Wei Fan

Professor

University of North Carolina at Charlotte

United States

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Wei Fan is a Professor at the University of North Carolina at Charlotte, specializing in transportation systems and urban mobility. His recent research focuses on environmental sustainability, connected and autonomous vehicles, and urban rail transit systems. Noteworthy topics include travel time reliability, trajectory prediction, and the optimization of transit schedules. Dr. Fan's work aims to enhance the robustness of traffic signal systems and address pedestrian safety in urban environments.

Wei Fan

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Articles (19)

Environmental sustainability and mobility impacts of connected and autonomous vehicles at urban highways

Measuring Route Travel Time Reliability in an Urban Rail Transit Network

CAVs - Will they ever be a mainstream transport mode?

Optimization of Periodic Train Schedule with Flexible Train Composition during Off-Peak Periods for Urban Rail Transit

Short-term trajectory prediction for individual metro passengers based on multi-level periodicity mining from semantic trajectory

The Performance of Connected and Autonomous Vehicles with Trajectory Planning in a Fixed Signal Controlled Intersection

Enhancing Robustness of Deep Reinforcement Learning Based Adaptive Traffic Signal Controllers in Mixed Traffic Environments Through Data Fusion and Multi-Discrete Actions

Performance of State-Shared Multiagent Deep Reinforcement Learning Controlled Signal Corridor with Platooning-Based CAVs

Intersection capacity adjustments considering different market penetration rates of connected and automated vehicles

Analyzing the Injury Severity in Overturn Crashes Involving Sport Utility Vehicles: Latent Class Clustering and Random Parameter Logit Model

Evaluation of transit signal priority at signalized intersections under connected vehicle environment

Modeling pedestrian injury severity in pedestrian-vehicle crashes considering different land use patterns: Mixed logit approach

Combining emerging hotspots analysis with XGBoost for modeling pedestrian injuries in pedestrian-vehicle crashes: a case study of North Carolina

A simulation study on the traffic delay and fuel consumption of connected and autonomous vehicles in superstreet with platooning, signal optimization, and trajectory planning

Dynamic speed harmonization for mixed traffic flow on the freeway using deep reinforcement learning

Emerging Technologies in the Electrification of Urban Mobility

Freeway Traffic Speed Prediction under the Intelligent Driving Environment: A Deep Learning Approach

Investigating contributing factors to injury severity levels in crashes involving pedestrians and cyclists using latent class clustering analysis and mixed logit models

Mixed logit models for examining pedestrian injury severities at intersection and non-intersection locations

Collaborators (2)

Li Song

Shaojie Liu

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