A new method for short-term traffic congestion forecasting based on LSTM

2018 International Joint Conference on Materials Science and Mechanical Engineering

Zhong, Ying^1 ; Xie, Xin^2 ; Guo, Jingjing^3 ; Wang, Qing^4 ; Ge, Songlin^2

College of Information Science and Engineering, Hunan University, China^1

School of Information Engineering, East China Jiaotong University, China^2

College of Electronic Information, Wuhan University, China^3

Central South University Railway Campus, Central South University, China^4

关键词: Congestion forecasting;    Hybrid neural networks;    Metropolitan area;    Short term;    Statistical datas;    Training sample;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/383/1/012043/pdf
DOI  :  10.1088/1757-899X/383/1/012043

来源: IOP

【 摘 要 】

Traffic congestion in metropolitan areas such as shenzhen, has become more and more serious. Over the past decades, many academic and industrial efforts have been made to alleviate this issue. In this paper, we propose a novel approach to predicting short-term traffic congestion. At first, we pre-process the data to get the speed, traffic, lane number of these parameters. Second, we carry out statistical data and create training samples. Third, We establish a hybrid neural network prediction model based on LSTM and substitute the generated samples into training. Finally, we use the model to predict the future congestion situation. The experimental results show that our model achieves good predictive results.