車載網路環境中基於自律車流預測之潔能導航系統

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：11

、訪客IP：18.221.53.209

姓名

楊俊彥(Jyun-yan Yang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

車載網路環境中基於自律車流預測之潔能導航系統
(A Green Navigation System Based on Autonomic Traffic Predication in VANETs)

相關論文

★ 無線行動隨意網路上穩定品質服務路由機制之研究	★ 應用多重移動式代理人之網路管理系統
★ 應用移動式代理人之網路協同防衛系統	★ 鏈路狀態資訊不確定下QoS路由之研究
★ 以訊務觀察法改善光突發交換技術之路徑建立效能	★ 感測網路與競局理論應用於舒適性空調之研究
★ 以搜尋樹為基礎之無線感測網路繞徑演算法	★ 基於無線感測網路之行動裝置輕型定位系統
★ 多媒體導覽玩具車	★ 以Smart Floor為基礎之導覽玩具車
★ 行動社群網路服務管理系統－應用於發展遲緩兒家庭	★ 具位置感知之穿戴式行動廣告系統
★ 調適性車載廣播	★ 車載網路上具預警能力之車輛碰撞避免機制
★ 應用於無線車載網路上之合作式交通資訊傳播機制以改善車輛擁塞	★ 智慧都市中應用車載網路以改善壅塞之調適性虛擬交通號誌

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

車輛壅塞降低運輸效率和提升運輸成本，此外也汙染空氣造成全球暖化。電動車是發展節能減碳的未來趨勢，此外，為了解決車輛壅塞，電動車搭載車輛導航系統是為經濟實惠的方法。導航系統整合定位系統和電子地圖導引車輛到目的地，近年來，更整合交通資訊以避開壅塞路段。然而，導航系統必須依賴交通資訊的正確性和可預測性，否則卻可能駛入即將發生壅塞的路段，甚至導致電動車電能耗竭而無法行駛。傳統的導航系統規劃路徑時並無考慮未來的交通資訊與電動車電能，因此本篇論文提出一基於車載網路的自律車況預測之潔能導航系統。本論文架構中，路旁裝置與車輛皆扮演感測裝置的角色，並自律地交換資訊和彙整數據用於預測車況，再由導航系統根據車況預測和耗能評估用於規劃電動車建議行駛路徑以提升運輸效率和避免電能耗竭。本論文研究天氣因素，包括溫度、濕度和雨量對於車速預測的影響，並藉由上游路段車速的改變提升下游路段車速預測的精準度。其次，本論文提出的潔能導航系統整合車速預測和電能分析用於路徑規劃。燃料車有足夠的油量能遠程行駛，而電動車受限電瓶容量只能短程行駛，因此本論文討論燃料引擎車與電動車分別使用本論文提出之潔能導航系統的優異。本實驗選用台北市民大道作為車速預測研究的路段，研究結果顯示與混和方法比較，導入天氣和上下游路段關聯性預測方法的精準度提升57.4%。此外，導航系統模擬情境考慮兩車種，分別是燃料車和電動車。導航系統模擬研究結果顯示，與分散式導航方法比較，燃料引擎車使用潔能導航系統的平均車速提升15.49%，電動車使用潔能導航系統的行駛里程數提升9.52%。總而言之，在高油價時代，本論文提出的潔能導航系統減低運輸成本，包含電能與燃料耗損。

摘要(英)

