Two-Stream Multi-Channel Convolutional Neural Network for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact

Traffic speed prediction is a critically important component of intelligent transportation systems. Recently, with the rapid development of deep learning and transportation data science, a growing body of new traffic speed prediction models have been designed that achieved high accuracy and large-scale prediction. However, existing studies have two major limitations. First, they predict aggregated traffic speed rather than lane-level traffic speed; second, most studies ignore the impact of other traffic flow parameters in speed prediction. To address these issues, the authors propose a two-stream multi-channel convolutional neural network (TM-CNN) model for multi-lane traffic speed prediction considering traffic volume impact. In this model, the authors first introduce a new data conversion method that converts raw traffic speed data and volume data into spatial–temporal multi-channel matrices. Then the authors carefully design a two-stream deep neural network to effectively learn the features and correlations between individual lanes, in the spatial–temporal dimensions, and between speed and volume. Accordingly, a new loss function that considers the volume impact in speed prediction is developed. A case study using 1-year data validates the TM-CNN model and demonstrates its superiority. This paper contributes to two research areas: (1) traffic speed prediction, and (2) multi-lane traffic flow study.

[1]  Tomoki Taniguchi,et al.  Multilane first-order traffic flow model with endogenous representation of lane-flow equilibrium , 2015 .

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

[3]  Xianfeng Tang,et al.  Modeling Spatial-Temporal Dynamics for Traffic Prediction , 2018, ArXiv.

[4]  Lelitha Vanajakshi,et al.  Short-term traffic flow prediction using seasonal ARIMA model with limited input data , 2015, European Transport Research Review.

[5]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[6]  Meng Li,et al.  Multistep Speed Prediction on Traffic Networks: A Graph Convolutional Sequence-to-Sequence Learning Approach with Attention Mechanism , 2018, ArXiv.

[7]  Zhiyong Cui,et al.  Real-Time Bidirectional Traffic Flow Parameter Estimation From Aerial Videos , 2017, IEEE Transactions on Intelligent Transportation Systems.

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

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

[10]  Huafeng Wu,et al.  Robust Ship Tracking via Multi-view Learning and Sparse Representation , 2018, Journal of Navigation.

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

[12]  Jianping Wu,et al.  Traffic speed prediction using deep learning method , 2016, 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC).

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

[14]  Dong Yu,et al.  Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..

[15]  Ruimin Ke,et al.  Innovative method for traffic data imputation based on convolutional neural network , 2018, IET Intelligent Transport Systems.

[16]  Soyoung Ahn,et al.  Freeway Traffic Oscillations and Vehicle Lane-Change Maneuvers , 2007 .

[17]  Xiaolei Ma,et al.  Forecasting Transportation Network Speed Using Deep Capsule Networks With Nested LSTM Models , 2018, IEEE Transactions on Intelligent Transportation Systems.

[18]  Ziyuan Pu,et al.  New Framework for Automatic Identification and Quantification of Freeway Bottlenecks Based on Wavelet Analysis , 2018, Journal of Transportation Engineering, Part A: Systems.

[19]  Zhiyong Cui,et al.  Digital roadway interactive visualization and evaluation network applications to WSDOT operational data usage. , 2016 .

[20]  Yajie Zou,et al.  Flexible and Robust Method for Missing Loop Detector Data Imputation , 2015 .

[21]  Carlos F. Daganzo,et al.  A BEHAVIORAL THEORY OF MULTI-LANE TRAFFIC FLOW. PART II, MERGES AND THE ONSET OF CONGESTION , 1999 .

[22]  Susan Grant-Muller,et al.  Use of sequential learning for short-term traffic flow forecasting , 2001 .

[23]  Fang Liu,et al.  An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic , 2017, IEEE Transactions on Intelligent Transportation Systems.

[24]  Haitham Al-Deek,et al.  Predictions of Freeway Traffic Speeds and Volumes Using Vector Autoregressive Models , 2009, J. Intell. Transp. Syst..

[25]  Jinjun Tang,et al.  Real-Time Traffic Flow Parameter Estimation From UAV Video Based on Ensemble Classifier and Optical Flow , 2019, IEEE Transactions on Intelligent Transportation Systems.

[26]  Mark Dougherty,et al.  SHORT TERM INTER-URBAN TRAFFIC FORECASTS USING NEURAL NETWORKS , 1997 .

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

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

[29]  Billy M. Williams,et al.  Comparison of parametric and nonparametric models for traffic flow forecasting , 2002 .

[30]  L. Vanajakshi,et al.  A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[31]  Jiawei Wang,et al.  Traffic speed prediction for urban transportation network: A path based deep learning approach , 2019, Transportation Research Part C: Emerging Technologies.

[32]  Yinhai Wang,et al.  Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting , 2018, IEEE Transactions on Intelligent Transportation Systems.

[33]  Zhiyong Cui,et al.  High-Order Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting , 2018, ArXiv.

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

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

[36]  Axel Klar,et al.  A Hierarchy of Models for Multilane Vehicular Traffic I: Modeling , 1998, SIAM J. Appl. Math..

[37]  Yinhai Wang,et al.  A Cost-Effective Framework for Automated Vehicle-Pedestrian Near-Miss Detection Through Onboard Monocular Vision , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

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

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

[40]  Lelitha Vanajakshi,et al.  Short Term Prediction of Traffic Parameters Using Support Vector Machines Technique , 2010, 2010 3rd International Conference on Emerging Trends in Engineering and Technology.

[41]  Li Li,et al.  Traffic flow data compression considering burst components , 2017 .

[42]  V Shvetsov,et al.  Macroscopic dynamics of multilane traffic. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

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

[44]  Won-Sik Yoon,et al.  Traffic Speed Prediction Under Weekday, Time, and Neighboring Links' Speed: Back Propagation Neural Network Approach , 2007, ICIC.

[45]  Kai Nagel,et al.  Realistic multi-lane traffic rules for cellular automata , 1997 .

[46]  C R Bennett,et al.  A SPEED PREDICTION MODEL FOR RURAL TWO-LANE HIGHWAYS , 1994 .

[47]  Zhiyong Cui,et al.  Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction , 2018, ArXiv.