CNN-LSTM Based Traffic Prediction Using Spatial-temporal Features

Content Provider	Scilit
Author	Zhao, Zhen Li, Ze Li, Fuxin Liu, Yang
Copyright Year	2021
Description	Journal: Journal of Physics: Conference Series Aiming at the problem of traffic congestion prediction based on taxi big data, a CNN-LSTM based traffic prediction model using spatial-temporal trajectory topology is proposed. First, the trajectory information is abstracted into a trajectory topology map with spatial-temporal characteristics according to the time and space dimensions. The topology map solves the problem that the road network map does not have stationarity, and extracts a variety of road condition influence factors. Then, the spatial characteristics of the trajectory traffic flow are extracted by CNN, and the temporal characteristics of the trajectory traffic flow are extracted according to the memory characteristics of LSTM. The experimental results show that the RMSE, MAPE and Spearman correlation coefficients of the proposed method on JT-T809-2011 dataset have an absolute improvement of 1%~2% over state-of-the-art strategies.
Related Links	https://iopscience.iop.org/article/10.1088/1742-6596/2037/1/012065/pdf
ISSN	17426588
e-ISSN	17426596
DOI	10.1088/1742-6596/2037/1/012065
Journal	Journal of Physics: Conference Series
Issue Number	1
Volume Number	2037
Language	English
Publisher	IOP Publishing
Publisher Date	2021-09-01
Access Restriction	Open
Subject Keyword	Journal: Journal of Physics: Conference Series Spatial Temporal
Content Type	Text
Resource Type	Article
Subject	Physics and Astronomy