Traffic jams reduce transportation efficiency and increase transportation cost. Traffic jams also cause air pollution and rise global warming. Electric vehicle is the future trend in power saving and CO2 reducing; moreover, electric vehicles embedded with navigation system is an economical solution for reducing traffic jams. Navigation system integrates the global positioning system and electric map to guide vehicles to reach right positions. Currently, navigation systems incorporate traffic information to avoid congested roads. Navigation systems benefit from the predicable traffic and its accuracy. In general, an electric vehicle will drive into a predictability congested road or has battery depletion. Conventional navigation systems are unable to respond to the sudden conditions, because they did not take predicted traffic and battery power into account. Therefore, this dissertation proposes a green navigation system based on autonomic traffic predication in vehicular ad-hoc networks. In this architecture, road-side units and vehicles play a role of sensor or monitor, and they autonomically exchange date and aggregate information for traffic prediction. Navigation systems plan recommendation paths according to the predicted traffic and the estimated state-of-charging to improve the traffic efficiency and to avoid battery depletion. First of all, this dissertation studies the influence of weather factors including temperature, humidity and rainfall on traffic prediction, and improves the accuracy of prediction according to the speed of upstream road segments. Then, the proposed green navigation system is incorporating the predicted traffic information and the estimated state-of-charging. In real world, the internal combustion engine vehicles have long continues driving mileage because of full-oil can reach destination, but the electric vehicles have a restriction of short continues driving mileage because of battery capacity need go to a charging station before battery depletion. Therefore, this dissertation discusses when the internal combustion engine vehicles and the electric vehicles adopt the proposed green navigation system, individually. Real traffic measurements and weather data are used for the evaluation of the proposed prediction scheme. Civic Boulevard in Taipei City is selected as the prediction target. The prediction results show that the proposed traffic prediction improves accuracy by 57.4% when compared with a hybrid approach. The simulation of green navigation system has two types of vehicles: internal combustion engine vehicles and electric vehicles. The navigation results show that the proposed green navigation system improves average speed by 15.49% for internal combustion engine vehicles when compared with the distributed approach. The navigation results also show that the proposed green navigation system improves mileage by 9.52% for electric vehicles when compared with the distributed approach. However, in the peak petroleum price, this dissertation proposes a green navigation system to reduce the transportation cost that includes electric power or oil consumptions.

關鍵字(中)

★ 車速預測
★ 導航系統
★ 電動車
★ 車載網路
★ 自律
★ 潔能

關鍵字(英)

★ traffic prediction
★ navigation system
★ electric vehicle
★ vehicular ad-hoc network
★ autonomic
★ green power

論文目次

Abstract i
摘要 iii
致謝 iv
Contents v
List of Figures vii
List of Tables x
Abbreviation xi
Explanation of Notations xiii
Chapter 1. Introduction 1
1.1 Overview 1
1.2 Motivations and Goals 6
1.3 Dissertation Organization 7
Chapter 2. Related Works 8
2.1 Traffic Prediction 8
2.2 Navigation System 14
Chapter 3. Green Navigation System 20
3.1 Assumptions 20
3.2 Autonomic Architecture of Green Navigation System 20
Chapter 4. Monitor Function: Data Collection 26
4.1 Report Messages 26
4.2 Cluster-based Traffic Data Aggregation via VANETs 28
Chapter 5. Analysis Function: Traffic Prediction 33
5.1 Average Speed Forecast and Adjustment 33
5.2 Routes-Based Traffic Prediction 36
5.3 Energy Consumption and Charging Time Analysis 39
Chapter 6. Plan Function: Search Routes 43
6.1 Time-dependent Dijkstra’s algorithm 43
6.2 Time-dependent A-start algorithm 45
Chapter 7. Simulation Results 48
7.1 Traffic Data Aggregation Time via VANETs 48
7.2 Average Speed Prediction for Real-World Data 54
7.3 Green Navigation System for Internal Combustion Engine Vehicles 61
A. All vehicles adopted the same method in Manhattan 62
B. A Portion of the vehicles complies with the proposed navigation system in Manhattan 73
7.4 Green Navigation System for Electric Vehicles 78
A. All vehicles adopted the same method in Manhattan 81
B. All vehicles adopted the same method in Taipei City 88
Chapter 8. Conclusions and Future Work 94
References 98
Publication List 105

