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SHORT-TERM TRAFFIC VOLUME PREDICTION BY NEURAL NETWORK CONSIDERING CONNECTIVITY TYPES BETWEEN ROAD LINKS

Detailed observation data on road traffic conditions enhance the possibility of accurate short-term traffic volume prediction. Deep learning is one method of predicting the short-term traffic volume of road sections on highway networks, and it provides high prediction accuracy. We propose a new convolutional neural network model that predicts the traffic volume of road links on urban networks, whose structure is more complex than that of highway networks. The proposed model is designed to consider the connectivity types of road links on urban networks through a convolution operation. The proposed model consists of three types of layers. A set of convolution layers extracts the local features of road networks through the convolution operation based on the connectivity types of road links. A pooling layer summarizes outputs of the convolution layers. A fully-connected layer extracts the global features of traffic conditions on a target urban network. We conducted an experiment to predict the traffic volume of road links on an arterial road network in Tokyo. The results indicated that the proposed model provides almost the same prediction ac-curacy as that of the fully-connected neural network model, even though the proposed model contains fewer parameters compared to the fully-connected neural network model.