Sun Yat-sen Freeway Travel Time Prediction using Deep Hybrid Model

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：45

、訪客IP：3.142.197.198

姓名

丁珮雅(Pei-Ya Ting) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

(Sun Yat-sen Freeway Travel Time Prediction using Deep Hybrid Model)

相關論文

★ Dynamic Overlay Construction for Mobile Target Detection in Wireless Sensor Networks	★ 車輛導航的簡易繞路策略
★ 使用傳送端電壓改善定位	★ 利用車輛分類建構車載網路上的虛擬骨幹
★ Why Topology-based Broadcast Algorithms Do Not Work Well in Heterogeneous Wireless Networks?	★ 針對移動性目標物的有效率無線感測網路
★ 適用於無線隨意網路中以關節點為基礎的分散式拓樸控制方法	★ A Review of Existing Web Frameworks
★ 將感測網路切割成貪婪區塊的分散式演算法	★ 無線網路上Range-free的距離測量
★ Inferring Floor Plan from Trajectories	★ An Indoor Collaborative Pedestrian Dead Reckoning System
★ Dynamic Content Adjustment In Mobile Ad Hoc Networks	★ 以影像為基礎的定位系統
★ 大範圍無線感測網路下分散式資料壓縮收集演算法	★ 車用WiFi網路中的碰撞分析

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

隨著人口不斷增長，交通壅塞的情況變得越來越頻繁。因此，旅行時間已成為交通狀況的重要指標。準確的旅行時間訊息可幫助駕駛人更明智地規劃路線，從而有效緩解交通壅塞的情形。在本研究中，我們提出了一種用於高速公路交通的車輛行駛時間預測模型。本研究使用的數據源自於台灣高速公路局的交通資料庫，並預測了中山高速公路中台北到新竹路段的旅行時間。首先，原始數據的缺失值將由Autoencoder進行插補。然後根據時間序列對數據進行分段，並用於預測模型的構建。為了有效捕捉預測高速公路行駛車輛行駛時間所需的隱藏特徵，我們的系統採用深度學習架構，包括GRU神經網絡模型，XGBoost模型和透過線性迴歸將GRU和XGBoost結合為一個新的混合模型。基於實際交通數據的實驗結果表明，我們所提出的系統在預測精確度和執行時間方面都可以取得良好的性能。

摘要(英)

As the population keeps growing, traffic congestion becomes more and more often. Consequently, travel time has become an important indicator of driving experience. Accurate travel time information helps drivers plan their route more wisely and thus effectively alleviate traffic congestion. In this research, we propose a vehicle travel time prediction model for highway traffic. The data used in this research is derived from the traffic database of the Taiwan Freeway Bureau, and the travel time prediction is made for the Sun Yat-sen Freeway between Taipei and Hsinchu. First, the missing value of the raw data is imputed by Autoencoder. The data are then segmented according to time series and are used to build the prediction model. To effectively capture the hidden features required to predict the travel time for the vehicle traveling on the highway, the deep learning architecture is adopted in our system, which includes the GRU neural network model, the XGBoost model, and the Hybrid model that combines the GRU and XGBoost through linear regression. Experimental results based on actual traffic data show that the proposed system can achieve good performance in terms of prediction accuracy and execution time.

關鍵字(中)

★ 旅行時間
★ 門控循環單元
★ 極限梯度提升
★ 混合模型

關鍵字(英)