參考文獻

[1] M. Abdul-Hak, N. Al-Holou, U. Mohammad, M. Alamir Tamer, and M. Arafat, "ITS based methodology to reduce energy consumption and emissions," in IEEE Transportation Electrification Conference and Expo, 2012, pp. 1-7.
[2] Y. Hattori, T. Shimoda, and M. Ito, "Development and evaluation of ITS information communication system for electric vehicle," in IEEE Vehicular Technology Conference, 2012, pp. 1-6.
[3] M. Neaimeh, C. Higgins, G. Hill, Y. Hubner, and P. Blythe, "Investigating the effects of topography and traffic conditions on the driving efficiency of electric vehicles to better inform smart navigation," in IET and ITS Conference on Road Transport Information and Control, 2012, pp. 1-6.
[4] (2013). Switch-EV Trial. Available: http://www.switchev.co.uk/
[5] F. Knorr, D. Baselt, M. Schreckenberg, and M. Mauve, "Reducing traffic jams via VANETs," IEEE Transactions on Vehicular Technology, vol. 61, pp. 3490-3498, 2012.
[6] J. A. Fernandez, K. Borries, C. Lin, B. V. K. V. Kumar, D. D. Stancil, and B. Fan, "Performance of the 802.11p physical layer in vehicle-to-vehicle environments," IEEE Transactions on Vehicular Technology, vol. 61, pp. 3-14, 2012.
[7] P. Lytrivis, G. Thomaidis, M. Tsogas, and A. Amditis, "An advanced cooperative path prediction algorithm for safety applications in vehicular networks," IEEE Transactions on Intelligent Transportation Systems, vol. 12, pp. 669-679, 2011.
[8] H. Liang and W. Zhuang, "Cooperative data dissemination via roadside WLANs," IEEE Communications Magazine, vol. 50, pp. 68-74, 2012.
[9] A. Simroth and H. Zahle, "Travel time prediction using floating car data applied to logistics planning," IEEE Transactions on Intelligent Transportation Systems, vol. 12, pp. 243-253, 2011.
[10] M. Ndoye, V. F. Totten, J. V. Krogmeier, and D. M. Bullock, "Sensing and signal processing for vehicle reidentification and travel time estimation," IEEE Transactions on Intelligent Transportation Systems, vol. 12, pp. 119-131, 2011.
[11] A. Simroth, Za, x, and H. hle, "Travel time prediction using floating car data applied to logistics planning," IEEE Transactions on Intelligent Transportation Systems, vol. 12, pp. 243-253, 2011.
[12] W. Zheng, D. Lee, and Q. Shi, "Short-term freeway traffic flow prediction: bayesian combined neural network approach," Journal of Transportation Engineering, vol. 132, pp. 114-121, 2006.
[13] L. Xiaoying, L. Yongzhi, and L. JianXin, "Research on Traffic Flow Base on Neural Network," in Measuring Technology and Mechatronics Automation, 2009. ICMTMA ′09. International Conference on, 2009, pp. 302-304.
[14] C.-C. Huang-Fu and Y.-B. Lin, "Deriving vehicle speeds from standard statistics of mobile telecom switches," IEEE Transactions on Vehicular Technology, vol. 61, pp. 3337-3341, 2012.
[15] R. Lu, X. Lin, H. Zhu, and X. Shen, "An intelligent secure and privacy-preserving parking scheme through vehicular communications," IEEE Transactions on Vehicular Technology, vol. 59, pp. 2772-2785, 2010.
[16] (2014). Far Eastern Electronic Toll Collection Available: http://www.fetc.net.tw/
[17] R. K. Guha and C. Wai, "A distributed traffic navigation system using vehicular communication," in IEEE Vehicular Networking Conference, 2009, pp. 1-8.
[18] J. W. Ding, C. F. Wang, F. H. Meng, and T. Y. Wu, "Real-time vehicle route guidance using vehicle-to-vehicle communication," IET Communications, vol. 4, pp. 870-883, 2010.
