Traffic Flow Forecast of Road Networks With Recurrent Neural Networks

The interest in developing smart cities has increased dramatically in recent years. In this context an intelligent transportation system depicts a major topic. The forecast of traffic flow is indispensable for an efficient intelligent transportation system. The traffic flow forecast is a difficult task, due to its stochastic and non linear nature. Besides classical statistical methods, neural networks are a promising possibility to predict future traffic flow. In our work, this prediction is performed with various recurrent neural networks. These are trained on measurements of induction loops, which are placed in intersections of the city. We utilized data from beginning of January to the end of July in 2018. Each model incorporates sequences of the measured traffic flow from all sensors and predicts the future traffic flow for each sensor simultaneously. A variety of model architectures, forecast horizons and input data were investigated. Most often the vector output model with gated recurrent units achieved the smallest error on the test set over all considered prediction scenarios. Due to the small amount of data, generalization of the trained models is limited.

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

[2]  Huadong Ma,et al.  An AutoEncoder and LSTM-Based Traffic Flow Prediction Method , 2019, Sensors.

[3]  Li Li,et al.  Deep Bi-directional Long Short-Term Memory Model for Short-Term Traffic Flow Prediction , 2017, ICONIP.

[4]  Klaus-Robert Müller,et al.  Efficient BackProp , 2012, Neural Networks: Tricks of the Trade.

[5]  Hao Peng,et al.  Forecasting Traffic Flow: Short Term, Long Term, and When It Rains , 2018, BigData Congress.

[6]  Mansur R. Kabuka,et al.  Combining Weather Condition Data to Predict Traffic Flow: A GRU Based Deep Learning Approach , 2017, 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech).

[7]  Fei-Yue Wang,et al.  DeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Prediction , 2017, ArXiv.

[8]  Paolo Frasconi,et al.  Short-Term Traffic Flow Forecasting: An Experimental Comparison of Time-Series Analysis and Supervised Learning , 2013, IEEE Transactions on Intelligent Transportation Systems.

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

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

[11]  Bin Yang,et al.  Correlated Time Series Forecasting using Deep Neural Networks: A Summary of Results , 2018, ArXiv.

[12]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[13]  Diego Klabjan,et al.  Dynamic Prediction Length for Time Series with Sequence to Sequence Networks , 2018, ArXiv.

[14]  Yoshua Bengio,et al.  The problem of learning long-term dependencies in recurrent networks , 1993, IEEE International Conference on Neural Networks.

[15]  Fei-Yue Wang,et al.  Traffic Flow Prediction with Parallel Data , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[16]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[17]  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).

[18]  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).