基於微觀與巨觀方法之預測車流旅行時間研究

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姓名

郭凱威(Kai-Wei Kuo) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

基於微觀與巨觀方法之預測車流旅行時間研究
(A Hybrid Travel-Time Prediction Approach Based on Macroscopic and Microscopic Methodologies)

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摘要(中)

近年來旅行時間預測成為智慧化運輸系統(Intelligent Transportation System，ITS)重要的議題，根據國際汽車組織(Organisation Internationale des Constructeurs d’Automobiles，OICA)的統計，全球的車輛不斷的增加，至西元2012年為止全球車輛已達到八千萬輛。而根據中華民國交通部(Ministry Of Transportation and Communications R.O.C.)統計指出，至西元2012年為止，台灣的國道使用率每年有五萬七千萬輛台小客車使用國道。增加的車輛數以及增加的道路使用率會間接地造成車輛壅塞，而在車輛壅塞的情況下駕駛者若因為搶快或者對於周遭環境不熟就很有可能造成車禍事故的發生。為了避免事故的發生以及保障用路人生命安全，精準的預測車流旅行時間可以讓用路者明確的瞭解道路的環境，進而避免自己陷入車輛壅塞的環境之中。傳統的旅行時間預測主要有兩種方法，第一種利用車內裝置定期的將車輛現在的資訊傳回中控中心，這種方法的缺點在於車載網路瞬息萬變，過度依賴全球定位系統(Global Position System，GPS)除了有可能面臨到通訊中斷而使得資料丟失，也有可能會因為GPS延遲使得中控中心取得不正確的資料；第二種是使用路側設施所獲取的車流資料，但是這種方法很容易因設施毀損以及通訊中斷導致中控中心無法取得正確的資料。為了精準的預測車流旅行時間，本論文提出了混合式之車流旅行時間預測方法HPAM (Hybrid Travel-Time Prediction Approach Based on Macroscopic and Microscopic Methodologies)，利用卡爾曼濾波考慮環境噪音的影響並且能夠快速的修正中控中心遺失的資料，並且利用支援向量機的方法考慮了車流資訊以及空氣汙染的影響，進而精準的預測車流旅行時間。利用本論所提出的HPAM機制，在高速公路環境下可以減少9.86%至54.40%的預測誤差，而在一般道路環境下可以減少9.75%至72.80%的預測誤差，因此本論文所提出的HPAM機制能夠有效的減少車流旅行時間預測誤差。

摘要(英)

Recently, travel time prediction approaches have become an important issue in intelligent transportation systems. According to the Organisation Internationale des Constructeurs d’Automogiles , the amount of global vehicles is over than eight hundred millions in 2012. According to the Ministry Of Transportation and Communications R.O.C., the usage rate of Taiwan freeway is more than 5.7 billion vehicles in 2012. Increment of vehicles and freeway usage rate indirectly conducts traffic jams, and traffic jams might conducts accidents. In order to avoid the car accident and keep people safe, accurately predict travel time let users to know the road situation clearly. There are two methods of traditional travel time prediction. First, vehicles use on board unit or global position system to deliver their information to control center. This method has a critical disappoint like control center will lose data due to communication lose or transmit latency. Second, using vehicle detectors to retrieve vehicle information and then transmit to control center. This method also faced same problem like first method. In order to predict travel time more accurately, this thesis propose a Hybrid Travel-Time Prediction Approach Based on Macroscopic and Microscopic Methodologies (HPAM) , using Kalman Filter to recover the loss data quickly and then using Support Vector Machine to predict travel time. In this thesis, HPAM can reduce 9.86% to 54.40% prediction error in freeway situation, and reduce 9.75% to 72.80% prediction error in urban situation. The mechanism HPAM can accurately predict travel time.

關鍵字(中)

★ 旅行時間預測
★ 卡爾曼濾波
★ 支援向量機
★ 車載網路

關鍵字(英)

★ travel time prediction
★ Kalman Filter
★ Support Vector Machine
★ VANET

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 x
第 1 章緒論 1
1.1 概要 1
1.2 研究動機與目標 6
1.3 論文架構 7
第 2 章背景知識與相關研究 8
2.1 卡爾曼濾波器 8
2.2 支援向量機 15
2.3 各項機器學習之車流預測模型與時間序列 25
2.4 旅行時間、空氣汙染資料與車載網路 31
2.5 相關文獻比較 36
第 3 章系統架構與設計 39
3.1 研究模型與假設 39
3.2 系統架構示意圖與模組介紹 40
3.3 系統流程說明 46
3.3.1 資料擷取與資料處理子系統 47
3.3.2 Microscopic Two-Dimensional Kalman Filter 模組與子系統 54
3.3.3 Macroscopic Support Vector Machine 模組與子系統 61
第 4 章實驗討論 71
4.1 實驗環境與評量指標 71
4.2 實驗一：基於高速公路環境之車流模型分析 72
4.3 實驗二：使用KF於高速公路環境固定與動態時序預測比較 76
4.4 實驗三：使用KF基於高速公路環境UKF與TDKF之比較 84
4.5 實驗四：基於一般道路環境之車流模型分析 88
4.6 實驗五：使用KF基於一般道路環境UKF與TDKF之比較 91
4.7 實驗六：旅行時間與空氣汙染資料之相關係數討論 95
4.8 實驗七：空氣汙染資料在高與低相關係數對於預測影響之討論 101
4.9 實驗八：HPAM基於高速公路環境中預測與Hybrid方法比較 104
4.10 實驗九：使用KF以平日之車流資訊預測假日車流資訊之比較 107
4.11 實驗十：使用KF以平日之車流資訊預測假日車流資訊之比較(一個月歷史資料) 109
4.12 實驗十一：以三週歷史資料預測未來一週之數據 111
第 5 章結論與未來工作 114
5.1 結論 114
5.2 研究限制 114
5.3 未來發展工作 115
參考文獻 116

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指導教授

周立德(Li-Der Chou)

審核日期

2013-8-28

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