[19] U. F. Siddiqi, Y. Shiraishi, and S. M. Sait, "Multi constrained route optimization for electric vehicles using SimE," in International Conference of Soft Computing and Pattern Recognition, 2011, pp. 376-383.
[20] RITA bureau of transportation statistics [Online]. Available: http://www.rita.dot.gov/
[21] (2014). ETSI. Available: http://www.etsi.org/technologies-clusters/technologies/intelligent-transport
[22] G. Marfia and M. Roccetti, "Vehicular congestion detection and short-term forecasting: a new model with results," IEEE Transactions on Vehicular Technology, vol. 60, pp. 2936-2948, 2011.
[23] J. Xue and Z. Shi, "Short-time traffic flow prediction based on chaos time series theory," Journal of Transportation Systems Engineering and Information Technology, vol. 8, pp. 68-72, 2008.
[24] J. Hu, I. Kaparias, and M. G. H. Bell, "Spatial econometrics models for congestion prediction with in-vehicle route guidance," IET Intelligent Transport Systems, vol. 3, pp. 159-167, 2009.
[25] B. Ghosh, B. Basu, and M. O′Mahony, "Multivariate short-term traffic flow forecasting using time-series analysis," IEEE Transactions on Intelligent Transportation Systems, vol. 10, pp. 246-254, 2009.
[26] J. Huifeng, X. Aigong, S. Xin, and L. Lanyong, "The applied research of Kalman in the dynamic travel time prediction," in 18th International Conference on Geoinformatics 2010, pp. 1-5.
[27] M. Yang, Y. Liu, and Z. You, "The reliability of travel time forecasting," IEEE Transactions on Intelligent Transportation Systems, vol. 11, pp. 162-171, 2010.
[28] T. Zhu, X. Kong, W. Lv, Y. Zhang, and B. Du, "Travel time prediction for float car system based on time series," in International Conference on Advanced Communication Technology, 2010, pp. 1503-1508.
[29] C.-T. Lin and L.-D. Chou, "A novel economy reflecting short-term load forecasting approach," Energy Conversion and Management, vol. 65, pp. 331-342, 2013.
[30] Y.-L. Chen, C. H. Huang, Y.-W. Kuo, and S.-S. Wang, "Artificial neural network for predictions of vehicle drivable range and period," in IEEE 2012 International Conference on Vehicular Electronics and Safety, 2012, pp. 329-333.
[31] P. Jungme, L. Dai, Y. L. Murphey, J. Kristinsson, R. McGee, K. Ming, et al., "Real time vehicle speed prediction using a Neural Network Traffic Model," in 2011 International Joint Conference on Neural Networks, 2011, pp. 2991-2996.
[32] C.-H. Wei and Y. Lee, "Development of freeway travel time forecasting models by integrating different sources of traffic data," IEEE Transactions on Vehicular Technology, vol. 56, pp. 3682-3694, 2007.
[33] W.-M. Hung, W.-C. Hong, and T.-B. Chen, "Application of hybrid genetic algorithm and simulated annealing in a SVR traffic flow forecasting model," in IEEE 2009 Congress on Evolutionary Computation, 2009, pp. 728-735.
[34] S. Innamaa, "Self-adapting traffic flow status forecasts using clustering," IET Intelligent Transport Systems, vol. 3, pp. 67-76, 2009.
[35] T. Wu, K. Xie, G. Song, and C. Hu, "A multiple SVR approach with time lags for traffic flow prediction," in IEEE 11th Conference on Intelligent Transportation Systems, 2008, pp. 228-233.
[36] W. Zheng, D.-H. Lee, and Q. Shi, "Short-term freeway traffic flow prediction: Bayesian combined neural network approach," Journal of Transportation Engineering, vol. 132, pp. 114-121, 02/01 2006.
[37] C. Liu, X. Liu, H. Huang, and L. Zhao, "Short-term traffic flow prediction methods and the correlation analysis of vehicle speed and traffic flow," in 2008 International Conference on Computational Intelligence and Security, 2008, pp. 415-418.
