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

Weibin Zhang · Yong Feng Qi · Feng Shu · Yinhai Wang

385 citations0/0 files storedTransportmetrica A Transport Science

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Traffic Prediction and Management Techniques Traffic control and management Air Quality Monitoring and Forecasting

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.

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References

2014Traffic Flow Prediction With Big Data: A Deep Learning Approach 2015Long short-term memory neural network for traffic speed prediction using remote microwave sensor data 2003Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results 2017Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction 2014Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning 1984Dynamic prediction of traffic volume through Kalman filtering theory 2017Deep learning for short-term traffic flow prediction 2015Short-term traffic flow prediction using seasonal ARIMA model with limited input data

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2021A Review of Traffic Congestion Prediction Using Artificial Intelligence

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