论文标题
混合时空图形卷积网络：通过导航数据改善流量预测
Hybrid Spatio-Temporal Graph Convolutional Network: Improving Traffic Prediction with Navigation Data
论文作者
论文摘要
由于在线导航服务，乘车共享和智能城市项目的普及，交通预测最近引起了人们的兴趣。由于道路交通的非平稳性，预测准确性在根本上受到缺乏上下文信息的限制。为了解决这个问题，我们提出了混合时空图形卷积网络（H-STGCN），该网络能够通过利用即将到来的流量量的数据来“推断”未来的旅行时间。具体来说，我们提出了一种算法，以从在线导航引擎中获取即将到来的流量。利用分段线性的流动密度关系，一种新型的变压器结构将即将到来的体积转换为相当于旅行时间的等效物。我们将该信号与普遍利用的旅行时间信号相结合，然后应用图形卷积以捕获空间依赖性。特别是，我们构建了一个反映先天交通接近的复合邻接矩阵。我们在现实世界数据集上进行了广泛的实验。结果表明，H-STGCN在各种指标中的表现明显胜过最先进的方法，尤其是对于预测非经常性拥塞的方法。
Traffic forecasting has recently attracted increasing interest due to the popularity of online navigation services, ridesharing and smart city projects. Owing to the non-stationary nature of road traffic, forecasting accuracy is fundamentally limited by the lack of contextual information. To address this issue, we propose the Hybrid Spatio-Temporal Graph Convolutional Network (H-STGCN), which is able to "deduce" future travel time by exploiting the data of upcoming traffic volume. Specifically, we propose an algorithm to acquire the upcoming traffic volume from an online navigation engine. Taking advantage of the piecewise-linear flow-density relationship, a novel transformer structure converts the upcoming volume into its equivalent in travel time. We combine this signal with the commonly-utilized travel-time signal, and then apply graph convolution to capture the spatial dependency. Particularly, we construct a compound adjacency matrix which reflects the innate traffic proximity. We conduct extensive experiments on real-world datasets. The results show that H-STGCN remarkably outperforms state-of-the-art methods in various metrics, especially for the prediction of non-recurring congestion.
