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Attention-Enabled Network-level Traffic Speed Prediction

机译：启用注意力的网络级流量速度预测

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摘要

Traffic forecasting is critical for the planning and monitoring of modern urban systems. Time-series and junior machine learning methods are either point-based and rely on unrealistic assumptions, or fail to capture the dynamics of the complex traffic network (e.g., non-Euclidean and spatiotemporal). New models need (1) to represent efficiently the spatial dependency of transportation network, and (2) to model nonlinear temporal dynamics simultaneously. They are also expected to forecast for multiple time steps, i.e., long-term. This study investigates a highway sensor network as a graph. Specifically, the level of road network details required for graph deep learning is first discussed. Secondly, this paper proposes a new graph deep learning model enabling attention mechanism to predict speeds in the network. It captures spatial dependencies with adjacency matrices and graph convolutions, and learns temporal information with a recurrent neural network (RNN) structure. Lastly, performance of the proposed model is compared with literature on a real-world dataset. Experiments show that physical roadway linkages are sufficient for the representation, and the proposed attention-enabled model performs better in the prediction task.
机译：交通预测对于现代城市系统的规划和监控至关重要。时间序列和初级机器学习方法要么是基于点的，并且依赖于不切实际的假设，要么无法捕获复杂交通网络（例如非欧几里德和时空）的动态。新模型需要（1）有效地表示运输网络的空间依赖性，以及（2）同时建模非线性时间动力学。还预期它们会预测多个时间步长，即长期时间。本研究以图形方式研究了高速公路传感器网络。具体来说，首先讨论图深度学习所需的道路网络详细程度。其次，本文提出了一种新的图深度学习模型，该模型使注意力机制能够预测网络的速度。它通过邻接矩阵和图卷积捕获空间依赖性，并通过递归神经网络（RNN）结构学习时间信息。最后，将所提出模型的性能与真实数据集上的文献进行比较。实验表明，物理巷道联系足以表示，并且所提出的注意力支持模型在预测任务中表现更好。

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