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交通流精准预测，对于构建高效智能交通信号控制系统至关重要。然而，智能交叉口产生的非规则交通时间序列使预测任务面临三大新挑战：1）异步空间依赖性，2）交通数据间的不规则时间依赖性，3）待预测序列长度可变。这些挑战严重制约了现有交通预测方法的性能。为此，我们提出异步时空图卷积网络（ASeer）以预测未来时间窗口内进入智能交叉口的车道交通状态。具体而言，首先通过交通扩散图关联车道，提出异步图扩散网络建模车道间时间错位交通状态测量的异步空间依赖性；其次，为捕捉不规则交通状态序列中的时间依赖性，设计可学习的个性化时间编码嵌入每条车道的连续时间；继而提出可变换时域卷积网络，通过学习元滤波器生成具有可变滤波器尺寸的时域卷积滤波器，实现不规则序列的高效时域卷积；最后，设计由状态演化单元和半自回归预测器构成的半自回归预测网络，有效且高效地预测变长交通状态序列。在两个真实世界数据集上的大量实验表明，ASeer在六项评估指标中均展现出卓越性能。