Multi-headed CNN on Short-term Traffic Flow Forecasting in Taoyuan Urban Area

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：29

、訪客IP：3.149.25.117

姓名

黃鐙毅(Tens-I Huang) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

(Multi-headed CNN on Short-term Traffic Flow Forecasting in Taoyuan Urban Area)

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)

隨著城鄉交通流量的不斷增加，交通信息在智慧型運輸系統中發揮著重要作用。在過去幾年中，許多不同的方法已用於短期交通流量預測。城市交通流量預測有助於為交通壅堵提供早期預警，並作為旅行時間預測的特徵。
本文提出了一種交通流預測系統，用於預測和視覺化桃園市區的小時交通流量。研究使用了從安裝在交叉路口的RFID 車流檢測器收集而來的整年交通數據。為了解決由定期維護或設備故障引起的數據缺失問題，三種不同的缺漏值填補技術被用來清除未校正的數據。兩個基於深度學習的交通流量預測模型，即遞歸神經網路（RNN）和卷積神經網路（CNN），用於預測短期交通流量。最後，設置了一個RShiny在線監平台來演示我們的結果。為了評估所提出的模型的性能，採用了幾個評估指標來驗證我們的模型。實驗結果表明，CNN 模型的準確率在所有模型中表現最佳。

摘要(英)

With the steady increasing of both rural and urban traffic flow, traffic information plays an important role in the Intelligent Transport System. Over the last few years, many different methodologies have been used for short-term traffic flow forecasting. The urban traffic flow forecast helps to provide an early warning for traffic congestion and works as a feature for travel time prediction.
In this thesis, we propose a traffic flow prediction system to predict and visualize hourly traffic flow in Taoyuan urban area. A full year traffic data collected from RFID traffic flow detectors installed at the intersections are used in our research. To solve the missing data problem caused by scheduled maintenance or device malfunction, three imputation techniques are carried out to cleanse the uncorrected data. Two deep learning-based traffic flow prediction models, i.e., Recurrent Neural Network and Convolutional Neural Network (CNN), are used to forecast the short-term traffic flow. Finally, a R-Shiny online dashboard is set up to demonstrate our results. To assess the performance of the proposed model, several evaluation metrics are adopted to validate our models. The experiment results show that the accuracy of the CNN model is the highest among all of the models.

關鍵字(中)

★ 智慧型運輸系統
★ 車流量
★ 預測
★ 深度學習
★ 卷積神經網絡

關鍵字(英)

★ ITS
★ Traffic flow
★ Forecasting
★ Deep learning
★ Convolutional neural network

論文目次

1 Introduction 1
2 RelatedWork 4
2.1 Urban traffic flow prediction 4
2.1.1 Regression model 5
2.1.2 Traditional machine learning and neural networks 6
2.1.3 Deep learning 7
2.2 Freeway traffic flow prediction 8
3 Preliminary 11
3.1 Data source 11
3.2 Machine learning techniques 13
3.2.1 Regression techniques 13
3.2.2 Deep learning techniques 14
3.3 Missing value imputation 20
3.3.1 Mean or Median imputation 20
3.3.2 Linear interpolation 20
3.3.3 Deep learning approach 21
4 Design 22
4.1 System Model 22
4.2 Data Collection 24
4.3 Missing Data Imputation 25
4.4 Prediction Models 29
4.4.1 Training data preparing 29
4.4.2 Feature scaling 30
4.4.3 RNN model 31
4.4.4 CNN model 32
4.4.5 Model regularization 35
4.5 Visualization 36
5 Performance 40
5.1 Data Collection 40
5.2 Evaluation metrics 41
5.3 Experimental Results 44
5.3.1 Imputation methods 44
5.3.2 RNN models 45
5.3.3 CNN models 48
5.3.4 Comparison between different models 51
6 Conclusions 54
Reference 55

