臺灣博碩士論文加值系統

為改善交通問題，提供完整、正確且即時(real-time)路況資訊是一個很重要的議題。固定式偵測器是台灣地區交通資訊的主要蒐集來源，限於成本因素難以全線佈設，沒有設偵測器之路段無法提供路況資訊，而已架設之偵測器一些因素導致無法提供資料，以上因素皆會造成路況資料的缺漏，故本研究之目的為預測高速公路上沒有設置偵測器或有設置偵測器，但無法提供服務之路段行車速率，期望給予用路人完整的資訊。
本研究探討林口交流道至桃園交流道南下路段，選定上、中、下游三個偵測器，應用上下游偵測器已知資料來推估中間偵測器之路段行車速率。本研究應用微觀模擬車流軟體PARAMICS，進行路網模擬預測中間偵測器之路段行車速率，分三個部分進行預測。第一部份應用2007年1月5日7點到9點55分之車流資料尋找最佳參數值，預測中間偵測器之MAPE值為6.4％；第二部份探討最佳參數值是否適用於其他日，2007年1月12日7點到9點55分之車流資料，預測中間偵測器之MAPE值為3.68％。第三部分探討模擬時間長短的不同，是否會影響預測能力，預測中間偵測器MAPE總績效為5.6％。上述驗證皆可證明此模式是可以準確預測沒有設置偵測器路段之行車速率。

In order to improve traffic problems, it seems to be important to provide complete, accurate and real-time traffic flow information. In Taiwan, stationary vehicle detectors are the main sources of traffic information, however, it seems impossible to achieve full installation on highways due to high costs. There would be no source of traffic information on the highways without detectors, and for some reasons some installed detectors fail to provide information. The above factors would lead to incomplete collection of traffic information. Thus, this study aims to predict the running speed of the highways with and without detectors that failed to provide traffic information, in hope of offering complete information for road users.
This study examines the section from Lingkou interchange to Taoyuan interchange, three detectors are chosen, which are located at the upper, middle and lower section.
The information obtained from the lower section is used to estimate the possible running speed of the middle section. The study is divided into three sections, the microscopic traffic simulation software PARAMICS is used to proceed network simulations to predict the running speed of the detector located at the middle section
In section 1, the traffic flow data from 7:00 – 9:55 Jan 5, 2007 is used to obtain the optimized parameter value, the Mean Absolute Percentage Error (MAPE) obtained is 6.4%; in section 2, the study discussed the possibility of applying the optimized parameter value on other days, the MAPE obtained is 3.68%. In section 3, the study discusses if different duration of simulation time would affect the estimation, the total MAPE obtained is 5.60%. The evidence indicates that the method can accurately estimate the running speed of the highway sections without vehicle detectors.

目錄
摘要 II
ABSTRACT III
誌 謝 V
目錄 I
圖目錄 III
表目錄 IV
第一章 緒論 1
1.1 研究動機與背景 1
1.2 研究目的 3
1.3 研究範圍 4
1.4 研究流程 5
第二章 文獻回顧 8
2.1 預測或推估路段行車速率相關文獻回顧 8
2.1.1 預測或推估路段速率 8
2.1.2 轉換速率推估旅行時間 9
2.2 回顧描述高速公路車流狀況之文獻 10
2.2.1 隨機模型 11
2.2.2 起迄點（O-D）矩陣模型 11
2.2.3 其他相關文獻 12
2.3 PARAMICS微觀車流模擬軟體相關文獻探討 13
2.3.1 車流模擬軟體（PARAMICS）介紹 13
2.3.2 國內外運用PARAMICS的文獻 18
2.4 小結 22
第三章 車流理論及模型介紹 23
3.1 基本車流特性 23
3.2 高速公路車流模式 27
3.2.1 高速公路基本路段 27
3.2.2 高速公路上下交流道路段 29
第四章 高速公路行車速率模式構建 34
4.1 數學模型介紹 34
4.2路段平均速率推估 36
4.3 模式與PARAMICS之間的關係 40
4.4 小結 44
第五章 建立模擬路網與數值測試 45
5.1 蒐集路網資料及建構路網 45
5.1.1 建構模擬路網模型流程 45
5.1.2 繪製模擬路網 49
5.2 路網模擬及修正 51
5.3 模擬路網之參數校估 54
5.4 模擬路網參數校估結果 56
5.4.1 最佳模擬路網參數值 57
5.4.2 模擬時間長度不同對預測準確度之影響 63
5.4.3 應用最佳參數值使用在不同日之預測準確度 64
5.4.4 小結 68
第六章 結論與建議 69
6.1 結論 69
6.2 建議 70
參考文獻 72

