Traffic flow prediction over muti-sensor data correlation with graph convolution network

Abstract Accurate and real-time traffic flow prediction plays an important role in improving the traffic planning capability of intelligent traffic systems. However, traffic flow prediction is a very challenging problem because the spatial-temporal correlation among roads is complex and changeable. Most of the existing methods do not reasonably analyze the dynamic spatial-temporal correlation caused by the changing relationship of traffic patterns among roads, thus cannot get satisfactory results in the medium and long-term traffic prediction. To address these issues, a novel M ultisensor D ata C orrelation G raph C onvolution N etwork model, named MDCGCN, is proposed in this paper. The MDCGCN model consists of three parts: recent, daily period and weekly period components, and each of which consists of two parts: 1) benchmark adaptive mechanism and 2) multisensor data correlation convolution block. The first part can eliminate the differences among the periodic data and effectively improve the quality of data input. The second part can effectively capture the dynamic temporal and spatial correlation caused by the changing relationship of traffic patterns among roads. Through substantial experiments conducted on two real data sets, results indicate that the proposed MDCGCN model can significantly improve the medium and long-term prediction accuracy for traffic networks of different sizes, and is superior to existing prediction methods.

[1]  Jie Cui,et al.  PA-CRT: Chinese Remainder Theorem Based Conditional Privacy-Preserving Authentication Scheme in Vehicular Ad-Hoc Networks , 2019, IEEE Transactions on Dependable and Secure Computing.

[2]  Guilherme De A. Barreto,et al.  Long-term time series prediction with the NARX network: An empirical evaluation , 2008, Neurocomputing.

[3]  Zibin Zheng,et al.  Covering-Based Web Service Quality Prediction via Neighborhood-Aware Matrix Factorization , 2019, IEEE Transactions on Services Computing.

[4]  Biao Leng,et al.  A novel wavelet-SVM short-time passenger flow prediction in Beijing subway system , 2015, Neurocomputing.

[5]  Nikos Komodakis,et al.  Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Md Zakirul Alam Bhuiyan,et al.  Joint Optimization of Offloading Utility and Privacy for Edge Computing Enabled IoT , 2020, IEEE Internet of Things Journal.

[7]  Janez Demsar,et al.  Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..

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

[9]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[10]  Ridha Soua,et al.  Improving Traffic Flow Prediction With Weather Information in Connected Cars: A Deep Learning Approach , 2016, IEEE Transactions on Vehicular Technology.

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

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

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

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

[15]  Min-Liang Huang,et al.  Intersection traffic flow forecasting based on ν-GSVR with a new hybrid evolutionary algorithm , 2015, Neurocomputing.

[16]  Hashem R Al-Masaeid,et al.  Short-Term Prediction of Traffic Volume in Urban Arterials , 1995 .

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

[18]  Kim-Kwang Raymond Choo,et al.  Applications of computational intelligence in vehicle traffic congestion problem: a survey , 2017, Soft Computing.

[19]  Zibin Zheng,et al.  When UAV Swarm Meets Edge-Cloud Computing: The QoS Perspective , 2019, IEEE Network.

[20]  Yu Liu,et al.  T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction , 2018, IEEE Transactions on Intelligent Transportation Systems.

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

[22]  Yan Tian,et al.  LSTM-based traffic flow prediction with missing data , 2018, Neurocomputing.

[23]  Yan Tian,et al.  Traffic flow prediction using LSTM with feature enhancement , 2019, Neurocomputing.

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

[25]  Qiang He,et al.  Efficient Query of Quality Correlation for Service Composition , 2018, IEEE Transactions on Services Computing.

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

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

[28]  Zhihui Lu,et al.  Recurrent Tensor Factorization for time-aware service recommendation , 2019, Appl. Soft Comput..

[29]  Xuyun Zhang,et al.  A QoS-aware virtual machine scheduling method for energy conservation in cloud-based cyber-physical systems , 2019, World Wide Web.

[30]  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.

[31]  Haibin Zhu,et al.  Location-Aware Deep Collaborative Filtering for Service Recommendation , 2021, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[32]  Pascal Frossard,et al.  The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains , 2012, IEEE Signal Processing Magazine.

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

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

[35]  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.

[36]  Shane G. Henderson,et al.  Travel time estimation for ambulances using Bayesian data augmentation , 2013, 1312.1873.

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

[38]  Fei Dai,et al.  Trust-Oriented IoT Service Placement for Smart Cities in Edge Computing , 2020, IEEE Internet of Things Journal.

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

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