參考文獻

[1] Baher Abdulhai, Himanshu Porwal, and Will Recker. Short term freeway traffic flow prediction using genetically-optimized time-delay-based neural networks. 1999.
[2] Sylvain Arlot, Alain Celisse, et al. A survey of cross-validation procedures for model selection. Statistics surveys, 4:40–79, 2010.
[3] Michele Banko and Eric Brill. Scaling to very very large corpora for natural lan- guage disambiguation. In Proceedings of the 39th annual meeting on association for computational linguistics, pages 26–33. Association for Computational Linguistics, 2001.
[4]J Baras, W Levine, and Tahsin Lin. Discrete-time point processes in urban traffic queue estimation. IEEE transactions on Automatic Control, 24(1):12–27, 1979.
[5] Chris Beeley. Web application development with R using Shiny. Packt Publishing Ltd, 2013.
[6] Felix Biessmann, David Salinas, Sebastian Schelter, Philipp Schmidt, and Dustin Lange. Deep learning for missing value imputationin tables with non-numerical data. In Proceedings of the 27th ACM International Conference on Information and Knowl- edge Management, pages 2017–2025. ACM, 2018.
[7] Richard Bishop. Intelligent vehicle technology and trends. 2005.
[8] Florida bycicle associaion. Info for motorists. https://floridabicycle.org/ for-motorists/.
[9] Manoel Castro-Neto, Young-Seon Jeong, Myong-Kee Jeong, and Lee D Han. Online-svr for short-term traffic flow prediction under typical and atypical traffic conditions.Expert systems with applications, 36(3):6164–6173, 2009.
[10] Tianqi Chen and Carlos Guestrin. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pages 785–794. ACM, 2016.
[11] E Chung and N Rosalion. Short term traffic flow prediction. In AUSTRALASIAN TRANSPORT RESEARCH FORUM (ATRF), 24TH, 2001, HOBART, TASMA- NIA, AUSTRALIA, 2001.
[12] M Dalto. Deep neural networks for time series prediction with applications in ultra- short-term wind forecasting. rn ((vartheta) 1).
[13] Gary A Davis and Nancy L Nihan. Nonparametric regression and short-term freeway traffic forecasting. Journal of Transportation Engineering, 117(2):178–188, 1991.
[14] Hussein Dia. An object-oriented neural network approach to short-term traffic fore- casting. European Journal of Operational Research, 131(2):253–261, 2001.
[15] AiLing Ding, XiangMo Zhao, and LiCheng Jiao. Traffic flow time series prediction based on statistics learning theory. In Intelligent Transportation Systems, 2002. Proceedings. The IEEE 5th International Conference on, pages 727–730. IEEE, 2002.
[16] Mark S Dougherty and Mark R Cobbett. Short-term inter-urban traffic forecasts using neural networks. International journal of forecasting, 13(1):21–31, 1997.
[17] Yanjie Duan, Yisheng Lv, Yu-Liang Liu, and Fei-Yue Wang. An efficient realization of deep learning for traffic data imputation. Transportation research part C: emerging technologies, 72:168–181, 2016.
[18] Fabian Fuchs. Forecasting financial time series with cnns aims cdt mini project supervised by prof. stephen roberts.
[19] Nathan H Gartner, Chronis Stamatiadis, and Philip J Tarnoff. Development of advanced traffic signal control strategies for intelligent transportation systems: Mul- tilevel design. Transportation Research Record, (1494), 1995.
[20] HX Ge, SQ Dai, LY Dong, and Yu Xue. Stabilization effect of traffic flow in an ex- tended car-following model based on an intelligent transportation system application. Physical Review E, 70(6):066134, 2004.
[21] Bidisha Ghosh, Biswajit Basu, and Margaret O’Mahony. Bayesian time-series model for short-term traffic flow forecasting. Journal of transportation engineering, 133(3):180–189, 2007.
[22] J. Gilmore and N. Abe. A neural networks system for traffic congestion forecasting. In Proceedings of 1993 International Conference on Neural Networks (IJCNN-93- Nagoya, Japan), volume 2, pages 2025–2028 vol.2, Oct 1993.
[23] Lovedeep Gondara and Ke Wang. Mida: Multiple imputation using denoising au- toencoders. In Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 260–272. Springer, 2018.
