車載隨意網路中車輛對車輛之間的通訊和機率式廣播，在資訊傳播方面扮演著很重要的角色。與固定式隨意網路相比，車輛之間的高速移動會造成無線通訊間歇性的斷線。然而，某些類型的資訊不需要依靠連續性的無線通訊連線來傳遞。一個重要的事實是許多車輛都需要這些資訊，例如行車安全資訊和休閒資訊。在近幾年為了傳遞緊急資訊給所有車輛，有非常多的資訊傳播演算法被提出。由於休閒資訊的重要性低所以一直被人們嚴重的忽略。即便如此，休閒資訊的傳播是一個很重要的議題在智慧城市，舉例來說人們在高速公路的長途旅行。因此，我們提出了休閒資訊傳播的興趣感知機率式傳播演算法。興趣感知機率式傳播演算法結合了機率式廣播與延遲廣播的技術。經由實驗的證明，興趣感知機率式傳播花費了較低的成本和獲得高的接收率。
智慧城市的第二個議題是減少交通壅塞和幫助車輛具有高優先權的先通過。本研究使用模糊邏輯也提出類神經網路用於公共運輸、一般汽車和機車的模組。利用模糊類神經網絡來計算交通號誌燈的系統，根據路口的交通情況來延長或終止綠色號誌的時間，同時也從相鄰的交叉路口來計算。在公共運輸下該系統決定哪些號誌應是紅色和多少個綠色號誌應該延長時間，基於優先權的車輛。該系統還監控汽車流量的密度，並作出即時的決策。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。在一般時段，可以有效的減少路口的壅塞程度，當不同程度的車輛接近時，能夠動態的調整綠燈時序及號誌週期。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。從結果來看，我們提出的多模組架構在交通控制未來的發展上是有所幫助的。
智慧城市的第三個議題提出一套適用計程車與共乘乘客的推薦機制。我們的第一個目標是分別推薦計程車能迅速載到乘客，而乘客也能容易的找到計程車。第二個目的是提供計程車共乘服務的機制給想要節省開銷的乘客。我們根據計程車在全球衛星定位系統記錄下的歷史軌跡資料，以R-Tree的方式分析出時間相關的服務區域。我們利用非合作式賽局理論，當多輛計程車在同一個區域選擇推薦路徑時，有彼此競爭且獲利互相影響的關係下，找出多輛計程車選擇推薦路線的納什均衡解決方案。從研究結果說明了我們的方法可以有效率地找到計程車跟乘客的需求。此外，運用我們的方法可以讓乘客減少車資，同時計程車也可以藉由共乘機制，延長載客的距離，提高計程車司機的收入。

Vehicle-to-vehicle (V2V) communication and probabilistic broadcast are important means for information dissemination in vehicular ad-hoc networks (VANETs). In contrast to static ad-hoc networks, high-speed mobility makes wireless connection between two vehicles intermittent. Nevertheless, some kinds of information do not rely on continuous connection for transmission. An important fact is that numerous vehicles ‘desire’ to have such information as safety-related data and leisure information. In recent years, an excessive number of protocols have attached themselves to critical information dissemination. People severely neglect leisure information because of its low importance. Even so, the dissemination of leisure information is an important issue in smart city when, for example, people take long trips on highways. Therefore, we propose interest-aware probabilistic dissemination (IAPD) of leisure information in VANET, which combines probabilistic broadcast and timer-based broadcast techniques. The simulation results show that IAPD cost less than restriction flooding and simple schemes in obtaining a high reception rate.
The second issue in smart city is to reduce traffic congestion and help vehicles with high priority pass through. Using fuzzy logic, this study also proposes a model with a neural network for public transport, normal cars, and motorcycles. A fuzzy neural network (FNN) calculates the traffic-light system and extends or terminates the green signal according to the traffic situation at the given junction while also computing from adjacent intersections. In the presence of public transports, the system decides which signal(s) should be red and how much of an extension should be given to green signals for the priority-based vehicle. The system also monitors the density of car flows and makes real-time decisions accordingly. The promising results present the efficiency and the scope of the proposed multi-module architecture for future development in traffic control.
The third issue in smart city presents a recommendation mechanism for taxi-sharing. The first aim of our model is to respectively recommend taxis and passengers for picking up passengers quickly and finding taxis easily. The second purpose is providing taxi-sharing service for passengers who want to save the payment. In our method, we analyze the historical Global Positioning System trajectories generated by taxis and present the service region with time-dependent R-Tree. We formulate the problem of choosing the paths among the taxis in the same region by using non-cooperative game theory, and find out the solution of this game which is known as Nash equilibrium. The results show that our method can find taxis and passengers efficiently. In addition, applying our method can reduce the payment of passengers and increase the taxi revenue by taxi-sharing.