英文部分
1.Al-Deek, M. H., Ishak, S. S. and Radwan, A. E., 1998, “Impact of Traffic Diversion with ATIS on Traveler''s Safety”, Computers ind Engng, Vol.34, No. 2, pp547-558.
2.Bartin, B., Ozbay, K., Yammaz, O. and Rathi, N., 2005, “Modeling and Simulation of an Unconventional Traffic Circle”, Proceedings of the 8th international IEEE Conference Intelligant Transportantion Systems, Austria.
3.Boel, R. and Mihaylova., 2003, “Hybrid Stochastic Framework for Freeway Traffic Flow Modelling”, In: Intl. Symp. on Information and Communication Technologies, Sept. 24-26, 2003, Dublin, Ireland.
4.Boel, R. and Mihaylova., 2004, “Modelling freeway networks by hybrid stochastic models”, IEEE Intelligent Vehicles Symposium University of Parma, Parma, Italy.
5.Boel, R. and Mihaylova., 2006, “A compositional stochastic Model for real time freeway traffic simulateion”, Transportation Research Part B, Vol. 40, No 4, pp.319-334.
6.Cameron, G., Wylie, B. J. and Mcarthur, D., 1994, “Paramics-moving vehicle on the connection machine”, Edinburgh Parallel Computing Center（EPCC）,The University of Edinburgh. pp 291-300.
7.Chang, Gang Len and Wu, Jifeng., 1994, “Recursive estimation of time-verying origin-destination flows from counts in corridors”, Transportation Research Part B, Vol 28, No 2, pp.141-160.
8.Dailey, D. J., 1999, “A statistical algorithm for estimation speed from single loop volume and occupancy measurements”, Transportation Research Part B, Vol. 33, No 4, pp.313-322.
9.Kim, J. and Lee, S., 2005, “The estimation of freeway origin-destination demand using real-tome traffic date of freeway traffic management system(FTMS)”, Journal of Eastern Asia Society for Transportation Studies, Vol. 6, pp.1695-1707.
10.Lee, Y. I. and Choi, C. Y., 1998, “Development of a link travel time prediction algorithm for urban expressway” ,Proceedings of the fifth ITS World Congress, Seoul, Korea.
11.Lewis, C. D., 1982, Industrial and Business Forecasting Methods: A practical Guide to Exponential Smoothing and Curve Fitting, London: Butterworth Scientific.
12.Lin, P.-W. and Chang, G.-L., 2007, “A generalized mO-Del and solution algorithm for estimation of dynamic freeway origin-destination matrix”, Transportation Research Part B, Vol. 41, No 5, pp.554-572.
13.Mark, C. D., Sadek, A. W. and Rizzo, D., 2004, “Predicting Exprienced Travel Time with Neural Network : A PARAMICS Simulation Study”, IEEE Intelligent Conference, Washington, D.C. USA.
14.Messai, N., Thomas, P., Lefebvre, D. and Moudni, A E., 2002, “Optimal neural networks architectures for the flow–density relationships of traffic mO-Dels”, Mathematical and Computers in Simulation, pp.401-409.
15.Mihaylova, R. and Boel., 2003, “Hybrid stochastic framework for freeway traffic flow modeling”, Proceedings of the Intelligent Symposium on Information and comm.unication Technologies, Trinity Collage, Dublin, Ireland, pp 372-377.
16.Nathan, H G., Carroll, M. and Ajaj, K R., 1985, Traffic Flow Theory., U.S. Department of Transportation, Washington, D.C, Federal Administration.
17.Oh, S., Ritchie, S. and Oh, C., 2001, “Real-time traffic measurement from single loop inductive signatures”, Paper presented at the 81st Annual Meeting of the Transportation Research Board, Washington, D.C.
18.Oh, J. S., Jayakrishnan, R. and Rcker, Will., 2003, “Section travel time estimation from point detection date”, Paper presented at the 82nd Annual Meeting of the Transportation Research Board, Washington, D.C.
19.Payne, H. J. and Diego S., 1979, FREFLO: “A macroscopic simulation model of freeway traffic”, Transportation Research Board, No 722, pp.68-77.
20.Pengpeng, Jiao. and Huapu, Lu., 2005, “Dynamic origin-destination flows estimation for freeway corridors using generic algorithm”, Journal of Eastern Asia Society for Transportation Studies, Vol.6 ,pp.2702-2717.
21.Petty, K F., Bickel, P., Ostland, M., Rice, J., Schoenberg., Jiang, J. and Ritov, Y., 1998, “Accurate estimateion of travel time from single-loop detectors”, Transportation Research Part A, Vol. 32, No 1, pp.1-17.
22.Quadstone, 2004a, Quadstone Paramics V5.1, MO-Deller Reference Manual.
23.Quadstone, 2004b, Quadstone Paramics V5.1, Modeller User Guide.
24.Quadstone, 2005, Quadstone Paramics V5.1, Viewer user Guid.
25.Ran, B., Rouphail, N M., Tarko, A. and Boyce, D E., 1997, “Toward a class of link travel time function for dynamics assignment modeling on signalized networks”, Transportation Research Part B, Vol. 31, No 4, pp.277-290.
26.Sheu, J.-B., Chou, Y.-H. and Weng, M.-C., 2003, “Stochastic System Modeling and Optimal Control of Incident-Induced Traffic Congestion”, Mathematical and Computer Modelling, Vol. 38, pp 533-549.
27.Sheu, J.-B., Chou, Y.-H., and Chen, A., 2004, “Stochastic Modeling and real-time prediction of incident effects on surface street traffic congestion”. Applied Mathematical Modeling, Vol. 28, pp 445-468.
28.Sheu, J.-B., 2006, “A composite traffic flow Modeling approach for incident-responsive network traffic assignment”, Physica A, Vol. 367, pp.461-478.
29.Sigurdu, E., Anna Maria, J. and Thorsteinn, T., 2006, “Traffic Stream Modeling of Road Facilities”, Transport Research Arena Europe 2006.
30.William, R M., Roger, P R. and Elean, S P., 1998, Traffic Engineering.(2nd ed)., Upper Saddle River, New Jersey: Prentice Hall.