[24] Ian J Goodfellow, Yaroslav Bulatov, Julian Ibarz, Sacha Arnoud, and Vinay Shet. Multi-digit number recognition from street view imagery using deep convolutional neural networks. arXiv preprint arXiv:1312.6082, 2013.
[25] Harold Greenberg. An analysis of traffic flow. Operations research, 7(1):79–85, 1959.
[26] Mohammad M Hamed, Hashem R Al-Masaeid, and Zahi M Bani Said. Short-term prediction of traffic volume in urban arterials. Journal of Transportation Engineering, 121(3):249–254, 1995.
[27] Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh. A fast learning algorithm for deep belief nets. Neural computation, 18(7):1527–1554, 2006.
[28] Geoffrey E Hinton and Ruslan R Salakhutdinov. Reducing the dimensionality of data with neural networks. science, 313(5786):504–507, 2006.
[29] Wenhao Huang, Guojie Song, Haikun Hong, and Kunqing Xie. Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Transac- tions on Intelligent Transportation Systems, 15(5):2191–2201, 2014.
[30] Satu Innamaa. Short-term prediction of traffic situation using mlp-neural networks. In Proceedings of the 7th world congress on intelligent transport systems, Turin, Italy, pages 6–9, 2000.
[31] Ministry of Transportation Institute of Transportation and Communications. Appli- cation Research of Radio Frequency Identification (RFID) Electronic Tag Technology in Traffic Information Collection. 2006.
[32] Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015.
[33] Rafal Jozefowicz, Wojciech Zaremba, and Ilya Sutskever. An empirical exploration of recurrent network architectures. In International Conference on Machine Learning, pages 2342–2350, 2015.
[34] Yiannis Kamarianakis and Poulicos Prastacos. Space–time modeling of traffic flow. Computers & Geosciences, 31(2):119–133, 2005.
[35] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.
[36] Ron Kohavi et al. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Ijcai, volume 14, pages 1137–1145. Montreal, Canada, 1995.
[37] Jan Koutnik, Klaus Greff, Faustino Gomez, and Juergen Schmidhuber. A clockwork rnn. arXiv preprint arXiv:1402.3511, 2014.
[38] Soi Hoi Michael Lam and Trinh Dinh Toan. Short-term travel time prediction using support vector regression. Technical report, 2008.
[39] Head K Larry. Event—based short—term traffic flow prediction model. Transporta- tion Research Record, 1510:125–143, 1995.
[40] Moshe Levin and Yen-Der Tsao. On forecasting freeway occupancies and volumes (abridgment). Transportation Research Record, (773), 1980.
[41] Pawan Lingras, Satish C Sharma, Phil Osborne, and Iftekhar Kalyar. Traffic volume time-series analysis according to the type of road use. Computer-Aided Civil and Infrastructure Engineering, 15(5):365–373, 2000.
[42] Ping Luo, Xinjiang Wang, Wenqi Shao, and Zhanglin Peng. Towards understanding regularization in batch normalization. 2018.
[43] Yisheng Lv, Yanjie Duan, Wenwen Kang, Zhengxi Li, and Fei-Yue Wang. Traffic flow prediction with big data: a deep learning approach. IEEE Transactions on Intelligent Transportation Systems, 16(2):865–873, 2015.
[44] Jun Ma, Xiao-dong Li, and Ying Meng. Research of urban traffic flow forecasting based on neural network. Acta Electronica Sinica, 37(5):1092–1094, 2009.
[45] Xiaolei Ma, Zhimin Tao, Yinhai Wang, Haiyang Yu, and Yunpeng Wang. Long short- term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C: Emerging Technologies, 54:187–197, 2015.
[46] Ahmad Munawar and Heri Sutanta. Implementation of intelligent transport systems in indonesian cities. Technical report, 2015.
[47] Kai Nagel and Maya Paczuski. Emergent traffic jams. Physical Review E, 51(4):2909, 1995.
[48] Bob Nau. Introduction to arima : nonseasonal models. https://people.duke.edu/~rnau/411arim.htm.
[49] Phillip E Pfeifer and Stuart Jay Deutsch. A starima model-building procedure with application to description and regional forecasting. Transactions of the Institute of British Geographers, pages 330–349, 1980.
[50] Lutz Prechelt. Early stopping-but when? In Neural Networks: Tricks of the trade, pages 55–69. Springer, 1998.
[51] Yeping Lina Qiu, Hong Zheng, and Olivier Gevaert. A deep learning framework for imputing missing values in genomic data. bioRxiv, page 406066, 2018.