論文目次

1 Introduction 1
2 RelatedWork 4
2.1 Parameter Models . .. . . . . . . . . . . . . . 4
2.1.1 ARIMA . . . . . . . . . . . . . . . . . . . . . 4
2.1.2 Linear Discriminant Analysis . . . . . . . . 5
2.1.3 Naive Bayes . . . . . . . . . . . . . . . . . 5
2.1.4 ANN . . . . . . . . . . . . . . . . . . . . 6
2.2 Non-Parameter Models . . . . . . . . . . . 7
2.2.1 Support Vector Machine . . . .. .. . . . . . . 8
2.2.2 k-NN . . . . . . . . . . . . . . . . . . . . . 8
2.3 Deep Learning . . . . . . . . . . . . . . . . . .9
3 Preliminary 11
3.1 Data source . . . . . . . . . . . . . . . . . . 11
3.2 Machine learning techniques . . . . . . . . . . 13
3.2.1 k-NN . . . . . . . . . . . . . . . . . . . . 13
3.2.2 XGBoost . . . . . . . . . . . . . . . . . . . 15
3.2.3 RNN . . . . . . . . . . . . . . . . . . . . . 15
3.3 Missing data imputation . . . . . . . . . . . . 19
4 Design 21
4.1 Data Collection . . . . . . . . . . . . . . . . 22
4.2 Data Pre-processing . . . . . . . . . . . . . . 24
4.2.1 Imputation model . . . . . . . . . . . . . . 24
iii
4.2.2 Feature scaling . . . . . . . . . . . . . . . 28
4.2.3 Time-level sliding window sequence . . . . . . 29
4.3 Prediction Model Design . . . . . . . . . . . . 31
4.3.1 GRU Model . . . . . . . . . . . . . . . . . . 32
4.3.2 Hybrid Model . . . . . . . . . . . . . . . . . 34
4.4 Data Post-processing . . . . . . . . . . . . . . 36
4.5 Visualization . . . . . . . . . . . . . . . . . 38
5 Performance 40
5.1 Data Description . . . . . . . . . . . . . . . . 40
5.2 Evaluation . . . . . . . . . . . . . . . . . . . 43
5.2.1 Performance Metrics . . . . . . . . . . . . . .43
5.2.2 Experimental Results . . . . . . . . . . . . . 45
6 Conclusions 57
Reference 58

