Yao-Jan Wu

Associate Professor

University of Arizona

United States

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Yao-Jan Wu is an Associate Professor at the University of Arizona, United States. His research areas include traffic signal performance, pedestrian and driver interactions at intersections, and the application of machine learning in traffic management. Recent articles authored by Professor Wu focus on topics such as the effectiveness of traffic safety measures, optimization of vehicle detection, and modeling driver behaviors using crowdsourced data.

Articles (20)

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University of Arizona

UNITED STATES

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Yao-Jan Wu

Research Interests

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About

Articles (20)

Investigating nighttime driver behaviors and interactions at Pedestrian Hybrid Beacons

Evaluating the Effectiveness of Red-Light Running Cameras on Intersection Crash Outcomes and Professional Perspectives

Examining Left-Turn Speeds for Intersection Signal Timing Design Using Crowdsourced Trajectory Data

Modeling dilemma zone at urban signalized intersections using crowdsourced trajectory data

Evaluation of Factors Influencing Ramp Metering Compliance Using Controller Event–Based Data

Machine learning-based optimization framework for vehicle reidentification between detectors at signalized intersections

Impact of Extended Red Clearance Intervals on Left-Turn Red-Light Violations: A Time Series Analysis

Modeling Influence Area for Urban Signalized Intersections Using Crowdsourced Trajectory Data

Quality assurance for network-level traffic signal performance measures derived from connected-vehicle data

Incident Duration Sequential Predictions Using Reinforcement Learning for Advanced Traveler Information System Applications

Network-level turning movement counts estimation using traffic controller event-based data

Safety Performance Evaluation of Flashing Yellow Arrow: Time-of-Day Versus 24-Hour Operation

Arterial Signal Offset Optimization Using Crowdsourced Speed Data

Distributed Signal Control of Arterial Corridors Using Multi-Agent Deep Reinforcement Learning

Transit Arrival Time Prediction Using Interaction Networks

Eliminating the impacts of traffic volume variation on before and after studies: a causal inference approach

Severity analysis of red-light–running behavior at signalized intersections

Real-Time Queue Length Estimation for Signalized Intersections Using Single-Channel Advance Detector Data

Modeling red-light running behavior using high-resolution event-based data: a finite mixture modeling approach

Estimating pedestrian delay at signalized intersections using high-resolution event-based data: a finite mixture modeling method

Collaborators (5)

Alyssa Ryan

Abolfazl Karimpour

Henrick Haule

Adrian Cottam

Xiaofeng Li

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