[1]S. Panichpapiboon and W. P. Atikom. A Review of Information Dissemination Protocols for Vehicular Ad Hoc Networks. IEEE Communications Surveys & Tutorials, 2012, pp. 784–798.
[2]M. Fogue, P. Garrido, F. Martinez, J. Cano, C. Calafate, and P. Manzoni. Identifying the Key Factors Affecting Warning Message Dissemination in VANET Real Urban Scenarios. Sensors 2013, pp.5220–5250.
[3]Y. Zhuang, J. Pan, Y. Luo, L. Cai. Time and location-critical emergency message dissemination for vehicular ad-hoc networks. IEEE Journal on Selected Area in Communications, 2011, pp.187–196.
[4]C. Boldrini, M. Conti, F. Delmastro, and A. Passarella. Context- and social-aware middleware for opportunistic networks. Journal of Network and Computer Applications, 2010, pp.520–541.
[5]C. Boldrini, M. Conti, and A. Passarella. Design and performance evaluation of ContentPlace, a social-aware data dissemination system for opportunistic networks. Computer Networks, 2010, pp.589–604.
[6]N. Cenerario, T. Delot, S. Ilarri. A Content-Based Dissemination Protocol for VANETs: Exploiting the Encounter Probability. IEEE Transactions on Intelligent Transportation Systems, 2011, pp.771–782.
[7]C. T. Lin and C. S. G. Lee. Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems (with disk). Prentice Hall, 1996.
[8]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, 2006, pp. 261–272.
[9]S. Lin, B. D. Schutter, Y. Xi, and H. Hellendoorn. Integrated Urban Traffic Control for the Reduction of Travel Delays and Emissions. IEEE Transactions on Intelligent Transportation Systems, 2013, pp.1609–1619.
[10]S. Yan, C. Y. Chen, and S. C. Chang. A Car Pooling Model and Solution Method with Stochastic Vehicle Travel Times. IEEE Transactions on Intelligent Transportation Systems, 2013, pp. 1–15.
[11]G. Dimitrakopoulos, P. Demestichas, and V. Koutra. Intelligent Management Functionality for Improving Transportation Efficiency by Means of the Car Pooling Concept. IEEE Transactions on Intelligent Transportation Systems, 2012, pp. 424–436.
[12]T. Haapasalo, I. Jaluta, S. Sippu, and E. S. Soininen. On the Recovery of R-Trees. IEEE Transactions on Knowledge and Data Engineering, 2013, pp. 145–157.
[13]B. Kao, S. D. Lee, F. K. F. Lee, D. W. Cheung, and W. S. Ho. Clustering Uncertain Data Using Voronoi Diagrams and R-Tree Index. IEEE Transactions on Knowledge and Data Engineering, 2010, pp. 1219–1233.
[14]N. Wisitpongphan, O. Tonguz, J. Parikh, P. Mudalige, F. Bai, and V. Sadekar. Broadcast storm mitigation techniques in vehicular ad hoc networks. IEEE Wireless Communications, 2007, pp.84–94.
[15]B. Bako, I. Rikanovic, F. Kargl, and E. Schoch. Adaptive topology based gossiping in VANETs using position information. ACM international conference on Mobile ad-hoc and sensor networks, 2007, pp.66–78.
[16]K. Ibrahim, M. Weigle, and M. Abuelela. p-IVG: Probabilistic inter-vehicle geocast for dense vehicular networks. IEEE Vehicular Technology Conference, 2009, pp.1–5.
[17]M. Nekovee. Epidemic algorithms for reliable and efficient information dissemination in vehicular ad hoc networks. IET Intelligent Transport Systems, 2009, pp.104–110.
[18]M. Saito, J. Tsukamoto, T. Umedu, and T. Higashinoz. Design and Evaluation of Intervehicle Dissemination Protocol for Propagation of Preceding Traffic Information. IEEE Transactions on Intelligent Transportation Systems, 2007, pp. 379–390.
[19]G. Ciccarese, M. D. Blasi, P. Marra, and C. Palazzo. A timer-based intelligent flooding scheme for VANETs. IEEE International Symposium on Communication Systems Networks and Digital Signal Processing, 2010, pp.377–381.
[20]A. Bachir and A. Benslimane. A multicast protocol in ad hoc networks inter-vehicle geocast. IEEE Vehicular Technology Conference, 2003, pp.2456–2460.