[52] 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.
[53] Arthur G Sims. The sydney coordinated adaptive traffic system. In Engineering Foundation Conference on Research Directions in Computer Control of Urban Traffic Systems, 1979, Pacific Grove, California, USA, 1979.
[54] Mike Spack. Level of service (los) definition. http://www.mikeontraffic.com/ mr-traffic-engi/.
[55] Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1):1929–1958, 2014.
[56] Caltrans State of California. Peformance measurement system (pems) data source. http://www.dot.ca.gov/trafficops/mpr/source.html.
[57] Anthony Stathopoulos and M Karlaftis. Temporal and spatial variations of real- time traffic data in urban areas. Transportation Research Record: Journal of the Transportation Research Board, (1768):135–140, 2001.
[58] Anthony Stathopoulos and Matthew G Karlaftis. A multivariate state space approach for urban traffic flow modeling and prediction. Transportation Research Part C: Emerging Technologies, 11(2):121–135, 2003.
[59] Yongxue Tian and Li Pan. Predicting short-term traffic flow by long short-term memory recurrent neural network. In 2015 IEEE international conference on smart city/SocialCom/SustainCom (SmartCity), pages 153–158. IEEE, 2015.
[60] Mascha Van Der Voort, Mark Dougherty, and Susan Watson. Combining kohonen maps with arima time series models to forecast traffic flow. Transportation Research Part C: Emerging Technologies, 4(5):307–318, 1996.
[61] J Van Lint, S Hoogendoorn, and H Van Zuylen. 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, (1811):30–39, 2002.
[62] Joachim Wahle, Ana Lu´cia C Bazzan, Franziska Klu¨gl, and Michael Schreckenberg. Decision dynamics in a traffic scenario. Physica A: Statistical Mechanics and its Applications, 287(3-4):669–681, 2000.
[63] JS Wang, BC Gao, and LP Shi. Application of support vector machines in traffic volume forecast. Information Technology, 4:8–10, 2004.
[64] Joe Whittaker, Simon Garside, and Karel Lindveld. Tracking and predicting a net- work traffic process. International Journal of Forecasting, 13(1):51–61, 1997.
[65] Billy Williams. Multivariate vehicular traffic flow prediction: evaluation of arimax modeling. Transportation Research Record: Journal of the Transportation Research Board, (1776):194–200, 2001.
[66] Billy M Williams, Priya K Durvasula, and Donald E Brown. Urban freeway traffic flow prediction: application of seasonal autoregressive integrated moving average and exponential smoothing models. Transportation Research Record, 1644(1):132–141, 1998.
[67] Billy M Williams and Lester A Hoel. Modeling and forecasting vehicular traffic flow as a seasonal stochastic time series process. Technical report, 1999.
[68] QH Xu and Rui Yang. Traffic flow prediction using support vector machine based method. Journal of Highway and Transportation Research and Development, 22(12):131–134, 2005.
[69] Hao-Fan Yang, Tharam S Dillon, and Yi-Ping Phoebe Chen. Optimized structure of the traffic flow forecasting model with a deep learning approach. IEEE transactions on neural networks and learning systems, 28(10):2371–2381, 2017.
[70] Hongsuk Yi, HeeJin Jung, and Sanghoon Bae. Deep neural networks for traffic flow prediction. In 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), pages 328–331. IEEE, 2017.
[71] Chen-Lin Zhang, Jian-Hao Luo, Xiu-Shen Wei, and Jianxin Wu. In defense of fully connected layers in visual representation transfer. In Pacific Rim Conference on Multimedia, pages 807–817. Springer, 2017.
[72] HM Zhang. Recursive prediction of traffic conditions with neural network models. Journal of Transportation Engineering, 126(6):472–481, 2000.
[73] Lun Zhang, Qiuchen Liu, Wenchen Yang, Nai Wei, and Decun Dong. An improved k-nearest neighbor model for short-term traffic flow prediction. Procedia-Social and Behavioral Sciences, 96:653–662, 2013.
[74] Xiao-li ZHANG, Guo-guang HE, and Hua-pu LU. Short-term traffic flow forecasting based on k-nearest neighbors non-parametric regression. Journal of Systems Engi- neering, 2:009, 2009.

指導教授

孫敏德(Min-Te Sun)

審核日期

2019-7-25

推文