thesis on traffic management

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Traffic Management System Full Thesis

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Home > Student Scholarship > Theses and Dissertations > Graduate > 436

Graduate Theses and Dissertations

Design of an intelligent traffic management system.

Amin Azimian , University of Dayton

Date of Award

Degree name.

M.S. in Civil Engineering

Department of Civil and Environmental Engineering and Engineering Mechanics

Advisor/Chair

Advisor: Deogratias Eustace

Due to present-day significant increases in population and consequently in traffic congestion in most metropolitan cities in the world, designing of an intelligent traffic management system (ITMS) in order to detect the path with the shortest travel time is critical for emergency, health, and courier services. The aim of this thesis study was to develop a theoretical traffic detection system and capable of estimating the travel time associated with each street segment based on the traffic data updated every 20 seconds, which successively finds the path with the shortest travel time in the network by using a dynamic programming technique. Furthermore, in this study we model the travel time associated with each street segment based on the historical and real time data considering that the traffic speed on each road segment is piecewise constant. It would be useful to implement such algorithms in GIS systems such as Google map in such a way that the service delivery drivers can avoid congested routes by receiving real time traffic information.

Route choice Mathematical models, Traffic flow Mathematical models, Traffic estimation Mathematical models, Travel time (Traffic engineering) Research, Geographic information systems

Rights Statement

Copyright © 2011, author

Recommended Citation

Azimian, Amin, "Design of an intelligent traffic management system" (2011). Graduate Theses and Dissertations . 436. https://ecommons.udayton.edu/graduate_theses/436

Since April 20, 2018

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Home > Graduate School > ETD > Theses > 3211

REVOLUTIONIZING URBAN TRAFFIC MANAGEMENT USING DEEP REINFORCEMENT LEARNING

Tanvi Reddy Dasaripally , Southern Illinois University Carbondale Follow

Date of Award

Degree name.

Master of Science

Computer Science

First Advisor

Hexmoor, Henry

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.

Since July 29, 2024

This thesis is only available for download to the SIUC community. Current SIUC affiliates may also access this paper off campus by searching Dissertations & Theses @ Southern Illinois University Carbondale from ProQuest. Others should contact the interlibrary loan department of your local library or contact ProQuest's Dissertation Express service .

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