[38] X. Zang, "The short-term traffic volume forecasting for urban interchange based on RBF Artificial Neural Networks," in 2009 International Conference on Mechatronics and Automation, 2009, pp. 2607-2611.
[39] M.-C. Tan, S. C. Wong, J.-M. Xu, Z.-R. Guan, and P. Zhang, "An aggregation approach to short-term traffic flow prediction," IEEE Transactions on Intelligent Transportation Systems, vol. 10, pp. 60-69, 2009.
[40] C.-N. Manoel, Y.-S. Jeong, M.-K. Jeong, and L. D. Han, "Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions," Expert Systems with Applications, vol. 36, pp. 6164-6173, 2009.
[41] Y. Qing, W. Y. Szeto, and S. C. Wong, "Short-Term traffic speed forecasting based on data recorded at irregular intervals," IEEE Transactions on Intelligent Transportation Systems, vol. 13, pp. 1727-1737, 2012.
[42] H. Gan, "Graphical route information panel for the urban freeway network in Shanghai, China," IET Intelligent Transport Systems, vol. 4, pp. 212-220, 2010.
[43] A. Hrazdira, A. Cela, R. Hamouche, A. Reama, S. I. Niculescu, B. Rezende, et al., "Optimal real-time navigation system: application to a hybrid electrical vehicle," in IEEE Conference on Intelligent Transportation Systems, 2012, pp. 409-414.
[44] F. Malandrino, C. Casetti, and C. Chiasserini, "A game-theoretic approach to EV driver assistance through ITS," in IEEE 23rd International Symposium on Personal Indoor and Mobile Radio Communications, 2012, pp. 2541-2546.
[45] M. Richter, S. Zinser, and H. Kabza, "Comparison of eco and time efficient routing of ICEVs, BEVs and PHEVs in inner city traffic," in IEEE Vehicle Power and Propulsion Conference, 2012, pp. 1165-1169.
[46] C. Masjosthusmann, U. Kohler, N. Decius, and U. Buker, "A vehicle energy management system for a battery electric vehicle," in IEEE Vehicle Power and Propulsion Conference, 2012, pp. 339-344.
[47] M. D. Galus, R. A. Waraich, F. Noembrini, K. Steurs, G. Georges, K. Boulouchos, et al., "Integrating power systems, transport systems and vehicle technology for electric mobility Impact assessment and efficient control," IEEE Transactions on Smart Grid, vol. 3, pp. 934-949, 2012.
[48] S. Hui, W. Jiafu, L. Di, and Z. Caifeng, "Energy management framework designed for autonomous electric vehicle with sensor networks navigation," in IEEE International Conference on Computer and Information Technology, 2012, pp. 914-920.
[49] M. Silva, F. Moita, U. Nunes, L. Garrote, H. Faria, and J. Ruivo, "ISRobotCar: The autonomous electric vehicle project," in IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012, pp. 4233-4234.
[50] Y. Chen, G. H. B. Michael, and B. Klaus, "Reliable pretrip multipath planning and dynamic adaptation for a centralized road navigation system," IEEE Transactions on Intelligent Transportation Systems, vol. 8, pp. 14-20, 2007.
[51] H. Schaminée and H. Aerts, "Short and winding road: software in car navigation systems," IEEE Software, vol. 28, pp. 19-21, 2011.
[52] Y. Wang, J. Hu, and Q. Wang, "A Simulation study of dynamic route guidance based on inter-vehicle communication," in International Conference on Communications and Mobile Computing, 2010, pp. 397-403.
[53] J. Hershberger, M. Maxel, and S. Suri, "Finding the K shortest simple paths: A new algorithm and its implementation," ACM Transactions on Algorithms, vol. 3, p. 45, 2007.
