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.

中文翻译：

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基于 GPS 的城市交通拥堵预测，使用 CNN-RNN 和 C3D 混合模型

城市道路网交通拥堵预测是城市交通运营管理中的一个具有挑战性的研究领域。GPS 技术的最新进展为改善交通数据的可用性和质量的局限性提供了很好的机会。受深度神经网络成功的启发，并考虑到网络交通的空间依赖性和时间演变，我们提出了一种创新的基于深度学习的映射到立方体架构的网络范围城市交通预测。使用真实出租车 GPS 车辆轨迹数据的实验证实了所提出的将 3 维卷积网络 (C3D) 与卷积神经元网络 (CNN) 和循环神经元网络 (RNN) 相结合的方法的准确性和有效性，称为 CRC3D 作为集成 CNN- RNN 和 C3D。我们还比较了各种循环神经网络架构。结果表明CRC3D成功继承了C3D和CNN-RNN的优点，在城市综合体系统中表现出一致且令人满意的效果。