進階搜尋


   電子論文尚未授權公開,紙本請查館藏目錄
(※如查詢不到或館藏狀況顯示「閉架不公開」,表示該本論文不在書庫,無法取用。)
系統識別號 U0026-2008201214565800
論文名稱(中文) 應用模糊類神經的交通壅塞控制於車載網路
論文名稱(英文) Using Fuzzy Neural Model to Reduce Traffic Congestion in VANET
校院名稱 成功大學
系所名稱(中) 資訊工程學系碩博士班
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 100
學期 2
出版年 101
研究生(中文) 王國川
研究生(英文) Kuo-Chuan Wang
學號 p76994474
學位類別 碩士
語文別 英文
論文頁數 66頁
口試委員 指導教授-鄭憲宗
口試委員-楊竹星
口試委員-陳嘉玫
口試委員-張瑞雄
口試委員-林正敏
中文關鍵字 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 
英文關鍵字 Neural network  fuzzy logic control  Intelligent Transportation System (ITS)  intersection delay prediction  multi-module system  urban traffic signal control, traffic congestion 
學科別分類
中文摘要 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。
英文摘要 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.
論文目次 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

參考文獻 [1] D. Srinivasan, C. Min Chee, and R. L. Cheu, “Neural Networks for Real-Time Traffic Signal Control,” IEEE Transactions on Intelligent Transportation Systems, vol. 7, no. 3, pp. 261-272, 2006.
[2] V. Milanes, J. Perez, E. Onieva et al., “Controller for Urban Intersections Based on Wireless Communications and Fuzzy Logic,” IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 1, pp. 243-248, 2010.
[3] B. P. Gokulan, and D. Srinivasan, “Distributed Geometric Fuzzy Multiagent Urban Traffic Signal Control,” IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 3, pp. 714-727, 2010.
[4] Y. Sazi Murat and E. Gedizlioglu, “A fuzzy logic multi-phased signal control model for isolated junctions,” Transportation Research Part C: Emerging Technologies, vol. 13, no. 1, pp. 19-36, 2005.
[5] J. Niittymaki and E. Turunen, “Traffic signal control on similarity logic reasoning,” Fuzzy Sets System, vol. 133, no. 1, pp. 109–131, Jan. 2003.
[6] P. B. Mirchandani and N. Zou, “Queuing models for analysis of traffic adaptive signal control,” IEEE Intelligent on Transportation Systems, vol. 8, no. 1, pp. 50–59, Mar. 2007.
[7] J. J. Sanchez-Medina, M. J. Galan-Moreno, and E. Rubio-Royo, “Traffic Signal Optimization in “La Almozara” District in Saragossa Under Congestion Conditions, Using Genetic Algorithms, Traffic Microsimulation, and Cluster Computing,” IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 1, pp. 132-141, 2010.
[8] M. Abdoos, N. Mozayani, and A. L. C. Bazzan, "Traffic light control in non-stationary environments based on multi agent Q-learning," in 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2011, pp. 1580-1585.
[9] P. G. Balaji, X. German, and D. Srinivasan, “Urban traffic signal control using reinforcement learning agents,” Intelligent Transport Systems, IET, vol. 4, no. 3, pp. 177-188, 2010.
[10] I. Arel, C. Liu, T. Urbanik et al., “Reinforcement learning-based multi-agent system for network traffic signal control,” Intelligent Transport Systems, IET, vol. 4, no. 2, pp. 128-135, 2010.
[11] L. A. Prashanth, and S. Bhatnagar, “Reinforcement Learning With Function Approximation for Traffic Signal Control,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 2, pp. 412-421, 2011.
[12] C. Min Chee, D. Srinivasan, and R. L. Cheu, “Cooperative, hybrid agent architecture for real-time traffic signal control,” IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 33, no. 5, pp. 597-607, 2003.
[13] S. Guojiang, and K. Xiangjie, “Study on Road Network Traffic Coordination Control Technique With Bus Priority,” IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 39, no. 3, pp. 343-351, 2009.
[14] E. Azimirad, N. Pariz, and M. B. N. Sistani, “A Novel Fuzzy Model and Control of Single Intersection at Urban Traffic Network,” IEEE Systems Journal, vol. 4, no. 1, pp. 107-111, 2010.
[15] Y. Lo-Yao, C. Yen-Cheng, and H. Jiun-Long, “ABACS: An Attribute-Based Access Control System for Emergency Services over Vehicular Ad Hoc Networks,” IEEE Journal on Selected Areas in Communications, vol. 29, no. 3, pp. 630-643, 2011.
[16] M. Patel, and N. Ranganathan, “IDUTC: an intelligent decision-making system for urban traffic-control applications,” IEEE Transactions on Vehicular Technology, vol. 50, no. 3, pp. 816-829, 2001.
[17] J. J. Henry, J. L. Farges, and J. L. Gallego, “Neuro-fuzzy techniques for traffic control,” Control Engineering Practice, vol. 6, no. 6, pp. 755-761, 1998.
[18] Traffic signal preemption, http://en.wikipedia.org/wiki/Traffic_signal_preemption
[19] K. D. and R. C., SUMO (Simulation of Urban MObility), German Aerospace Centre, 2007. http://sumo.sourceforge.net/
[20] MOVE (MObility model generator for VEhicular networks): Rapid Generation of Realistic Simulation for VANET., 2007.
http://lens1.csie.ncku.edu.tw/MOVE/index.htm
[21] The Network Simulator – NS-2, http://www.isi.edu/nsnam/ns/
[22] P. simulation VISSIM, http://www.english.ptv.de/
[23] L. Jee-Hyong, and L.-K. Hyung, “Distributed and cooperative fuzzy controllers for traffic intersections group,” IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 29, no. 2, pp. 263-271, 1999.
[24] W. Junhua, W. Anlin, and N. Du, “Study of self-organizing control of traffic signals in an urban network based on cellular automata,” IEEE Transactions on Vehicular Technology, vol. 54, no. 2, pp. 744-748, 2005.
[25] M. Wiering, J. Vreeken, J. van Veenen, and A. Koopman, "Simulation and optimization of traffic in a city," in IEEE Intelligent Vehicles Symposium, 2004, pp. 453-458.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2017-08-30起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw