Multi-Lane Short-Term Traffic Forecasting With Convolutional LSTM Network

Short-term traffic prediction consists a crucial component in intelligent transportation systems. With the explosion of automated traffic monitoring sensors and the flourishing of deep learning techniques, a growing body of deep neural network models have been employed to tackle this problem. In particular, convolutional neural networks (CNN) and long short-term memory (LSTM) recurrent networks have demonstrated their advantages in modeling and predicting the spatiotemporal evolution of traffic flows. In this paper, we propose a novel Convolutional LSTM neural network architecture for multi-lane short-term traffic prediction. Compared to existing methods, we highlight the importance of (1) applying multiple features to characterize traffic conditions; (2) explicitly considering the routing between neighbouring lanes and downstream/upstream traffics; and (3) predicting multiple time-step traffic in a rolling-prediction manner. Experiments on 10 months 5-minute interval observations of the US I-101 Northern freeway at California Bay Area verify the proposed model. The results show that our model has considerable advantages in predicting multi-lane short-term traffic flow.

[1]  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.

[2]  Panos G. Michalopoulos,et al.  Multilane traffic flow dynamics: Some macroscopic considerations , 1984 .

[3]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[4]  Qingchao Liu,et al.  Short‐Term Traffic Speed Forecasting Based on Attention Convolutional Neural Network for Arterials , 2018, Comput. Aided Civ. Infrastructure Eng..

[5]  Razvan Pascanu,et al.  On the difficulty of training recurrent neural networks , 2012, ICML.

[6]  D. L. Gerlough,et al.  Traffic flow theory : a monograph , 1975 .

[7]  Zijian Liu,et al.  Short-Term Prediction of Passenger Demand in Multi-Zone Level: Temporal Convolutional Neural Network With Multi-Task Learning , 2020, IEEE Transactions on Intelligent Transportation Systems.

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

[9]  Zijian Liu,et al.  A deep learning based multitask model for network-wide traffic speed prediction , 2020, Neurocomputing.

[10]  Moshe Levin,et al.  ON FORECASTING FREEWAY OCCUPANCIES AND VOLUMES (ABRIDGMENT) , 1980 .

[11]  Lei Lin,et al.  Predicting Station-level Hourly Demands in a Large-scale Bike-sharing Network: A Graph Convolutional Neural Network Approach , 2017, Transportation Research Part C: Emerging Technologies.

[12]  Yoshua Bengio,et al.  Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .

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

[14]  S. P. Hoogendoorn,et al.  Freeway Travel Time Prediction with State-Space Neural Networks: Modeling State-Space Dynamics with Recurrent Neural Networks , 2002 .

[15]  A. R. Cook,et al.  ANALYSIS OF FREEWAY TRAFFIC TIME-SERIES DATA BY USING BOX-JENKINS TECHNIQUES , 1979 .

[16]  Jason Weston,et al.  A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.

[17]  Carlos F. Daganzo,et al.  A BEHAVIORAL THEORY OF MULTI-LANE TRAFFIC FLOW. PART I, LONG HOMOGENEOUS FREEWAY SECTIONS , 1999 .

[18]  Bin Jia,et al.  A data-driven lane-changing model based on deep learning , 2019, Transportation Research Part C: Emerging Technologies.

[19]  Peter C. Y. Chen,et al.  LSTM network: a deep learning approach for short-term traffic forecast , 2017 .

[20]  Zhanxing Zhu,et al.  Spatio-temporal Graph Convolutional Neural Network: A Deep Learning Framework for Traffic Forecasting , 2017, IJCAI.

[21]  Gao Xuedong,et al.  Trend feature-based clustering for research funding time series data , 2015, 2015 International Conference on Logistics, Informatics and Service Sciences (LISS).

[22]  Kuolin Hsu,et al.  Precipitation Prediction Skill for the West Coast United States: From Short to Extended Range , 2018, Journal of Climate.

[23]  Yu Zheng,et al.  Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction , 2016, AAAI.

[24]  Li Li,et al.  Using LSTM and GRU neural network methods for traffic flow prediction , 2016, 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC).

[25]  Xuan Song,et al.  Learning Deep Representation from Big and Heterogeneous Data for Traffic Accident Inference , 2016, AAAI.

