论文标题
图形卷积网络，带有信号相相信息的动脉流量预测
A Graph Convolutional Network with Signal Phasing Information for Arterial Traffic Prediction
论文作者
论文摘要
准确可靠的交通测量结果在现代智能运输系统的发展中起着至关重要的作用。由于道路几何形状和信号控制的存在，动脉交通预测高于高速公路交通预测的水平。许多有关动脉交通预测的现有研究仅考虑循环传感器的流量和占用时间的时间测量，而忽略了上游检测器和下游检测器之间的丰富空间关系。结果，他们经常遭受较大的预测错误，尤其是对于长距离的。我们通过增强深度学习方法，扩散卷积复发性神经网络的空间来填补这一空白，并从目标交叉口处的信号正时计划产生了空间信息。信号交叉点处的流量被建模为一个扩散过程，其通过信号相时机的相位分割构建的过渡矩阵。我们应用这种新颖的方法来预测循环传感器测量值的交通流量，并在加利福尼亚州阿卡迪亚的动脉交叉点上进行信号正时计划。我们证明我们提出的方法得出了卓越的预测。对于30分钟的预测范围，我们将MAPE降低至16％的早晨峰值，关闭峰值10％，下午峰值为8％。此外，我们通过在探测器覆盖范围，检测器类型和数据质量中的各种设置的许多实验来体现模型的鲁棒性。
Accurate and reliable prediction of traffic measurements plays a crucial role in the development of modern intelligent transportation systems. Due to more complex road geometries and the presence of signal control, arterial traffic prediction is a level above freeway traffic prediction. Many existing studies on arterial traffic prediction only consider temporal measurements of flow and occupancy from loop sensors and neglect the rich spatial relationships between upstream and downstream detectors. As a result, they often suffer large prediction errors, especially for long horizons. We fill this gap by enhancing a deep learning approach, Diffusion Convolutional Recurrent Neural Network, with spatial information generated from signal timing plans at targeted intersections. Traffic at signalized intersections is modeled as a diffusion process with a transition matrix constructed from the phase splits of the signal phase timing plan. We apply this novel method to predict traffic flow from loop sensor measurements and signal timing plans at an arterial intersection in Arcadia, CA. We demonstrate that our proposed method yields superior forecasts; for a prediction horizon of 30 minutes, we cut the MAPE down to 16% for morning peaks, 10% for off peaks, and even 8% for afternoon peaks. In addition, we exemplify the robustness of our model through a number of experiments with various settings in detector coverage, detector type, and data quality.
