提供旅行者必要的交通資訊以作為路徑選擇的決策依據為先進旅行者資訊
系統(ATIS)的主要目的，旅行時間資訊為交通資訊中相當重要的資訊之一，而依
旅次預計出發時間查詢起迄點的旅行時間資訊為旅行者於行前規劃路徑時的重
要需求，因此為提供符合旅行者需求的交通服務資訊，本研究提出一複合式旅行
時間預測模式以進行未來多時階的旅行時間預測演算，於不同預測時間範圍採用
不同預測方法進行旅行時間預測以提高預測準確度。
本研究的複合式旅行時間預測模式共包含三個演算模組，分別為異常資料處
理模組、資料融合模組與旅行時間預測模組，異常資料處理模組針對離群與漏失
資料做前處理，而資料融合模組是以VD 資料和ETC 資料進行資料融合，目的
為提升演算資料的準確度，旅行時間預測模組則分別以歷史資料和即時資料構建
兩卡曼濾波預測模式(KF1、KF2)，以歷史資料構建傅立葉轉換預測模式(DTFT)。
本研究分別建立以KF1 和DTFT 所組合的複合式預測模式一與以KF2 和DTFT
所組合的複合式預測模式二，建立方式為將預測時間長度分為短期與長期兩個時
間範圍，並比較預測模式於不同預測時間長度的預測績效以建立合適的長短期門
檻值並決定預測模式適用的時間範圍。
以國道一號林口交流道到中壢交流道為路徑範圍，分別以不同日型態(平常
日、周末日)進行測試，結果顯示兩複合式旅行時間預測模式的預測績效皆比單
一預測模式佳，且長短期門檻值會依不同日型態而有所不同，而比較兩複合式預
測模式於不同日型態的預測績效後，本研究建議平常日採用複合式預測模式一進
行旅行時間預測，周末日採用複合式預測模式二進行旅行時間預測。

The primary objective of Advanced Traveler Information System (ATIS) is providing travelers the necessary traffic information as decision guidance of route selection, and travel time information is one of the most important traffic information; querying travel time based on trip departing time is indispensable demand when travelers doing pre-trip planning. Hence, in order to provide traffic information which meets the need of travelers, this study proposes a "Hybrid Travel time Prediction Model" to forecast multi-step ahead travel time, applying different prediction methods within different prediction periods to conduct travel time prediction to improve prediction accuracy.
Hybrid travel time prediction model includes abnormal data processing module, data fusion module and travel time prediction module. The abnormal data processing module is for addressing outlier and missing data interpolation. The data fusion module fuses VD data and ETC data to improve the accuracy of input data. The travel time prediction module respectively constructs two Kalman Filter prediction models (KF1, KF2) with historical data and real-time data, and a Fourier Transform prediction model with historical data (DTFT). This study proposes hybrid prediction modelⅠwith KF1 and DTFT, hybrid prediction modelⅡwith KF2 and DTFT, dividing prediction length into short-term and long-term, and comparing performance of prediction models to decide the threshold and applicable prediction models for both short-term and long term prediction.
With respect to the tests for different daily traffic profiles (weekday, weekend), the result shows that the performance of the two hybrid travel time prediction models is significantly better than the single prediction model. The result also supports applying the hybrid prediction modelⅠ in weekday and the hybrid prediction modelⅡ in weekend.