[54] Y. Gao, "An improved shortest route algorithm in vehicle navigation system," in International Conference on Advanced Computer Theory and Engineering 2010, pp. V2-363-V2-366.
[55] Q. Li, Z. Zeng, and B. Yang, "Hierarchical model of road network for route planning in vehicle navigation systems," IEEE Intelligent Transportation Systems Magazine, vol. 1, pp. 20-24, 2009.
[56] I. Kaparias and M. G. H. Bell, "Testing a reliable in-vehicle navigation algorithm in the field," IET Intelligent Transport Systems, vol. 3, pp. 314-324, 2009.
[57] I. Kaparias, M. G. H. Bell, and H. Belzner, "A new measure of travel time reliability for in-vehicle navigation systems," Journal of Intelligent Transportation Systems, vol. 12, pp. 202-211, 2008/11/12 2008.
[58] C. Sommer, O. K. Tonguz, and F. Dressler, "Traffic information systems: efficient message dissemination via adaptive beaconing," IEEE Communications Magazine, vol. 49, pp. 173-179, 2011.
[59] S. M. Alhalabi, S. M. Al-Qatawneh, and V. W. Samawi, "Developing a route navigation system using genetic algorithm," in International Conference on Information and Communication Technologies: From Theory to Applications, 2008, pp. 1-6.
[60] CHAdeMO. Available: http://www.chademo.com/
[61] J. Blum and E. Azim, "The threat of intelligent collisions," IT Professional, vol. 6, pp. 24-29, 2004.
[62] Q. Ding, X. Li, M. Jiang, and X. Zhou, "Reputation management in vehicular ad hoc networks," in International Conference on Multimedia Technology, 2010, pp. 1-5.
[63] T. Song, W. Xia, T. Song, and L. Shen, "A cluster-based directional routing protocol in VANET," in IEEE 12th International Conference on Communication Technology, 2010, pp. 1172-1175.
[64] A. Benslimane, T. Taleb, and R. Sivaraj, "Dynamic clustering-based adaptive mobile gateway management in integrated VANET-3G heterogeneous wireless networks," IEEE Journal on Selected Areas in Communications vol. 29, pp. 559-570, 2011.
[65] Y. Zhang and G. Cao, "V-PADA: vehicle-platoon-aware data access in VANETs," IEEE Transactions on Vehicular Technology, vol. 60, pp. 2326-2339, 2011.
[66] H. Lv, X. Ye, L. An, and Y. Wang, "Distributed beacon frequency control algorithm for VANETs (DBFC)," in Intelligent System Design and Engineering Application, 2012, pp. 243-246.
[67] B. Li, Y. K. Tan, and A. G. Dempster, "Using two global positioning system satellites to improve wireless fidelity positioning accuracy in urban canyons," IET Communications, vol. 5, pp. 163-171, 2011.
[68] N. M. Drawil and O. Basir, "Intervehicle-Communication-Assisted Localization," IEEE Transactions on Intelligent Transportation Systems, vol. 11, pp. 678-691, 2010.
[69] L.-D. Chou, J.-Y. Yang, Y.-C. Hsieh, D.-C. Chang, and C.-F. Tung, "Intersection-based routing protocol for VANETs," Wireless Personal Communications, vol. 60, pp. 105-124, 2011/09/01 2011.
[70] N. Wisitpongphan, B. Fan, P. Mudalige, and O. K. Tonguz, "On the routing problem in disconnected vehicular ad-hoc networks," in IEEE 26th International Conference on Computer Communications, 2007, pp. 2291-2295.
[71] C. Lin and S. Panichpapiboon, "Effects of intervehicle spacing distributions on connectivity of VANET: A case study from measured highway traffic," IEEE Communications Magazine, vol. 50, pp. 90-97, 2012.
[72] S. Panichpapiboon and W. Pattara-atikom, "Connectivity requirements for self-organizing traffic information systems," IEEE Transactions on Vehicular Technology, vol. 57, pp. 3333-3340, 2008.
[73] DTREG. [Online]. Available: http://www.dtreg.com/mlfn.htm