參考文獻

References
[1] The comprehensive r archive network. https://cran.r-project.org/.
[2] Google map platform. https://cloud.google.com/maps-platform/.
[3] shinyapps.io by rstudio. https://www.shinyapps.io/.
[4] Mohammed S Ahmed and Allen R Cook. Analysis of freeway trac time-series data by using Box-Jenkins techniques. Number 722. 1979.
[5] Brett K Beaulieu-Jones and Jason H Moore. Missing data imputation in the electronic health record using deeply learned autoencoders. In Pacic Symposium on Biocomputing 2017, pages 207{218. World Scientic, 2017.
[6] Chris Beeley. Web application development with R using Shiny. Packt Publishing Ltd, 2013.
[7] Freeway Bureau. Expressway metering electronic toll collection trac data collection support system. http://tisvcloud.freeway.gov.tw/TDCS31.pdf/.
[8] Freeway Bureau. Freeway bureau,motc.http://tisvcloud.freeway.gov.tw/history/TDCS/.
[9] Freeway Bureau. Freeway bureau,motc.https://www.freeway.gov.tw/english/Default.aspx/.
[10] Freeway Bureau. National highway fee evolution. https://transport-curation.nat.gov.tw/toll/index.html/.
[11] Gordon DB Cameron and Gordon ID Duncan. Paramics|parallel microscopic simulationof road trac. The Journal of Supercomputing, 10(1):25{53, 1996.
[12] H Chang, Youngjoo Lee, B Yoon, and Sanghoon Baek. Dynamic near-term traffic how prediction: system-oriented approach based on past experiences. IET intelligent
transport systems, 6(3):292{305, 2012.
[13] Hao Chen, Hesham A Rakha, and Catherine C McGhee. Dynamic travel time prediction using pattern recognition. In 20th World Congress on Intelligent Transportation
Systems. TU Delft, 2013.
[14] Hung-Yu Chien and Che-Hao Chen. Mutual authentication protocol for rd conforming to epc class 1 generation 2 standards. Computer Standards & Interfaces,
29(2):254{259, 2007.
[15] Kyunghyun Cho, Bart Van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio. On the properties of neural machine translation: Encoder-decoder approaches. arXiv
preprint arXiv:1409.1259, 2014.
[16] Mehdi Danech-Pajouh and Maurice Aron. Athena: a method for short-term interurban motorway trac forecasting. Recherche Transports Securite, (6), 1991.
[17] Gary A Davis and Nancy L Nihan. Nonparametric regression and short-term freeway traffic forecasting. Journal of Transportation Engineering, 117(2):178{188, 1991.
[18] Xiang Fei, Chung-Cheng Lu, and Ke Liu. A bayesian dynamic linear model approach
for real-time short-term freeway travel time prediction. Transportation Research Part C: Emerging Technologies, 19(6):1306{1318, 2011.
[19] Martin Fellendorf. Vissim: A microscopic simulation tool to evaluate actuated signal control including bus priority. In 64th Institute of Transportation Engineers Annual Meeting, volume 32. Springer, 1994.
[20] R.Jayakrishnan Hani S. Mahmassani. Dynamic trac assignment and simulation for advanced network informatics (dynasmart), 1995.
[21] Sepp Hochreiter and Jurgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735{1780, 1997.
[22] Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700{4708, 2017.
[23] Sherif Ishak, Prashanth Kotha, and Ciprian Alecsandru. Optimization of dynamic neural network performance for short-term trac prediction. Transportation Research Record, 1836(1):45{56, 2003.
[24] Ranhee Jeong and R Rilett. Bus arrival time prediction using articial neural network model. In Proceedings. The 7th International IEEE Conference on Intelligent
Transportation Systems (IEEE Cat. No. 04TH8749), pages 988{993. IEEE, 2004.
[25] Guiyan Jiang and Ruoqi Zhang. Travel-time prediction for urban arterial road: a case on china. In IVEC2001. Proceedings of the IEEE International Vehicle Electronics
Conference 2001. IVEC 2001 (Cat. No. 01EX522), pages 255{260. IEEE, 2001.
[26] Yiannis Kamarianakis and Poulicos Prastacos. Forecasting traffic how conditions in an urban network: Comparison of multivariate and univariate approaches. Trans-
portation Research Record: Journal of the Transportation Research Board, (1857):74{84, 2003.
[27] Taehyung Kim, Hyoungsoo Kim, and David J Lovell. Traffic how forecasting: overcoming memoryless property in nearest neighbor non-parametric regression. In Pro-
ceedings. 2005 IEEE Intelligent Transportation Systems, 2005., pages 965{969. IEEE, 2005.
[28] Lajos Kisgyorgy and Laurence R Rilett. Travel time prediction by advanced neural network. Periodica Polytechnica Civil Engineering, 46(1):15{32, 2002.
[29] Brent Komer, James Bergstra, and Chris Eliasmith. Hyperopt-sklearn: automatic hyperparameter conguration for scikit-learn. In ICML workshop on AutoML, pages 2825{2830. Citeseer, 2014.
[30] Kranti Kumar, M Parida, and VK Katiyar. Short term traffic how prediction for a non urban highway using articial neural network. Procedia-Social and Behavioral
Sciences, 104:755{764, 2013.
[31] Jaimyoung Kwon, Benjamin Coifman, and Peter Bickel. Day-to-day travel-time trends and travel-time prediction from loop-detector data. Transportation Research