中文部分
1.交通部研究所合作研究案，民國94c年，“智慧型運輸走廊路況動態即時資訊開發與建置（二）台北都會區至中正機場智慧運輸走廊交通資訊與控制示範系統建置”。
2.何志宏、蘇志哲、李治綱、凌瑞賢、魏建宏、鄭心婷、何大衛、侯鈞元、朱文正、洪玉芳、薛聖宏、吳悅慈、陳一昌、黃運貴、黃燦煌、張建彥、黃銘崇，民國90b年，“先進式微觀車流模擬器 - PARAMICS應用於台灣地區發展ITS模擬網路之模式校估測試研究”，交通部研究所合作研究案。
3.吳玉珍和趙志民，民國94a年，“高速公路路段速率推估之研究”，交通部研究所合作研究案。
4.李宗憶，民國86年，“考慮動態O-D推估之適應性整體匝道儀控模式研究”， 私立淡江大學，碩士論文。
5.洪士傑，民國94年，“高速公路事件影響區段範圍之研究”，私立淡江大學，碩士論文。
6.胡守任、張勝雄、劉士先、羅孝賢、陶治中、董啟崇、蕭瑞祥、曾惠鈺、林聖偉、康書嫚、李鈺雯、姜禹辰、高啟涵、郭思緯，民國94b年，“智慧型交通資訊蒐集、處理、傳播與旅行者行為系列之研究－號誌化道路路況資訊偵測方法與格式訂定（二）”，交通部研究所合作研究案。
7.徐宏昇、唐文彬、翁忠川、黃淑美、汪曉岫和林志宏，民國88a年，“都市快速道路與高速公路進口匝道車流特性之研究”，交通部研究所合作研究案。
8.徐淵靜和鍾敦沛，民國91年，“高速公路匝道流量控制模式之研究”，運輸計畫季刊，第31卷，第一期，頁59～94，3月。
9.許鉅秉、黃燦煌和白仁德，民國89年，“應用先進式微觀車流模擬器 - PARAMICS構建台灣地區智慧型運輸模擬系統可行性之研究”，交通部研究所合作研究案。
10.黃惠隆、翁忠川、黃月貞，民國93年，“以車輛偵測器推估旅行時間之研究－以北二高為例”，中華技術季刊，第63期，7月。
11.黃智建，民國96年，“車輛偵測器不完整資訊推估旅行時間之研究”，私立逢甲大學，碩士論文。
12.張惠汶，民國91年，“利用公車GPS定位資料推估路段行車速率”，國立交通大學，碩士論文。
13.歐陽餘慶和林豐博，民國88b年，“高速公路進口匝道匯流路段容量分析之架構” 交通部研究所合作研究案。
14.蘇振維、張瓊文、湯儒彥、黃運貴、吳繼虹和陳勁甫，民國90a年，“2001年台灣地區公路容量手冊”，交通部研究所合作研究案。
15.董啟崇、陶治中、胡守任、黃惠隆和趙祖佑，民國92年，“智慧型交通資訊蒐集、處理、傳播與旅行者行為系列之研究－號誌化道路路況資訊偵測方法與格式訂定”，交通部研究所合作研究案。
16.孫以濬、董啟崇、蘇志哲、羅孝賢、劉士先、陶治中、鍾慧諭、簡益正、歐陽恬恬、洪百賢、卓政宏、林國顯、蘇振維、張瓊文、楊幼文、和鄭嘉盈，民國93年，“公路行車資訊管理系統之規劃研究，1/4，－高速公路部分及模式先期研究”，交通部研究所合作研究案。
17.鍾敦沛，民國89年，“高速公路匝道流量控制模式之研究”，國立交通大學，碩士論文。