge, Sierra Leone
Large truck, at-fault, Crash severity, injury
Shared Autonomous Vehicles, Dynamic Ride-Sharing, Small and Medium-sized Urban Area, Road Network, Agent-based Modeling
electric vehicle, state tax revenues, transportation infrastructure funding
roadside trees, vehicle-tree crashes, machine learning, road safety
