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研究生:Ivander William
研究生(外文):Ivander William
論文名稱:基於SUMO與Q-Learning 結合加權演算法進行路口交通流量最佳化研究
論文名稱(外文):A Study on Intersection Traffic Flow Optimization Using SUMO and Q Learning with Weighted Algorithm
指導教授:周碩彥周碩彥引用關係郭伯勳
指導教授(外文):Shuo-Yan ChouPo-Hsun Kuo
口試委員:周碩彥郭伯勳羅士哲
口試日期:2024-01-25
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:工業管理系
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:英文
論文頁數:42
外文關鍵詞:SumoQ LearningSimulationAgent Based ModellingTraffic Flow
相關次數:
  • 被引用被引用:0
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  • 下載下載:12
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This study addresses the challenge of optimizing traffic flow at intersections with the dual objectives of reducing average waiting times and increasing the number of vehicles passing through. To achieve this, we employ a combination of Simulation of Urban MObility (SUMO) and Q-Learning with a Weighted Algorithm.
Our methodology involves simulating various traffic scenarios using SUMO, a state-of-the-art traffic simulation tool, to model real-world traffic conditions. We implement Q-Learning, a reinforcement learning technique, to dynamically adjust traffic signal timings at the intersections. The novel aspect of our approach lies in the incorporation of a Weighted Algorithm, which prioritizes minimizing waiting times while balancing the overall traffic volume to achieve reduce trip duration for the traffics.
Our findings demonstrate a significant improvement in traffic flow optimization compared to traditional signal control methods. The trip duration at intersections are noticeably reduced, leading to enhanced traffic efficiency. Simultaneously, we observe a notable increase in the number of vehicles successfully passing through intersections, contributing to a smoother and more efficient traffic system.
The implications of our research extend to urban planning and transportation management, offering a practical solution to mitigate traffic congestion and improve overall traffic flow in urban areas. This study represents a step forward in the quest for sustainable and efficient urban transportation systems.
TABLE OF CONTENTS

CHAPTER 1 INTRODUCTION 1
1.1 BACKGROUND 1
1.2 OBJECTIVES 2
1.3 SCOPE AND LIMITATIONS 2
1.4 ORGANIZATIONS OF THESIS 3
CHAPTER 2 LITERATURE REVIEW 5
2.1 TYPE OF ROAD USERS 5
2.2 OBJECTIVES 6
2.3 SOLUTION METHOD 7
CHAPTER 3 METHODOLOGY 10
3.1 SIMULATION OF URBAN MOBILITY (SUMO) 10
3.2 REINFORCEMENT LEARNING IN TRAFFIC SIGNAL CONTROL 16
CHAPTER 4 RESULTS AND DISCUSSION 22
4.1 DATA PREPARATION 22
4.2 DATA DISTRIBUTION 23
4.3 SCENARIO 24
4.4 SIMULATION 25
4.5 SIMULATION RESULT ANALYSIS 30
CHAPTER 5 CONCLUSION & FUTURE WORK 38
5.1 CONCLUSION 38
5.2 RECOMMENDATION AND FUTURE RESEARCH 38
REFERENCES 40
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