誌謝................................................................................................................................ I
摘要............................................................................................................................... II
Abstract ........................................................................................................................ III
目錄.............................................................................................................................. IV
圖目錄.......................................................................................................................... VI
表目錄....................................................................................................................... VIII
第一章 緒論.................................................................................................................. 1
1.1 研究動機......................................................................................................... 1
1.2 研究目的......................................................................................................... 2
1.3 研究內容與範圍............................................................................................. 2
1.4 研究流程......................................................................................................... 3
第二章 文獻回顧.......................................................................................................... 6
2.1 先進旅行者資訊系統..................................................................................... 6
2.1.1 國外先進旅行者資訊系統.................................................................. 6
2.1.2 國內先進旅行者資訊系統................................................................ 10
2.2 資料融合....................................................................................................... 13
2.2.1 資料融合概述.................................................................................... 13
2.2.2 資料融合應用.................................................................................... 16
2.3 旅行時間預測............................................................................................... 19
2.4 文獻回顧小結............................................................................................... 29
第三章 旅行時間預測模式建構................................................................................ 31
3.1 異常資料處理模組....................................................................................... 31
3.1.1 線性插補............................................................................................ 32
3.1.2 z 分數(z-score)................................................................................... 32
3.1.3 異常資料處理流程............................................................................ 33
3.2 資料融合模組............................................................................................... 34
3.2.1 交通資料特性.................................................................................... 34
3.2.2 路段旅行時間定義............................................................................ 35
3.2.3 資料融合演算法................................................................................ 38
3.3 旅行時間預測模組....................................................................................... 39
3.3.1 卡曼濾波法........................................................................................ 39
3.3.2 傅立葉轉換技術................................................................................ 44
3.3.3 旅行時間預測模組開發.................................................................... 48
3.3.4 複合式旅行時間預測模式................................................................ 54
第四章 演算模式庫實作開發.................................................................................... 59
4.1 系統開發環境............................................................................................... 59
4.2 資料庫設計規則........................................................................................... 61
4.3 演算模式庫實作........................................................................................... 66
第五章 數值實驗設計與分析.................................................................................... 69
5.1 實驗範圍....................................................................................................... 69
5.2 旅行時間真值取得方式............................................................................... 70
5.3 績效評估指標............................................................................................... 71
5.4 資料融合模組驗證....................................................................................... 73
5.5 旅行時間預測模式績效評估....................................................................... 75
5.5.1 卡曼濾波預測方法............................................................................ 75
5.5.2 離散時間傅立葉轉換預測方法........................................................ 80
5.5.3 複合式旅行時間預測模式................................................................ 82
第六章 結論與建議.................................................................................................... 90
6.1 結論............................................................................................................... 90
6.2 建議............................................................................................................... 92
參考文獻...................................................................................................................... 94

1. 交通部運輸研究所(2010)，99-TDB001 車路整合系統發展趨勢與ITS 節能減碳關聯之研究。
2. 何旺宗(2010)，「資料融合技術結合類神經網路對高速公路事件延遲時間預測
之研究」，國立成功大學交通管理科學系碩士論文。
3. 吳欣潔(2004)，「熵應用於交通資料融合之研究」，國立交通大學運輸科技與管理學系碩士論文。
4. 吳金杰(2005)，「融合偵測器與探測車資料預測高速公路旅時時間之研究」，國立中央大學碩士論文。
5. 吳瑞豐(2006)，「最佳權重法應用於交通資料融合」，國立交通大學運輸科技與管理學系碩士論文。
6. 李穎(2002)，「類神經網路運用於國道客運班車旅行時間預測模式之研究」，國立成功大學交通管理科學系碩士論文。
7. 林士傑(2001)，「高速公路旅行時間預測模式之研究-類神經網路之應用」，國立成功大學碩士論文。
8. 邱孟佑(2010)，「以車流狀態為基礎之高速公路旅行時間預測模式」，國立交通大學交通運輸研究所博士論文。
9. 洪偉勛(2013)，「高速公路旅行時間推估模式之開發與應用－以國道五號為
例」，國立成功大學交通管理科學系碩士論文。
10. 張佳雯(2007)，「資料融合於異質性資料推估路段行駛速率之研究」，國立台灣土木工程學系碩士論文。
11. 許政憲(2006)，「資料融合技術應用於事故影響下高速公路旅行時間預測之研究」，國立成功大學交通管理科學系碩士論文。
12. 郭中天(2002)，「公車到站時間暨複合路線旅行時間預估模式之研究」，國立台灣土木工程學系碩士論文。
13. 陳寶如(2006)，「市區路段動態旅行時間預測之研究」，國立台灣土木工程學系碩士論文。
14. 曾志維(2004)，「Dempster-Shafer 理論於交通資料整合之應用」，國立交通大學運輸科技與管理學系碩士論文。
15. 蔡繼光(2009)，「高速公路旅行時間預測-以k-NN 法及分群方法探討」，國立交通大學運輸科技與管理學系碩士論文。
16. 魏健宏、陳奕志(2001)，「類神經網路模式在國內交通運輸研究之成果分析」，運輸計畫季刊第30 卷第2 期，pp.323-348。
17. 羅至浩(2008)，「以加入準確度之團隊共識法進行交通資料融合」，國立交通大學運輸科技與管理學系碩士論文。
18. Bachmann, C., Abdulhai, B., Roorda, M. J., &; Moshiri, B. (2013), "A comparative assessment of multi-sensor data fusion techniques for freeway traffic speed estimation using microsimulation modeling", Transportation Research Part C,
Vol.26, pp.33-48.
19. Billings, D., &; Yang, J. S. (2006), "Application of the ARIMA models to urban roadway travel time prediction-a case study". IEEE International Conference on Systems, Man and Cybernetics, Vol. 3, pp.2529-2534.
20. Chen, M., &; Chien, S. I. J. (2001), "Dynamic Freeway Travel Time Prediction Using Probe Vehicle Data: Link-based vs. Path-based", Transportation Research Record: Journal of the Transportation Research Board, Vol.1768, pp.157-161.