Record, 1717(1):120{129, 2000.
[32] Hyunjo Lee, Nihad Karim Chowdhury, and Jaewoo Chang. A new travel time prediction method for intelligent transportation systems. In International Conference
on Knowledge-Based and Intelligent Information and Engineering Systems, pages 473{483. Springer, 2008.
[33] Sangsoo Lee and Daniel B Fambro. Application of subset autoregressive integrated moving average model for short-term freeway trac volume forecasting. Transporta-
tion Research Record, 1678(1):179{188, 1999.
[34] EB Lewis. Control of body segment dierentiation in drosophila by the bithorax gene complex. In Genes, Development and Cancer, pages 239{253. Springer, 1982.
[35] Hsuan-Ning Liu. The study of travel time prediction for freeway by using dynamic k-nn method. pages 1{32, 2016.
[36] Tao Liu, Jihui Ma, Wei Guan, Yue Song, and Hu Niu. Bus arrival time prediction based on the k-nearest neighbor method. In 2012 Fifth International Joint Conference
on Computational Sciences and Optimization, pages 480{483. IEEE, 2012.
[37] Daisik Nam, Hyunmyung Kim, Jaewoo Cho, and R Jayakrishnan. A model based on deep learning for predicting travel mode choice. In Proceedings of the Transportation
Research Board 96th Annual Meeting Transportation Research Board, Washington, DC, USA, pages 8{12, 2017.
[38] Andrew Ng et al. Sparse autoencoder. CS294A Lecture notes, 72(2011):1{19, 2011.
[39] Ministry of Transportation and Communications. Roadside facilities real-time traffic information release standard format v1.1. https://www.motc.gov.tw/.
[40] opendeep.org. Denoising autoencoder implemented on the mnist dataset. http://www.opendeep.org/v0.0.5/docs/tutorial-your-first-model/.
[41] John Rice and Erik Van Zwet. A simple and eective method for predicting travel times on freeways. IEEE Transactions on Intelligent Transportation Systems,
5(3):200{207, 2004.
[42] Steve Robinson and John Polak. 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, (1935):47{56, 2005.
[43] Sehyun Tak, Sunghoon Kim, Kiate Jang, and Hwasoo Yeo. Real-time travel time prediction using multi-level k-nearest neighbor algorithm and data fusion method.
In Computing in Civil and Building Engineering (2014), pages 1861{1868. 2014.
[44] PVV Theja and Lelitha Vanajakshi. Short term prediction of trac parameters using support vector machines technique. In 2010 3rd International Conference on
Emerging Trends in Engineering and Technology, pages 70{75. IEEE, 2010.
[45] Wichai Treethidtaphat, Wasan Pattara-Atikom, and Sippakorn Khaimook. Bus arrival time prediction at any distance of bus route using deep neural network model.
In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pages 988{992. IEEE, 2017.
[46] Mascha Van Der Voort, Mark Dougherty, and Susan Watson. Combining kohonen maps with arima time series models to forecast traffic how. Transportation Research
Part C: Emerging Technologies, 4(5):307{318, 1996.
[47] Lelitha Vanajakshi and Laurence R Rilett. Support vector machine technique for the short term prediction of travel time. In 2007 IEEE Intelligent Vehicles Symposium,
pages 600{605. IEEE, 2007.
[48] Pascal Vincent, Hugo Larochelle, Isabelle Lajoie, Yoshua Bengio, and Pierre-Antoine Manzagol. Stacked denoising autoencoders: Learning useful representations in a
deep network with a local denoising criterion. Journal of machine learning research, 11(Dec):3371{3408, 2010.
[49] Eleni I Vlahogianni, John C Golias, and Matthew G Karlaftis. Short-term traffic forecasting: Overview of objectives and methods. Transport reviews, 24(5):533{557,
2004.
[50] Fei-Yue Wang et al. Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications. 2010.
[51] Billy Williams. Multivariate vehicular traffic how prediction: evaluation of arimax modeling. Transportation Research Record: Journal of the Transportation Research
Board, (1776):194{200, 2001.
[52] Billy M Williams and Lester A Hoel. Modeling and forecasting vehicular traffic how as a seasonal arima process: Theoretical basis and empirical results. Journal of
transportation engineering, 129(6):664{672, 2003.
[53] Chun-HsinWu, Jan-Ming Ho, and Der-Tsai Lee. Travel-time prediction with support vector regression. IEEE transactions on intelligent transportation systems, 5(4):276{
281, 2004.
[54] M Yang, C Chen, L Wang, X Yan, and L Zhou. Bus arrival time prediction using support vector machine with genetic algorithm. Neural Network World, 26(3):205,
2016.
[55] Hongsuk Yi, HeeJin Jung, and Sanghoon Bae. Deep neural networks for traffic how prediction. In 2017 IEEE International Conference on Big Data and Smart
Computing (BigComp), pages 328{331. IEEE, 2017.
[56] Bin Yu, William HK Lam, and Mei Lam Tam. Bus arrival time prediction at bus stop with multiple routes. Transportation Research Part C: Emerging Technologies,
19(6):1157{1170, 2011.

指導教授

孫敏德

審核日期

2019-7-25

推文