Abstract
Abstract
Accurate traffic flow forecasts provide an important data basis for traffic management departments. This paper proposes a traffic flow prediction model based on deep learning, which combines Convolutional Neural Network (CNN), Long Short-term Memory (LSTM) and Support Vector Regression (SVR) features: use CNN neural network to mine the spatial characteristics of traffic flow, and then input the time series features captured by LSTM neural network into the SVR model for traffic prediction. The actual traffic flow data of intersections in Mianyang City are selected to verify the CNN-LSTM-SVR hybrid model, and compare it with the CNN model, LSTM model, and SVR model. The results show that the proposed prediction model has higher prediction accuracy.
Subject
General Physics and Astronomy
Reference9 articles.
1. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data;Ma;Transportation Research Part C Emerging Technologies,2015
2. Traffic flowprediction based on deep leaming;Liu;Joumal of System Simulation,2018
3. Holiday Highway Traffic Flow Prediction Method Based on Deep Learning;Ji;Joumal of System Simulation,2020
4. Deep architecturefor traffic flow prediction: deep belief networks with multitask learning;Huang;IEEE Trans on Intelligent Transportation Systems,2014
5. Time series forecasting using a deep belief network with restricted Boltzmann machines;Kuremoto;Neurocomputing,2014
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献