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GPS-based citywide traffic congestion forecasting using CNN-RNN and C3D hybrid model

Traffic congestion prediction in citywide road networks is a challenging research field in metropolitan transportation operation and management. Recent advances in GPS technology offer great opportunities to improve upon the limitations on the availability and quality of traffic data. Motivated by the success of deep neural networks and considering the spatial dependencies and temporal evolutions of network traffic, we propose an innovative deep learning-based mapping to cube architecture for network-wide urban traffic forecasting. Experiments using real Taxi GPS vehicle trajectory data confirm the accuracy and effectiveness of the proposed approach combining 3-Dimensional Convolutional Networks (C3D) with Convolutional Neuron Networks (CNNs) and Recurrent Neuron Networks (RNNs), called CRC3D as a hybrid method integrating CNN-RNNs and C3Ds. We also compared a variety of recurrent neural network architectures. Results show that CRC3D succeeds in inheriting the advantages of C3D and CNN-RNN, and show its consistent and satisfactory results in urban complex system.