國立虎尾科技大學 |

Revolutionizing Urban Traffic Management Using Deep Reinforcement Learning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Revolutionizing Urban Traffic Management Using Deep Reinforcement Learning./
Author:	Dasaripally, Tanvi Reddy.
Description:	1 online resource (66 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-12.
Contained By:	Masters Abstracts International85-12.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798383058367

Revolutionizing Urban Traffic Management Using Deep Reinforcement Learning.
Dasaripally, Tanvi Reddy.

Revolutionizing Urban Traffic Management Using Deep Reinforcement Learning. - 1 online resource (66 pages)

Source: Masters Abstracts International, Volume: 85-12.

Thesis (M.S.)--Southern Illinois University at Carbondale, 2024.

Includes bibliographical references

This thesis explores the integration of Deep Reinforcement Learning (DRL) in urban traffic management, examining how these technologies can be adapted and their effectiveness in the context of growing intelligent city infrastructures. By thoroughly examining more than 30 advanced research studies, this work highlights the creative use of DRL and other related technologies, such as vehicle-to-everything (V2X) communications, to improve traffic flow, alleviate congestion, and facilitate the smooth operation of Connected and Automated Vehicles (CAVs) in urban settings. This thorough review emphasizes the potential of DRL to surpass traditional traffic management systems, showcasing its ability to adapt to complex and dynamic traffic scenarios.Nevertheless, this thesis delves deeper into the analysis to pinpoint notable gaps and challenges that continue to exist in the current research landscape. These tasks involve integrating DRL-based systems with existing traffic management infrastructures, developing DRL models that can be applied to various urban environments, moving from simulated studies to real-world implementation, and considering the ethical considerations of using AI in public areas. Tackling these challenges is essential for unlocking the full potential of DRL in urban traffic management.The proposed future directions aim to address the identified research gaps and envision a future where traffic management systems are more advanced, streamlined, and fair. This thesis asserts that by adopting these cutting-edge solutions, urban traffic management can make substantial progress, resulting in enhanced mobility, safety, and quality of life in urban centers across the globe. The effective execution of these proposals has the potential to revolutionize urban traffic management, creating a system that is more adaptable, environmentally friendly, and accessible. This will set the stage for the development of intelligent cities in the years to come.

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

Mode of access: World Wide Web

ISBN: 9798383058367Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Urban traffic managementIndex Terms--Genre/Form:

554714
Electronic books.

Revolutionizing Urban Traffic Management Using Deep Reinforcement Learning.
LDR:03355ntm a22003977 4500 001 1150171
005 20241022111606.5
006 m o d
007 cr bn ---uuuuu
008 250605s2024 xx obm 000 0 eng d
020 $a 9798383058367
035 $a (MiAaPQ)AAI31145935
035 $a AAI31145935
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Dasaripally, Tanvi Reddy. $3 1476606
245 1 0 $a Revolutionizing Urban Traffic Management Using Deep Reinforcement Learning.
264 0 $c 2024
300 $a 1 online resource (66 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 85-12.
500 $a Advisor: Hexmoor, Henry.
502 $a Thesis (M.S.)--Southern Illinois University at Carbondale, 2024.
504 $a Includes bibliographical references
520 $a This thesis explores the integration of Deep Reinforcement Learning (DRL) in urban traffic management, examining how these technologies can be adapted and their effectiveness in the context of growing intelligent city infrastructures. By thoroughly examining more than 30 advanced research studies, this work highlights the creative use of DRL and other related technologies, such as vehicle-to-everything (V2X) communications, to improve traffic flow, alleviate congestion, and facilitate the smooth operation of Connected and Automated Vehicles (CAVs) in urban settings. This thorough review emphasizes the potential of DRL to surpass traditional traffic management systems, showcasing its ability to adapt to complex and dynamic traffic scenarios.Nevertheless, this thesis delves deeper into the analysis to pinpoint notable gaps and challenges that continue to exist in the current research landscape. These tasks involve integrating DRL-based systems with existing traffic management infrastructures, developing DRL models that can be applied to various urban environments, moving from simulated studies to real-world implementation, and considering the ethical considerations of using AI in public areas. Tackling these challenges is essential for unlocking the full potential of DRL in urban traffic management.The proposed future directions aim to address the identified research gaps and envision a future where traffic management systems are more advanced, streamlined, and fair. This thesis asserts that by adopting these cutting-edge solutions, urban traffic management can make substantial progress, resulting in enhanced mobility, safety, and quality of life in urban centers across the globe. The effective execution of these proposals has the potential to revolutionize urban traffic management, creating a system that is more adaptable, environmentally friendly, and accessible. This will set the stage for the development of intelligent cities in the years to come.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Engineering. $3 561152
650 4 $a Urban planning. $3 1180826
653 $a Urban traffic management
653 $a DRL models
653 $a Deep Reinforcement Learning
653 $a Alleviate congestion
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0800
690 $a 0537
690 $a 0999
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Southern Illinois University at Carbondale. $b Computer Science. $3 1190734
773 0 $t Masters Abstracts International $g 85-12.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31145935 $z click for full text (PQDT)