Research on Short-Term Flow Forecast of Intelligent Traffic

With the rapid development of China’s economy and the deepening of urbanization, the burden of urban traffic is becoming more and heavier. Scientific traffic planning and guidance has become an important research content of urban traffic management departments. The first day before the guidance of rational planning is the scientific and effective prediction of traffic flow. This chapter mainly studies and analyzes the wavelet neural network, and expounds the role of traffic flow prediction in intelligent transportation; the wavelet neural network model is studied in detail. At present, the wavelet neural network can forecast the urban traffic flow accurately and scientifically. I hope that the author’s research can give short-term traffic flow prediction researchers a reference.

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

[2]  Yao Wei-hon.  Traffic flow forecasting based on optimized SVR with hybrid artificial fish swarm algorithm , 2015 .

[3]  Weili Xiong,et al.  Human Action Recognition Based on Supervised Class-specific Dictionary Learning with Deep Convolutional Neural Network Features , 2020 .

[4]  Chao Zhang,et al.  Multiple sources and multiple measures based traffic flow prediction using the chaos theory and support vector regression method , 2017 .

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

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

[7]  Jinyoung Ahn,et al.  Predicting Spatiotemporal Traffic Flow Based on Support Vector Regression and Bayesian Classifier , 2015, 2015 IEEE Fifth International Conference on Big Data and Cloud Computing.

[8]  Jin Xin Cao,et al.  Traffic volume forecasting based on radial basis function neural network with the consideration of traffic flows at the adjacent intersections , 2014 .

[9]  Chun Mei Duan.  Design of Big Data Processing System Architecture Based on Hadoop under the Cloud Computing , 2014 .

[10]  Chan-Young Park,et al.  Predicting Concentration of PM10 using Optimal Parameters of Deep Neural Network , 2019, Intelligent Automation and Soft Computing.

[11]  Hui Chen,et al.  Urban traffic flow prediction: a spatio‐temporal variable selection‐based approach , 2016 .

[12]  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.

[13]  George K. Karagiannidis,et al.  Efficient Machine Learning for Big Data: A Review , 2015, Big Data Res..