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Simulation and Analysis of Traffic C...

Wang, Chen.

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Simulation and Analysis of Traffic Congestion Prediction and Vehicle Re-Routing Strategy Using Image-Based Surveillance Camera.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Simulation and Analysis of Traffic Congestion Prediction and Vehicle Re-Routing Strategy Using Image-Based Surveillance Camera./
Author:	Wang, Chen.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	147 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
Contained By:	Dissertations Abstracts International85-03B.
Subject:	Transportation. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30567210
ISBN:	9798380336369

Simulation and Analysis of Traffic Congestion Prediction and Vehicle Re-Routing Strategy Using Image-Based Surveillance Camera.
Wang, Chen.

Simulation and Analysis of Traffic Congestion Prediction and Vehicle Re-Routing Strategy Using Image-Based Surveillance Camera. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 147 p.

Source: Dissertations Abstracts International, Volume: 85-03, Section: B.

Thesis (Ph.D.)--The University of Alabama, 2023.

This item must not be sold to any third party vendors.

Traffic flow management is crucial for intelligent transportation systems, as congestion in arterial areas, highways, during bad weather, and rush hours is increasingly prevalent. Efficient traffic flow detection, prediction, vehicle re-routing, and active travel planning are essential for transportation system management. However, upgrading hardware infrastructure like radar, cameras, and detection tools to keep up with evolving vehicle tracking algorithms is costly and time-consuming.This dissertation reviews different approaches to vehicle tracking, short-term congestion prediction, and mitigation. Based on previous research, a cost-effective integrated congestion awareness system called the heat-balancing path planning system is proposed. The system predicts and detects congestion, balances traffic flow, and reduces overall congestion potential. It comprises the same vehicle recognition, short-term congestion detection and prediction, and passive vehicle notification with dynamic re-routing. Leveraging existing traffic surveillance cameras, this method offers a viable solution for regions without additional hardware investments.The core methodology of the proposed system is inspired by thermal-transfer characteristics in materials. The model predicts congestion based on vehicle volume heat-density and traffic speed. Simulated annealing is used to suggest a traffic-balancing plan, and a dynamic re-routing algorithm is adapted from the k-shortest path algorithm. The model is implemented in a custom-designed simulated traffic flow environment, mimicking real-life conditions. Simulation tests validate the model's performance, preventing 28.75% of congestion, suppressing 63.39\\% of congestion within a minute, and increasing average travel speed to 72.92--76.15% of the speed limit.Compared to other approaches, the proposed method consistently outperforms, reducing travel time and maintaining higher average speeds. This advantage, combined with practical implications for transportation management, makes it a promising solution for modern traffic challenges.In summary, this dissertation introduces a cost-effective, integrated method for traffic flow management, featuring novel heat-balancing path plan algorithms. Simulation and comparisons with existing methods demonstrate their merits. This work has the potential to enhance transportation system efficiency, especially in regions with limited infrastructure resources.

ISBN: 9798380336369Subjects--Topical Terms:

555912
Transportation.
Subjects--Index Terms:

Algorithms

Simulation and Analysis of Traffic Congestion Prediction and Vehicle Re-Routing Strategy Using Image-Based Surveillance Camera.
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506 $a This item must not be sold to any third party vendors.
520 $a Traffic flow management is crucial for intelligent transportation systems, as congestion in arterial areas, highways, during bad weather, and rush hours is increasingly prevalent. Efficient traffic flow detection, prediction, vehicle re-routing, and active travel planning are essential for transportation system management. However, upgrading hardware infrastructure like radar, cameras, and detection tools to keep up with evolving vehicle tracking algorithms is costly and time-consuming.This dissertation reviews different approaches to vehicle tracking, short-term congestion prediction, and mitigation. Based on previous research, a cost-effective integrated congestion awareness system called the heat-balancing path planning system is proposed. The system predicts and detects congestion, balances traffic flow, and reduces overall congestion potential. It comprises the same vehicle recognition, short-term congestion detection and prediction, and passive vehicle notification with dynamic re-routing. Leveraging existing traffic surveillance cameras, this method offers a viable solution for regions without additional hardware investments.The core methodology of the proposed system is inspired by thermal-transfer characteristics in materials. The model predicts congestion based on vehicle volume heat-density and traffic speed. Simulated annealing is used to suggest a traffic-balancing plan, and a dynamic re-routing algorithm is adapted from the k-shortest path algorithm. The model is implemented in a custom-designed simulated traffic flow environment, mimicking real-life conditions. Simulation tests validate the model's performance, preventing 28.75% of congestion, suppressing 63.39\\% of congestion within a minute, and increasing average travel speed to 72.92--76.15% of the speed limit.Compared to other approaches, the proposed method consistently outperforms, reducing travel time and maintaining higher average speeds. This advantage, combined with practical implications for transportation management, makes it a promising solution for modern traffic challenges.In summary, this dissertation introduces a cost-effective, integrated method for traffic flow management, featuring novel heat-balancing path plan algorithms. Simulation and comparisons with existing methods demonstrate their merits. This work has the potential to enhance transportation system efficiency, especially in regions with limited infrastructure resources.
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