Transit Signal Priority Control With Connected Vehicle Technology: Deep Reinforcement Learning Approach

Fan, Wei; Yang, Tianjia; University of North Carolina at Charlotte. Department of Civil and Environmental Engineering; University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education

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Transit Signal Priority Control With Connected Vehicle Technology: Deep Reinforcement Learning Approach

2024-09-01
By Fan, Wei ; Yang, Tianjia

[PDF-3.85 MB]

English

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Creators:

Fan, Wei ; Yang, Tianjia

Fan, Wei ; Yang, Tianjia Less -
Corporate Creators:

University of North Carolina at Charlotte. Department of Civil and Environmental Engineering ; University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education

University of North Carolina at Charlotte. Department of Civil and Environmental Engineering ; University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education Less -
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology Less -
Subject/TRT Terms:

[+]

Autonomous Vehicles Connected Vehicles Highway Capacity Machine Learning Mobility Signalized Intersections Sustainable Transportation Traffic Congestion Traffic Signal Priority Traffic Signal Timing Traffic Simulation
Publication/ Report Number:

2022 Project 17
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report
Corporate Publisher:

University of North Carolina at Charlotte. Center for Advanced Multimodal Mobility Solutions and Education
Abstract:

Transit Signal Priority (TSP) is a traffic signal control strategy that can provide priority to transit vehicles and thus improve transit service. However, this control strategy generally causes adverse effects on other traffic, which limits its widespread adoption. The development of Connected Vehicle (CV) technology enables the real-time acquisition of fine-grained traffic information, providing more comprehensive data for the optimization of traffic signals. Simultaneously, optimization algorithms in the field of TSP have been advancing at a rapid pace. Artificial intelligent (AI)-powered techniques, such as Deep Reinforcement Learning (DRL), have become promising approaches for addressing TSP problems recently.
Format:

PDF
Funding:

69A3551747133
Collection(s):

University Transportation Centers
Main Document Checksum:

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urn:sha-512:cc06812b8576e1d8eeed90efa48a129d2a9304488a9d52b7353f4b58c7faef6c03b90d9ad6c0502dac18f5fd45e2d3c70207428ff4b4cd5fef532cfee8729bdb
Download URL:

https://rosap.ntl.bts.gov/view/dot/77807/dot_77807_DS1.pdf
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Transit Signal Priority Control With Connected Vehicle Technology: Deep Reinforcement Learning Approach

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