[26]  D. Zhang,et al.  High-speed Train Control System Big Data Analysis Based on the Fuzzy RDF model and Uncertain Reasoning , 2017, Int. J. Comput. Commun. Control.

[27]  Zijian Liu,et al.  A novel generative adversarial network for estimation of trip travel time distribution with trajectory data , 2019, Transportation Research Part C: Emerging Technologies.

[28]  Zhenji Zhang,et al.  Subsequence pattern discovery in Chinese university financial data , 2016, 2016 International Conference on Logistics, Informatics and Service Sciences (LISS).

[29]  Yunpeng Wang,et al.  Spatiotemporal Recurrent Convolutional Networks for Traffic Prediction in Transportation Networks , 2017, Sensors.

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

[31]  Zhirui Ye,et al.  Short-Term Traffic Flow Forecasting Using Fuzzy Logic System Methods , 2008, J. Intell. Transp. Syst..

[32]  Andrew Zisserman,et al.  Two-Stream Convolutional Networks for Action Recognition in Videos , 2014, NIPS.

[33]  Fei-Fei Li,et al.  Visualizing and Understanding Recurrent Networks , 2015, ArXiv.

[34]  J. W. C. van Lint,et al.  Short Term Traffic Prediction Models , 2007 .

[35]  H Lieu,et al.  TRAFFIC-FLOW THEORY , 1999 .

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

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

[38]  Wei Deng,et al.  New Bayesian combination method for short-term traffic flow forecasting , 2014 .

[39]  Bin Ran,et al.  Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm , 2019, Knowl. Based Syst..

[40]  Fei-Yue Wang,et al.  Long short-term memory model for traffic congestion prediction with online open data , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

[41]  Sherif Ishak,et al.  A Hidden Markov Model for short term prediction of traffic conditions on freeways , 2014 .

[42]  Selvaraj Vasantha Kumar,et al.  Traffic Flow Prediction using Kalman Filtering Technique , 2017 .

[43]  Andrew W. Senior,et al.  Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition , 2014, ArXiv.

[44]  Alexander T. Ihler,et al.  Estimating Warehouse Rental Price using Machine Learning Techniques , 2018, Int. J. Comput. Commun. Control.

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

[46]  Biswajit Basu,et al.  Random Process Model for Urban Traffic Flow Using a Wavelet‐Bayesian Hierarchical Technique , 2010, Comput. Aided Civ. Infrastructure Eng..

[47]  Kuolin Hsu,et al.  Improving Precipitation Estimation Using Convolutional Neural Network , 2019, Water Resources Research.

[48]  Bin Ran,et al.  A hybrid deep learning based traffic flow prediction method and its understanding , 2018 .

[49]  Kuolin Hsu,et al.  The Use of Convolutional Neural Network in Relating Precipitation to Circulation , 2017 .

[50]  Huachun Tan,et al.  Short-term traffic flow forecasting with spatial-temporal correlation in a hybrid deep learning framework , 2016, ArXiv.

[51]  Cícero Nogueira dos Santos,et al.  Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts , 2014, COLING.

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

[53]  Manoranjan Parida,et al.  Short Term Traffic Flow Prediction for a Non Urban Highway Using Artificial Neural Network , 2013 .

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

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

[56]  S. Sorooshian,et al.  Improving Monsoon Precipitation Prediction Using Combined Convolutional and Long Short Term Memory Neural Network , 2019, Water.

[57]  Li Pan,et al.  Predicting Short-Term Traffic Flow by Long Short-Term Memory Recurrent Neural Network , 2015, 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity).

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

[59]  Yong Wang,et al.  Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction , 2017, Sensors.

[60]  Yunzhan Gong,et al.  LARGE SCALE SOFTWARE TEST DATA GENERATION BASED ON COLLECTIVE CONSTRAINT AND WEIGHTED COMBINATION METHOD , 2017 .

[61]  Wan Li,et al.  Two-Stream Multi-Channel Convolutional Neural Network for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact , 2019, Transportation Research Record: Journal of the Transportation Research Board.

[62]  Zibin Zheng,et al.  Deep and Embedded Learning Approach for Traffic Flow Prediction in Urban Informatics , 2019, IEEE Transactions on Intelligent Transportation Systems.

[63]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

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