Summary
Smart Info-Media Systems in Asia
2019
Session Number:SS2
Session:
Number:SS2-2
Short-term Traffic Flow Prediction Based on Recurrent Neural Network
Bohan Huang, Hongye Yang, Yun Bai,
pp.29-33
Publication Date:2019/9/4
Online ISSN:2188-5079
Summary:
With the increasing traffic congestion problem, the development of intelligent transportation systems has become an important measure for countries to solve traffic problems. As an important part of the intelligent transportation system, short-term traffic flow prediction plays an important role in pedestrian traffic control and traffic control and traffic guidance. The three algorithms of LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit) and BRNN (Bi-directional RNN) in the recurrent neural network are selected for prediction and compared with real data. The results show that the recurrent neural network is a feasible traffic flow prediction model.