Spatiotemporal Traffic Flow Prediction with KNN and LSTM

The traffic flow prediction is becoming increasingly crucial in Intelligent Transportation Systems. Accurate prediction result is the precondition of traffic guidance, management, and control. To improve the prediction accuracy, a spatiotemporal traffic flow prediction method is proposed combined with k-nearest neighbor (KNN) and long short-term memory network (LSTM), which is called KNN-LSTM model in this paper. KNN is used to select mostly related neighboring stations with the test station and capture spatial features of traffic flow. LSTM is utilized to mine temporal variability of traffic flow, and a two-layer LSTM network is applied to predict traffic flow respectively in selected stations. The final prediction results are obtained by result-level fusion with rank-exponent weighting method. The prediction performance is evaluated with real-time traffic flow data provided by the Transportation Research Data Lab (TDRL) at the University of Minnesota Duluth (UMD) Data Center. Experimental results indicate that the proposed model can achieve a better performance compared with well-known prediction models including autoregressive integrated moving average (ARIMA), support vector regression (SVR), wavelet neural network (WNN), deep belief networks combined with support vector regression (DBN-SVR), and LSTM models, and the proposed model can achieve on average 12.59% accuracy improvement.

[1]  Kaiyun Wang,et al.  Vibration Response Characteristics of the Cross Tunnel Structure , 2016 .

[2]  Fei-Yue Wang,et al.  Performance evaluation of the deep learning approach for traffic flow prediction at different times , 2016, 2016 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI).

[3]  Guoqiang Han,et al.  δ-agree AdaBoost stacked autoencoder for short-term traffic flow forecasting , 2017, Neurocomputing.

[4]  Hao Sun,et al.  Traffic Structure Optimization in Historic Districts Based on Green Transportation and Sustainable Development Concept , 2019, Advances in Civil Engineering.

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

[6]  S. Suhas,et al.  A Comprehensive Review on Traffic Prediction for Intelligent Transport System , 2017, 2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT).

[7]  Alessandro De Gloria,et al.  Time-Aware Multivariate Nearest Neighbor Regression Methods for Traffic Flow Prediction , 2015, IEEE Transactions on Intelligent Transportation Systems.

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

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

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

[11]  Boon-Hee Soong,et al.  Traffic flow prediction with Long Short-Term Memory Networks (LSTMs) , 2016, 2016 IEEE Region 10 Conference (TENCON).

[12]  X. Weng,et al.  Modeling of Loess Soaking Induced Impacts on a Metro Tunnel Using a Water Soaking System in Centrifuge , 2019, Geofluids.

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

[14]  Se-do Oh,et al.  Urban Traffic Flow Prediction System Using a Multifactor Pattern Recognition Model , 2015, IEEE Transactions on Intelligent Transportation Systems.

[15]  Guan Huang,et al.  traffic flow prediction model based on deep belief network and genetic algorithm , 2018 .

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

[17]  Marcin Bernaś,et al.  Segmentation of vehicle detector data for improved k-nearest neighbours-based traffic flow prediction , 2015 .

[18]  G. Xu,et al.  Dynamic Failure Mode and Dynamic Response of High Slope Using Shaking Table Test , 2019, Shock and Vibration.

[19]  Yongli Xie,et al.  A New Soil-Water Characteristic Curve Model for Unsaturated Loess Based on Wetting-Induced Pore Deformation , 2019, Geofluids.

[20]  Jianhua Guo,et al.  Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification , 2014 .

[21]  Eleni I. Vlahogianni,et al.  Road Traffic Forecasting: Recent Advances and New Challenges , 2018, IEEE Intelligent Transportation Systems Magazine.

[22]  Eleni I. Vlahogianni,et al.  Short-term traffic forecasting: Where we are and where we’re going , 2014 .

[23]  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.

[24]  Danyang Li,et al.  Traffic Flow Prediction during the Holidays Based on DFT and SVR , 2019, J. Sensors.

[25]  Reinhard Klette,et al.  Accurate and Interpretable Bayesian MARS for Traffic Flow Prediction , 2014, IEEE Transactions on Intelligent Transportation Systems.

[26]  Jiangtao Lei,et al.  Optimization Analysis of Settlement Parameters for Postgrouting Piles in Loess Area of Shaanxi, China , 2019, Advances in Civil Engineering.

[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]  Xianglong Luo,et al.  An Algorithm for Traffic Flow Prediction Based on Improved SARIMA and GA , 2018, KSCE Journal of Civil Engineering.

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

[30]  Yunpeng Wang,et al.  A spatiotemporal correlative k-nearest neighbor model for short-term traffic multistep forecasting , 2016 .

[31]  Qian Zhang,et al.  Fiber Bragg Grating Sensors-Based In Situ Monitoring and Safety Assessment of Loess Tunnel , 2016, J. Sensors.

[32]  Arief B. Koesdwiady,et al.  Big-data-generated traffic flow prediction using deep learning and dempster-shafer theory , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

[33]  Carlos Canudas de Wit,et al.  Adaptive Kalman filtering for multi-step ahead traffic flow prediction , 2013, 2013 American Control Conference.

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

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

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

[37]  Dawei Chen,et al.  Research on Traffic Flow Prediction in the Big Data Environment Based on the Improved RBF Neural Network , 2017, IEEE Transactions on Industrial Informatics.

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

[39]  Bin Yu,et al.  Improved k-nn for Short-Term Traffic Forecasting Using Temporal and Spatial Information , 2014 .

[40]  Tharam S. Dillon,et al.  Optimized Structure of the Traffic Flow Forecasting Model With a Deep Learning Approach , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[41]  Michael J Demetsky,et al.  SHORT-TERM TRAFFIC FLOW PREDICTION: NEURAL NETWORK APPROACH , 1994 .

[42]  Wei Cheng,et al.  Short-Term Traffic Forecasting Using Self-Adjusting k-Nearest Neighbours , 2017 .

[43]  Wei Huang,et al.  Modeling Seasonal Heteroscedasticity in Vehicular Traffic Condition Series Using a Seasonal Adjustment Approach , 2014 .

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

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