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研究生:張晛承
研究生(外文):Chang, Hsien-Cheng
論文名稱:以交通特性探勘為基礎之即時交通預測模型及車輛路徑規劃方法
論文名稱(外文):Exploiting Traffic Patterns for Real-Time Traffic Prediction and Vehicle Routing
指導教授:楊舜仁楊舜仁引用關係
指導教授(外文):Yang, Shun-Ren
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:英文
論文頁數:32
中文關鍵詞:交通預測空間和時間關聯性路徑規劃
外文關鍵詞:traffic predictionspatial and temporal correlationsvehicle routing
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在車載網路中,即時的交通預測是一項十分重要的發展,其可用來降低交通擁塞及改善車輛移動性。近幾年,台灣政府積極布建道路流量監控系統,並有交通部運輸管理研究所專門負責各項關於交通運輸方面的研究;此項建設使得我們可以得到最即時的交通流量資訊,並且發展相關的研究與應用。本篇論文主要利用統計學上的一些工具來分析這些實際觀測到的數據,從中探勘出重要的交通特性;並根據這些特性,設計一個適用於車載網路中的即時交通預測模型,此預測模型可預估未來任一時間點的交通狀況。此外,我們還提出一種路徑規劃方法,此方法結合現存的最短路徑演算法和我們所提出的交通預測模型,其可以規劃出最佳的行車路徑,讓駕駛人員可以在較少的時間內抵達目的地。最後,在效能評估的實驗中,我們使用實際觀測到的數據來進行測試,結果顯示我們的預測模型無論在短期或長期的預測,皆擁有精確的預估能力;同時,我們也說明路徑規劃系統擁有對未來交通狀況預測的能力,能夠規劃出較佳的路徑供駕駛人員做為參考。
Real-time traffic prediction is a fundamental capability of reducing traffic congestion and improving traffic mobility. Recently, the traffic information in the urban area of the
Taipei city is available from Institute of Transportation, Ministry of Transportation and Communications, Taiwan. Given this information, it is possible to analyze and extract some traffic patterns. In this paper, we use these patterns to design a semi-parametric prediction model which provides an efficient mean to estimate accurately the future traffic
conditions. Furthermore, we propose a novel vehicle routing algorithm which can plan routes with less delay for the drivers. The vehicle routing algorithm is composed of our
proposed prediction model and the existing shortest path algorithm. Finally, in the performance evaluation, we show the capability of our methodology to predict future traffic
conditions accurately and to enable the drivers to arrive the destinations within less time.
Abstract i
Contents i
List of Figures iv
List of Tables vi
1 Introduction 1
2 Related Work 3
2.1 The ARMA Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.2 The MST-ARMA Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3 Information of The Empirical Data 5
4 The Semi-Parametric Prediction Model 8
4.1 Overall Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
4.2 Temporal and Spatial Correlations for estimating Yk;t . . . . . . . . . . . . 10
4.2.1 Temporal Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.2.2 Spatial Correlation . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.3 A Spatial-Temporal Transient Model for Estimating Yk;t . . . . . . . . . . 14
5 Analysis of Different Estimations for Yk;t 15
6 Vehicle Routing Algorithm 17
ii
7 Performance Evaluation 19
7.1 Setting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
7.2 Results and Analysis in Prediction Performance Evaluation . . . . . . . . . 22
7.3 Results and Analysis in Routing Performance Evaluation . . . . . . . . . . 28
8 Conclusion 30
Bibliography 31
[1] Wanli Min and Laura Wynter. Real-Time Road Traffic Prediction with Spatio-Temporal Correlations. Transportation Research Part C: Emerging Technologies, Volume 19, Issue 4, August 2011, Pages 606616.
[2] Jungme Park, Dai Li, Yi L. Murphey, Johannes Kristinsson, Ryan McGee, Ming Kuang, and Tony Phillips. Real Time Vehicle Speed Prediction using a Neural Network Traffic Model. Proceedings of International Joint Conference on Neural Networks, San Jose, California, USA, July 31 - August 5, 2011.
[3] H. Zare Moayedi and M. A. Masnadi-Shirazi. Arima Model for Network Traffic
Prediction and Anomaly Detection. Information Technology, 2008. ITSim 2008. International Symposium on.
[4] Samuel Kotz and Norman L. Johnson. Encyclopedia of Statistical Sciences. Campbell B. Read
[5] Hans-Peter Kriegel, Matthias Renz, Matthias Schubert and Andreas Zufle. Efficient
Traffic Density Prediction in Road Networks Using Suffix Trees. German Journal on Artificial Intelligence Springer-Verlag 2012.
[6] Tao Cheng, James Haworth and Jiaqiu Wang. Spatio-Temporal Autocorrelation of Road Network Data. Journal of Geographical Systems Geographical Information, Analysis, Theory, and Decision Springer-Verlag 2011.
[7] Huiyu Zhou and Shingo Mabu. Generalized Rule Extraction and Traffic Prediction in the Optimal route Search. Evolutionary Computation (CEC), 2010 IEEE Congress on , vol., no., pp.1,8, 18-23 July 2010.
[8] Huiyu Zhou, Wei Wei, Kaoru Shimada, Shingo Mabu and Kotaro Hirasawa. Time Related Association Rules Mining with Attributes Accumulation Mechanism and its Application to Traffic Prediction. Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.12, No.5 pp. 467-478, 2008.
[9] Huiyu Zhou, Shingo Mabu, Kaoru Shimada and Kotaro Hirasawa. Traffic Prediction using Time Related Association Rules and Vehicle Routing. Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on, vol., no., pp.2203,2208, 9-12 Oct. 2011.
[10] Edmund S. Yu and C. Y. Roger Chen. Traffic Prediction Using Neural Networks. Global Telecommunications Conference, 1993, including a Communications Theory Mini-Conference. Technical Program Conference Record, IEEE in Houston. GLOBECOM ’93., IEEE, vol., no., pp.991,995 vol.2, 29 Nov-2 Dec 1993.
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