[21]C. Liaskos, A. Xeros, I. P. Georgios, M. Lestas, and A. Pitsillides. Balancing Wireless Data Broadcasting and Information Hovering for Efficient Information Dissemination. IEEE Transactions on Broadcasting, 2012, pp. 66–76.
[22]M. Torrent-Moreno, D. Jiang, and H. Hartenstein. Broadcast reception rates and effects of priority access in 802.11-based vehicular ad-hoc networks. ACM international workshop on Vehicular ad hoc networks, 2004, pp.10–18.
[23]M. Killat, F. Schmidt-Eisenlohr , H. Hartenstein, C. Rössel, P. Vortisch, S. Assenmacher, F. Busch. Enabling efficient and accurate large-scale simulations of VANETs for vehicular traffic management. ACM international workshop on Vehicular ad hoc networks, 2007, pp.29–38.
[24]M. Killat and H. Hartenstein. An empirical model for probability of packet reception in vehicular ad hoc networks. ACM EURASIP Journal on Wireless Communications and Networking – Special issue on wireless access in vehicular environments, 2009, DOI:10.1155/2009/721301.
[25]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, 2010, pp. 132–141.
[26]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, 2010, pp. 128–135.
[27]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, 2011, pp. 412–421.
[28]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.
[29]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, 2005, pp. 744–748.
[30]Y. L. Yao, C. Y. Cheng, and H. J. 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, 2011, pp. 630–643.
[31]Traffic signal preemption, http://en.wikipedia.org/wiki/Traffic_signal_preemption
[32]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, 2010, pp. 243–248.
[33]B. P. Gokulan, and D. Srinivasan. Distributed Geometric Fuzzy Multiagent Urban Traffic Signal Control. IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 3, 2010, pp. 714–727.
[34]P. B. Mirchandani and N. Zou. Queuing models for analysis of traffic adaptive signal control. IEEE Intelligent on Transportation Systems, vol. 8, no. 1, 2007, pp. 50–59.
[35]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, 2010, pp. 107–111.
[36]C. M. 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, 2003, pp. 597–607.
[37]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, 2009, pp. 343–351.
[38]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, 2001, pp. 816–829.
[39]A. Guttman. R-trees a dynamic index structure for spatial searching. Proceedings of the ACM SIGMOD international conference on Management of data, 1984, pp. 47–57.
[40]P. M. d’Orey, R. Fernandes and M. Ferreira. Empirical Evaluation of Dynamic and Distribution Taxi-Sharing System. IEEE International Conference on Intelligent Transportation Systems, 2012, pp. 140–146.
[41]S. Ma, Y. Zheng, and O. Wolfson. T-Share: A Large-Scale Dynamic Taxi Ridesharing Service. IEEE International Conference on Data Engineering, 2013, pp. 410–421.
[42]N. J. Yuan, Y. Zheng, L. Zhang, and X. Xie. T-Finder: A Recommender System for Finding Passengers and Vacant Taxis. IEEE Transactions on Knowledge and Data Engineering, 2013, pp. 2390–2403.
[43]R. B. Myerson. Game Theory: Analysis of Conflict, Harvard University Press, 1991.
[44]L. Zhao, J. Zhang, and H. Zhang. Using Incompletely Cooperative Game Theory in Wireless Mesh Networks. IEEE Network, 2008, pp. 39–44.
[45]Z. S. Ma and A. W. Krings. Dynamic Hybrid Fault Modeling and Extended Evolutionary Game Theory for Reliability, Survivability and Fault Tolerance Analyses. IEEE Transactions on Reliability, 2011, pp. 180–196.
[46]E. Umehara and T. Ohta. Using Game Theory to Investigate Risk Information Disclosure by Government Agencies and Satisfying the Public—The Role of the Guardian Agent. IEEE Transactions on Humans, 2009, pp. 321–330.
[47]X. Liang and Y. Xiao. Game Theory for Network Security. IEEE Communications Surveys & Tutorials, 2013, pp. 472–486.
[48]M. Wiering, J. Vreeken, J. Veenen, and A. Koopman. Simulation and optimization of traffic in a city. IEEE Intelligent Vehicles Symposium, 2004, pp. 453–458.
[49]J. Yuan, Y. Zheng, C. Zhang, W. Xie, X. Xie, G. Sun, and Y. Huang. T-drive: driving directions based on taxi trajectories. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2010, pp. 99–108.
[50]J. Yuan, Y. Zheng, X. Xie, and G. Sun. Driving with knowledge from the physical world. The 17th ACM SIGKDD international conference on Knowledge Discovery and Data mining, 2011, pp. 316–324.