Pengfei (Taylor) Li

Assistant Professor

The University of Texas at Arlington

United States

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About

Pengfei (Taylor) Li is an Assistant Professor at the University of Texas Arlington, USA. His research focuses on traffic signal performance measures, congestion ranking methodologies, and the application of connected vehicle data in traffic management. Recent articles include work on adaptive traffic signal control frameworks, performance evaluation using high-resolution data, and innovative strategies for improving pedestrian safety at intersections.

Articles (10)

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2024

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2024

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2024

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2023

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2023

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2023

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2022

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2022

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2020

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2020

Collaborators (1)

Stephen Mattingly

Professor

The University of Texas at Arlington

UNITED STATES

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Pengfei (Taylor) Li

Research Interests

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Articles (10)

Automated Traffic Signal Performance Measures (ATSPMs) in the Loop Simulation: A Digital Twin Approach

A Graphical Approach to Automated Congestion Ranking for Signalized Intersections Using High-Resolution Traffic Signal Event Data

Multicriteria Planning Framework for Regional Intersection Improvement Using Telematics Data of Connected Vehicles

A New Framework for Regional Traffic Volumes Estimation with Large-Scale Connected Vehicle Data and Deep Learning Method

Developing a Tracking-Based Dynamic Flash Yellow Arrow Strategy for Permissive Left-Turn Vehicles to Improve Pedestrian Safety at Intersections

Congestion-aware heterogeneous connected automated vehicles cooperative scheduling problems at intersections

Developing Novel Performance Measures for Traffic Congestion Management and Operational Planning Based on Connected Vehicle Data

Development of an Adaptive Traffic Signal Control Framework for Urban Signalized Interchanges Based on Infrastructure Detectors and CAV Technologies

Actuated Traffic Signal Performance Evaluation along Arterials using Wi-Fi Travel Time Samples and High-Resolution Traffic Signal Events Data

A mixed integer programming formulation and scalable solution algorithms for traffic control coordination across multiple intersections based on vehicle space-time trajectories

Collaborators (1)

Stephen Mattingly

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