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Transit Signal Priority Control with Connected Vehicle Technology : = Deep Reinforcement Learning Approach.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Transit Signal Priority Control with Connected Vehicle Technology :/
Reminder of title:	Deep Reinforcement Learning Approach.
Author:	Yang, Tianjia.
Description:	1 online resource (155 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
Contained By:	Dissertations Abstracts International85-10B.
Subject:	Transportation. -
Online resource:	click for full text (PQDT)
ISBN:	9798382307787

Transit Signal Priority Control with Connected Vehicle Technology : = Deep Reinforcement Learning Approach.
Yang, Tianjia.

Transit Signal Priority Control with Connected Vehicle Technology :Deep Reinforcement Learning Approach. - 1 online resource (155 pages)

Source: Dissertations Abstracts International, Volume: 85-10, Section: B.

Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2024.

Includes bibliographical references

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. In this study, we developed adaptive TSP control frameworks for both isolated intersection scenarios and multiple intersection scenarios, assuming the implementation of CV technology. Leveraging the comprehensive traffic data obtained from CVs, our frameworks employ both single-agent DRL and multi-agent DRL techniques to address optimization problems. The controllers, based on our proposed frameworks, were tested in simulation environments and compared with various widely used traffic signal controllers across different scenarios, demonstrating superior performance.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382307787Subjects--Topical Terms:

558117
Transportation.
Subjects--Index Terms:

Connected VehicleIndex Terms--Genre/Form:

554714
Electronic books.

Transit Signal Priority Control with Connected Vehicle Technology : = Deep Reinforcement Learning Approach.
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502 $a Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2024.
504 $a Includes bibliographical references
520 $a 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. In this study, we developed adaptive TSP control frameworks for both isolated intersection scenarios and multiple intersection scenarios, assuming the implementation of CV technology. Leveraging the comprehensive traffic data obtained from CVs, our frameworks employ both single-agent DRL and multi-agent DRL techniques to address optimization problems. The controllers, based on our proposed frameworks, were tested in simulation environments and compared with various widely used traffic signal controllers across different scenarios, demonstrating superior performance.
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653 $a Data fusion
653 $a Mixed traffic environment
653 $a Reinforcement learning
653 $a Traffic signal control
653 $a Transit Signal Priority
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