21. Cheu, R. L., Lee, D. H., &; Xie, C. (2001), "An Arterial Speed Estimation Model Fusing Data from Stationary and Mobile Sensors", IEEE Intelligent Transportation
Systems Conference, pp.573-578.
22. Chien, S. I., Liu, X., &; Ozbay, K. (2003), "Predicting Travel Times for the South Jersey Real-Time Motorist Information System", Transportation Research Record:
Journal of the Transportation Research Board, Vo1.1855, pp.32-40.
23. Dailey, D. J., Harn, P., &; Lin, P. J. (1996), "ITS Data Fusion", No. WA-RD 410.1, Washington State Department of Transportation.
24. Dia, H. (2001), "An object-oriented neural network approach to short-term traffic forecasting" , European Jurnal of Operation Research, Vol.131, pp.253-261.
25. Hall, D. L., &; McMullen, S. A. (2004), "Mathematical Techniques in Multisensor Data Fusion", Artech House, Inc. Norwood, MA.
26. Klein, L. A. (2001), "Sensor Technologies and Data Requirements for ITS", Artech House.
27. Kuchipudi, C. M., &; Chien, S. I. (2003), "Development of a Hybrid Model for Dynamic Travel Time Prediction", Transportation Research Record: Journal of the Transportation Research Board, Vo1.1855, pp.22-31.
28. Linn, R. J., Hall, D. L., &; Llinas, J. (1991), "Survey of Multisensor Data Fusion Systems", Proceedings of the SPIE-The International Society for Optical Engineering, Vol. 1470, pp.13-29.
29. Liu, X., Chien, S. I., &; Chen, M. (2012), "An adaptive model for highway travel time prediction", Journal of Advanced Transportation.
30. Makridakis, S., Hibon, M., &; Moser, C. (1979), "Accuracy of forecasting: An empirical investigation", Journal of the Royal Statistical Society. Series A (General), pp.97-145.
31. Makridakis, Spyros, et al., (1982), "The Accuracy of Extrapolation (Time Series) Methods: Results of a Forecasting Competition", Journal of Forecasting, Vol.1,
pp.111-153.
32. Makridakis, Spyros, et al., (1993), "The M2-Competition: A real-time judgmentally based forecasting study", International Journal of Forecasting, Vol.9, No.4, pp.5-22.
33. Makridakis, S., &; Hibon, M. (2000), "The M3-Competition: results, conclusions and implications", International Journal of Forecasting, Vol.16, No.4, pp.451-476.
34. Rice, J., &; Van Zwet, E. (2004), "A Simple and Effective Method for Predicting Travel Times on Freeways", IEEE Transactions on Intelligent Transportation Systems, Vol.5, No.3, pp.200-207.
35. Robinson, S., &; Polak, J. W. (2005), "Modeling Urban Link Travel Time with Inductive Loop Detector Data by Using the k-NN Method", Transportation Research Record: Journal of the Transportation Research Board, Vol.1935, pp.47-56.
36. Sarma, V. V. S., &; Raju, S. (1991), "Multisensor Data Fusion and Decision Support for Airborne Target Identification", IEEE Transactions on Systems, Man and
Cybernetics, Sept.-Oct.
37. Smith, B. L., &; Demetsky, M. J. (1997), "Traffic Flow Forecasting: Comparison of Modeling Approaches", Journal of Transportation Engineering, Vol.123, No.4, pp.261-266.
38. van Lint, J. W. C., Hoogendoorn, S. P., &; van Zuylen, H. J. (2005), "Accurate freeway travel time prediction with state-space neural networks under missing data",
Transportation Research Part C, Vol.13, pp.347-369.
39. van Lint, J. W. C., Hoogendoorn, S. P., &; van Zuylen, H. J. (2002), "Freeway travel time prediction with state-space neural networks: modeling state-space dynamics with recurrent neural networks", Transportation Research Record: Journal of the Transportation Research Board, Vol.1811, pp.30-39.
40. Wu, C. H., Ho, J. M., &; Lee, D. T. (2004), "Travel time prediction with support vector regression", IEEE Transactions on Intelligent Transportation Systems, Vol.5,
No.4, pp.276-281.
41. Wu, H., Siegel, M., &; Ablay, S. (2003), "Sensor Fusion Using Dempster-Shafer Theory II: Static Weighting and Kalman Filter-like Dynamic Weighting", IEEE Instrumentation and Measurement Technology Conference.
42. Wu, H., Siegel, M., Stiefelhagen, R., &; Yang, J. (2002), "Sensor Fusion Using Dempster-Shafer Theory", IEEE Instrumentation and Measurement Technology Conference.