博碩士論文 965402004 詳細資訊




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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)
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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
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指導教授 周立德(Li-der Chou) 審核日期 2014-7-25
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