Accurate short-term traffic flow forecasting facilitates active traffic control and trip planning. Most existing traffic flow models fail to make full use of the temporal and spatial features of traffic data. This study proposes a short-term traffic flow prediction model based on a convolution neural network (CNN) deep learning framework. In the proposed framework, the optimal input data time lags and amounts of spatial data are determined by a spatio-temporal feature selection algorithm (STFSA), and selected spatio-temporal traffic flow features are extracted from actual data and converted into a two-dimensional matrix. The CNN then learns these features to construct a predictive model. The effectiveness of the proposed method is evaluated by comparing the forecast results with actual traffic data. Other existing models are also evaluated for comparison. The proposed method outperforms baseline models in terms of accuracy.
Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning
Transportmetrica A: Transport Science ; 15 , 2 ; 1688-1711
2019-11-29
24 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Short-term traffic flow prediction method based on spatio-temporal correlation
Europäisches Patentamt | 2020
|Deep learning for short-term traffic flow prediction
Elsevier | 2017
|Deep Learning for Short-Term Traffic Flow Prediction
Online Contents | 2016
|Short-term traffic flow prediction using deep learning
SLUB | 2023
|