Traffic forecasting is an essential problem in urban planning and computing. The complex dynamic spatial-temporal dependencies among traffic objects (e.g., sensors and road segments) have been calling for highly flexible models; unfortunately, sophisticated models may suffer from poor robustness especially in capturing the trend of the time series (1st-order derivatives with time), leading to unrealistic forecasts. To address the challenge of balancing dynamics and robustness, we propose TrendGCN, a new scheme that extends the flexibility of GCNs and the distribution-preserving capacity of generative and adversarial loss for handling sequential data with inherent statistical correlations. On the one hand, our model simultaneously incorporates spatial (node-wise) embeddings and temporal (time-wise) embeddings to account for heterogeneous space-and-time convolutions; on the other hand, it uses GAN structure to systematically evaluate statistical consistencies between the real and the predicted time series in terms of both the temporal trending and the complex spatial-temporal dependencies. Compared with traditional approaches that handle step-wise predictive errors independently, our approach can produce more realistic and robust forecasts. Experiments on six benchmark traffic forecasting datasets and theoretical analysis both demonstrate the superiority and the state-of-the-art performance of TrendGCN. Source code is available at https://github.com/juyongjiang/TrendGCN.

翻译：交通预测是城市计算与规划中的核心问题。交通对象（如传感器与道路路段）间复杂的动态时空依赖性对模型灵活性提出了极高要求；然而，复杂模型可能面临鲁棒性不足的挑战，尤其是在捕捉时间序列趋势（时间的一阶导数）时，可能导致不切实际的预测结果。为应对动态性与鲁棒性之间的平衡难题，本文提出TrendGCN——一种融合图卷积网络灵活性与生成对抗损失分布保持能力的新框架，用于处理具有内在统计相关性的序列数据。一方面，该模型通过联合嵌入空间（节点级）与时间（时间级）特征，实现异构的时空卷积运算；另一方面，其采用生成对抗网络结构系统评估真实时间序列与预测时间序列在趋势演化及复杂时空依赖性方面的统计一致性。相较传统方法独立处理逐点预测误差的方式，本文方法能生成更真实鲁棒的预测结果。在六个基准交通预测数据集上的实验与理论分析均证明了TrendGCN的优越性与当前最优性能。源代码已开源至https://github.com/juyongjiang/TrendGCN。