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研究生:王國川
研究生(外文):Kuo-ChuanWang
論文名稱:應用模糊類神經的交通壅塞控制於車載網路
論文名稱(外文):Using Fuzzy Neural Model to Reduce Traffic Congestion in VANET
指導教授:鄭憲宗鄭憲宗引用關係
指導教授(外文):Sheng-Tzong Cheng
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:66
中文關鍵詞:模糊邏輯控制類神經網路模糊類神經控制交通控制壅塞控制
外文關鍵詞:Neural networkfuzzy logic controlIntelligent Transportation System (ITS)intersection delay predictionmulti-module systemurban traffic signal controltraffic congestion
相關次數:
  • 被引用被引用:1
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  • 下載下載:30
  • 收藏至我的研究室書目清單書目收藏:0
近年來,車載網路應用於交通控制的重要性已經慢慢浮現,其發展已引起各領域學者的興趣與關注,如何透過車與車通訊以及車與路邊設備(Road-Side Unit)聯繫達到高安全性、高穩定度、低延遲的綠色能源環境被視為下一代新的研究鑽研方向。
本研究之目的在於提出一套適用減少交通壅塞以及針對不同程度的訊息達到即時的交通控制,例如救護車輛、警車、一般道路駕駛人,透過車間路邊設備的傳輸,即時傳遞到各路口的交通號誌控制,達到自動控制且即時的號誌控制規畫,在一般時段,可以有效的減少路口的壅塞程度,在帶有不同程度的車輛接近時,能夠動態的調整綠燈時序及燈號週期,讓上述的車輛可以快速通過,並平衡交通狀況,減少該高優先權車輛在未依照號誌通過路口時意外事故的機率,此機制結合模糊類神經網路技術可使得應用領域更有彈性。由於在模糊邏輯控制上可以有效的即時處理交通訊息,而搭配類神經網路更可以增加其運算學習彈性。因此在本研究中,除了找出相關項目的控制機制是研究重點之外,對交通的控制分析也是目標之一。從以往過去交通控制模型,應用於模糊控制理論以及類神經網路推論出對於目前最有效率的控制模型,進而產生出號誌控制決策。
本研究主要採用的方式是以多模組(multi-module)資訊處理模組以及路口號誌控制單元,前者為主要訊息傳遞中心,後者為本論文中的模糊類神經決策控制。以單一路口為中心控制為基礎,透過模糊類神經依據目前路口狀況做即時決策,並在收到優先權車輛的訊息時,利用燈號控制平衡並協調交通狀況。在一般狀況下,可以有效的控制並平衡交通壅塞。

The aim of our model is to design and propose a model using fuzzy logic with neural network based on different priority such as emergency vehicles, normal cars, and motorcycles to control the traffic light systems to reduce the traffic congestion and help vehicles with different priority pass through.
Using Fuzzy Neural Network (FNN) to calculate the traffic light system extends or terminates the green signal according to the traffic situation at the junction while also computing from adjacent intersections. On the presence of emergency vehicles, the system decides which signal(s) should be red and how much an extension should be given to green signal for the priority-based vehicle or change the phase state. The system also monitors the density of car flows and makes real-time decisions accordingly. In order to verify the proposed design algorithm, the simulations of sumo, ns2, and GLD are adopted to fit our model and further results show the performance of the proposed FNN in handling traffic congestion and priority-based control. The promising results present the efficiency of the proposed multi-module architecture and scope for future development in traffic control.

口試委員會審定書 #
中文摘要 i
ABSTRACT ii
Acknowledgement iii
CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES viii
Chapter 1 Introduction 1
1.1 Motivation 2
1.2 Objectives 6
1.3 Thesis Overview 7
Chapter 2 Related Works 8
2.1 Intelligent Transportation Systems (ITS) 8
2.1.1 Genetic algorithms 8
2.1.2 Reinforcement Learning 9
2.1.3 Q-Learning 10
2.1.4 Cellular automata 10
2.1.5 Traffic Signal Preemption to Assist Emergency Vehicles 10
2.2 Fuzzy Logic Control 12
2.3 Neural Network 16
2.4 Recent Traffic and Network Simulator 20
2.4.1 SUMO (Simulation of Urban MObility) 20
2.4.2 MOVE (MObility model generator for Vehicular networks) 20
2.4.3 Traffic Control Interface (TraCI) 22
2.4.4 Green Light District 22
2.4.5 VISSIM 23
2.4.6 NS-2 24
Chapter 3 System Structure 25
3.1 Overview 25
3.2 Parameters Definitions 30
3.3 Traffic Monitor Module 32
3.4 Message Module 35
3.5 Phase Controller Module for Decision Making 39
Chapter 4 Simulation and Result 46
4.1 Simulation Setup 46
4.2 Parameters 48
4.2.1 Evaluation Factor 49
4.2.2 Traditional traffic control algorithms our work compared 50
4.2.3 Driving Policies 51
4.3 Performance Evaluation 52
4.3.1 The observation of ‘Average Junction Waiting Time’ 52
4.3.2 The observation of ‘Average Trip Waiting Time’ 56
4.3.3 The observation of ‘Total Arrived Road users’ 59
4.3.4 The observation of ‘Average Trip Waiting Time’ 61
Chapter 5 Conclusion and Future Work 63
REFERENCE 64


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