一种融合信号相位信息的图卷积网络用于干道交通预测
A Graph Convolutional Network with Signal Phasing Information for Arterial Traffic Prediction
AI总结：
针对现有干道交通预测方法忽略空间关系、长时域误差大的问题，提出融合信号相位空间信息的扩散卷积循环神经网络，在阿卡迪亚市交叉口实验中显著降低不同时段30分钟预测的MAPE，且鲁棒性良好。
摘要：
准确可靠的交通测量值预测在现代智能交通系统的发展中起着至关重要的作用。由于更复杂的道路几何结构以及信号控制的存在，干道交通预测的难度高于高速公路交通预测。现有的许多干道交通预测研究仅考虑环形传感器采集的流量和占有率的时间序列测量值，忽略了上下游检测器之间丰富的空间关系。因此，这类方法往往存在较大的预测误差，尤其是在长预测时域下。我们通过对深度学习方法Diffusion Convolutional Recurrent Neural Network（扩散卷积循环神经网络）进行增强，融入目标交叉口信号配时方案生成的空间信息，填补了这一空白。信号控制交叉口的交通被建模为一个扩散过程，其转移矩阵由信号相位配时方案的相位绿信比构建。我们将这一新方法应用于加利福尼亚州阿卡迪亚市一处干道交叉口，基于环形传感器测量数据和信号配时方案预测交通流量。实验证明，我们提出的方法能生成更优的预测结果；对于30分钟的预测时域，我们将早高峰的平均绝对百分比误差（MAPE）降至16%，平峰期降至10%，晚高峰甚至降至8%。此外，我们通过设置不同检测器覆盖范围、检测器类型和数据质量的多组实验，验证了模型的鲁棒性。
英文摘要：
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.
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