Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning

Abstract

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.

KEYWORDS:

• Short-term traffic prediction
• deep learning
• convolution neural network
• spatio-temporal model
• intelligent transportation

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research is sponsored by National key research and development program: Key projects of international scientific and technological innovation cooperation between governments [grant number 2016YFE0108000].