[74] T. Umedu, K. Isu, T. Higashino, and C. K. Toh, "An intervehicular-communication protocol for distributed detection of dangerous vehicles," IEEE Transactions on Vehicular Technology, vol. 59, pp. 627-637, 2010.
[75] U. S. D. o. T. F. H. Administration. Traffic flow theory – a state of the art report [Online]. Available: http://www.tfhrc.gov/its/tft/tft.htm
[76] M. S. van den Broek, J. S. H. van Leeuwaarden, I. J. B. F. Adan, and O. J. Boxma, "Bounds and approximations for the fixed-cycle traffic-light queue," Transportation Science, vol. 40, pp. 484-496, 2006.
[77] W. Lei, E. G. Collins, Jr., and L. Hui, "Optimal design and real-time control for energy management in electric vehicles," IEEE Transactions on Vehicular Technology, vol. 60, pp. 1419-1429, 2011.
[78] M. Camara, B. Dakyo, and H. Gualous, "Polynomial control method of DC/DC converters for DC-bus voltage and currents management battery and supercapacitors," IEEE Transactions on Power Electronics, vol. 27, pp. 1455-1467, 2012.
[79] E. Kahrimanovic and A. Khaligh, "Feasibility of installing additional battery pack on a conventional city transit bus to power the auxiliary loads and reduce the fuel consumption," in IEEE Vehicle Power and Propulsion Conference, 2011, pp. 1-5.
[80] K. Sung-Il, L. Geun-Ho, H. Jung-Pyo, and T.-U. Jung, "Design process of interior PM synchronous motor for 42-v electric air-conditioner system in hybrid electric vehicle," IEEE Transactions on Magnetics, vol. 44, pp. 1590-1593, 2008.
[81] H. Khayyam, A. Z. Kouzani, and E. J. Hu, "Reducing energy consumption of vehicle air conditioning system by an energy management system," in IEEE Intelligent Vehicles Symposium, 2009, pp. 752-757.
[82] V. Agarwal, K. Uthaichana, R. A. DeCarlo, and L. H. Tsoukalas, "Development and validation of a battery model useful for discharging and charging power control and lifetime estimation," IEEE Transactions on Energy Conversion, vol. 25, pp. 821-835, 2010.
[83] NS2. [Online]. Available: http://isi.edu/nsnam/ns/
[84] Y. Jyun-Yan, C. Li-Der, T. Chi-Feng, H. Shu-Min, and W. Tong-Wen, "Average-speed forecast and adjustment via VANETs," IEEE Transactions on Vehicular Technology, vol. 62, pp. 4318-4327, 2013.
[85] (2014). Taipei ATIS. Available: http://its.taipei.gov.tw/atis_index/
[86] (2014). Central Weather Bureau. Available: http://www.cwb.gov.tw/
[87] J.-Y. Yang, L.-D. Chou, Y.-C. Li, Y.-H. Lin, S.-M. Huang, G. Tseng, et al., "Prediction of short-term average vehicular velocity considering weather factors in urban VANET environments," in 2010 International Conference on Machine Learning and Cybernetics, 2010, pp. 3039-3043.
[88] Estinet [Online]. Available: http://www.estinet.com/
[89] G. Zhang and Y. Wang, "Optimizing minimum and maximum green time settings for traffic actuated control at isolated intersections," IEEE Transactions on Intelligent Transportation Systems, vol. 12, pp. 164-173, 2011.
[90] M. Neaimeh, G. A. Hill, Hu, x, Y. bner, and P. T. Blythe, "Routing systems to extend the driving range of electric vehicles," IET Intelligent Transport Systems, vol. 7, pp. 327-336, 2013.
[91] J. Timpner and L. Wolf, "Design and evaluation of charging station scheduling strategies for electric vehicles," IEEE Transactions on Intelligent Transportation Systems, vol. PP, pp. 1-10, 2013.
[92] (2014). National Center for High-performance Computing. Available: http://www.nchc.org.tw/en/

指導教授

周立德(Li-der Chou)

審核日期

2014-7-25

推文