Journal Article

GLSNN Network: A Multi-Scale Spatiotemporal Prediction Model for Urban Traffic Flow

Cai, Benhe, Liu, Jiahao, Teng, Wenxin, Huang, Chong, Wang, Yanhui

2022

Overview

Traffic flow prediction is a key issue in intelligent transportation systems. The growing trend in data disclosure has created more potential sources for the input for predictive models, posing new challenges to the prediction of traffic flow in the era of big data. In this study, the prediction of urban traffic flow was regarded as a spatiotemporal prediction problem, focusing on the traffic speed. A Graph LSTM (Long Short-Term Memory) Spatiotemporal Neural Network (GLSNN) model was constructed to perform a multi-scale spatiotemporal fusion prediction based on the multi-source input data. The GLSNN model consists of three parts: MS-LSTM, LZ-GCN, and LSTM-GRU. We used the MS-LSTM module to scale the traffic timing data, and then used the LZ-GCN network and the LSTM-GRU network to capture both the time and space dependencies. The model was tested on a real traffic dataset, and the experiment results verified the superior performance of the GLSNN model on both a high-precision and multi-scale prediction of urban traffic flow.

• Publisher: MDPI AG,MDPI
• Subject: Accuracy / Algorithms / Artificial intelligence / Big Data / City traffic / Deep learning / GLSNN model / Intelligent vehicle-highway systems / Kalman filters / multi-scale / Neural networks / Neural Networks, Computer / Roads & highways / spatiotemporal data fusion / spatiotemporal dependence / Time series / Traffic flow / traffic flow prediction