Accurate traffic forecasting at intersections governed by intelligent traffic signals is critical for the advancement of an effective intelligent traffic signal control system. However, due to the irregular traffic time series produced by intelligent intersections, the traffic forecasting task becomes much more intractable and imposes three major new challenges: 1) asynchronous spatial dependency, 2) irregular temporal dependency among traffic data, and 3) variable-length sequence to be predicted, which severely impede the performance of current traffic forecasting methods. To this end, we propose an Asynchronous Spatio-tEmporal graph convolutional nEtwoRk (ASeer) to predict the traffic states of the lanes entering intelligent intersections in a future time window. Specifically, by linking lanes via a traffic diffusion graph, we first propose an Asynchronous Graph Diffusion Network to model the asynchronous spatial dependency between the time-misaligned traffic state measurements of lanes. After that, to capture the temporal dependency within irregular traffic state sequence, a learnable personalized time encoding is devised to embed the continuous time for each lane. Then we propose a Transformable Time-aware Convolution Network that learns meta-filters to derive time-aware convolution filters with transformable filter sizes for efficient temporal convolution on the irregular sequence. Furthermore, a Semi-Autoregressive Prediction Network consisting of a state evolution unit and a semiautoregressive predictor is designed to effectively and efficiently predict variable-length traffic state sequences. Extensive experiments on two real-world datasets demonstrate the effectiveness of ASeer in six metrics.

翻译：由智能交通信号灯控制的交叉口处的精确交通预测对于推进高效的智能交通信号控制系统至关重要。然而，由于智能交叉口产生的不规则交通时间序列，交通预测任务变得更加棘手，并带来三个主要新挑战：1）异步空间依赖性，2）交通数据间的不规则时间依赖性，以及3）待预测的可变长度序列，这些严重阻碍了现有交通预测方法的性能。为此，我们提出一种异步时空图卷积网络（ASeer）来预测未来时间窗口内进入智能交叉口车道的交通状态。具体地，通过交通扩散图连接车道，我们首先提出一种异步图扩散网络来建模车道间时间不对齐的交通状态测量值的异步空间依赖性。随后，为捕捉不规则交通状态序列内的时间依赖性，我们设计了一种可学习的个性化时间编码来嵌入每条车道的连续时间。进而提出一种可变换时间感知卷积网络，该网络学习元滤波器以推导具有可变换滤波器大小的时间感知卷积滤波器，用于在不规则序列上进行高效时间卷积。此外，设计了一种由状态演化单元和半自回归预测器组成的半自回归预测网络，以高效且有效地预测可变长度的交通状态序列。在两个真实世界数据集上的大量实验证明，ASeer在六项指标上均具有有效性。