Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

[1]  Ivan Laptev,et al.  Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Der-Horng Lee,et al.  Short-term freeway traffic flow prediction : Bayesian combined neural network approach , 2006 .

[3]  Yunpeng Wang,et al.  Large-Scale Transportation Network Congestion Evolution Prediction Using Deep Learning Theory , 2015, PloS one.

[4]  Ulrich Weidmann,et al.  Assessing the feasibility of transport Megaprojects; ; Transportation research record : journal of the Transportation Research Board; , 2007 .

[5]  Fei-Fei Li,et al.  Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Dijiang Huang,et al.  Short term traffic flow prediction based on on-line sequential extreme learning machine , 2016, 2016 Eighth International Conference on Advanced Computational Intelligence (ICACI).

[7]  Yunpeng Wang,et al.  Long short-term memory neural network for traffic speed prediction using remote microwave sensor data , 2015 .

[8]  Bin Ran,et al.  Short‐term highway traffic flow prediction based on a hybrid strategy considering temporal–spatial information , 2016 .

[9]  Hengchao Li,et al.  A Multiplicative Seasonal ARIMA/GARCH Model in EVN Traffic Prediction , 2015 .

[10]  Billy M. Williams,et al.  Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results , 2003, Journal of Transportation Engineering.

[11]  Ah Chung Tsoi,et al.  Face recognition: a convolutional neural-network approach , 1997, IEEE Trans. Neural Networks.

[12]  Dirk Helbing,et al.  Empirical Features of Congested Traffic States and Their Implications for Traffic Modeling , 2007, Transp. Sci..

[13]  Nicholas G. Polson,et al.  Deep learning for short-term traffic flow prediction , 2016, 1604.04527.

[14]  Gary A. Davis,et al.  Nonparametric Regression and Short‐Term Freeway Traffic Forecasting , 1991 .

[15]  Fei-Yue Wang,et al.  Traffic Flow Prediction With Big Data: A Deep Learning Approach , 2015, IEEE Transactions on Intelligent Transportation Systems.

[16]  Bo Peng,et al.  Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network , 2016 .

[17]  Fei-Yue Wang,et al.  Data-Driven Intelligent Transportation Systems: A Survey , 2011, IEEE Transactions on Intelligent Transportation Systems.

[18]  Eleni I. Vlahogianni,et al.  Statistical methods versus neural networks in transportation research: Differences, similarities and some insights , 2011 .

[19]  Billy M. Williams.  Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX Modeling , 2001 .

[20]  Warren S. Sarle,et al.  Stopped Training and Other Remedies for Overfitting , 1995 .

[21]  Hubert Rehborn,et al.  Recognition and tracking of spatial–temporal congested traffic patterns on freeways , 2004 .

[22]  Zili Zhang,et al.  A distributed spatial-temporal weighted model on MapReduce for short-term traffic flow forecasting , 2016, Neurocomputing.

[23]  Yisheng Lv,et al.  A deep learning based approach for traffic data imputation , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[24]  Ming Yang,et al.  3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Srinivas Peeta,et al.  Multiple measures-based chaotic time series for traffic flow prediction based on Bayesian theory , 2016, Nonlinear Dynamics.

[26]  Bin Ran,et al.  AN APPLICATION OF NEURAL NETWORK ON TRAFFIC SPEED PREDICTION UNDER ADVERSE WEATHER CONDITION , 2003 .

[27]  Lee D. Han,et al.  Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions , 2009, Expert Syst. Appl..

[28]  Muhammad Tayyab Asif,et al.  Spatiotemporal Patterns in Large-Scale Traffic Speed Prediction , 2014, IEEE Transactions on Intelligent Transportation Systems.

[29]  Mascha C. van der Voort,et al.  Combining kohonen maps with arima time series models to forecast traffic flow , 1996 .

[30]  Wenhao Huang,et al.  Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning , 2014, IEEE Transactions on Intelligent Transportation Systems.

[31]  Stefano Panzieri,et al.  Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling , 2015, Neurocomputing.

[32]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[33]  Tao Cheng,et al.  Non-parametric regression for space-time forecasting under missing data , 2012, Comput. Environ. Urban Syst..

[34]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[35]  Stephen D. Clark,et al.  Traffic Prediction Using Multivariate Nonparametric Regression , 2003 .

[36]  Byoung-Jo Yoon,et al.  Dynamic near-term traffic flow prediction: system- oriented approach based on past experiences , 2012 .

[37]  Nicholas G. Polson,et al.  Deep Learning Predictors for Traffic Flows , 2016 .

[38]  Bin Ran,et al.  A Comparison of Traffic Flow Prediction Methods Based on DBN , 2016 .

[39]  D. T. Lee,et al.  Travel-time prediction with support vector regression , 2004, IEEE Transactions on Intelligent Transportation Systems.

[40]  Yi Lu Murphey,et al.  Real time vehicle speed prediction using a Neural Network Traffic Model , 2011, The 2011 International Joint Conference on Neural Networks.

[41]  Wei-Chiang Hong,et al.  Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm , 2011, Neurocomputing.

[42]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .