School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan.
Sensors (Basel). 2023 Apr 9;23(8):3836. doi: 10.3390/s23083836.
Intelligent transportation systems (ITSs) have become an indispensable component of modern global technological development, as they play a massive role in the accurate statistical estimation of vehicles or individuals commuting to a particular transportation facility at a given time. This provides the perfect backdrop for designing and engineering an adequate infrastructural capacity for transportation analyses. However, traffic prediction remains a daunting task due to the non-Euclidean and complex distribution of road networks and the topological constraints of urbanized road networks. To solve this challenge, this paper presents a traffic forecasting model which combines a graph convolutional network, a gated recurrent unit, and a multi-head attention mechanism to simultaneously capture and incorporate the spatio-temporal dependence and dynamic variation in the topological sequence of traffic data effectively. By achieving 91.8% accuracy on the Los Angeles highway traffic (Los-loop) test data for 15-min traffic prediction and an R2 score of 85% on the Shenzhen City (SZ-taxi) test dataset for 15- and 30-min predictions, the proposed model demonstrated that it can learn the global spatial variation and the dynamic temporal sequence of traffic data over time. This has resulted in state-of-the-art traffic forecasting for the SZ-taxi and Los-loop datasets.
智能交通系统（ITSs）已经成为现代全球技术发展不可或缺的组成部分，因为它们在准确统计特定时间内前往特定交通设施的车辆或个人方面发挥了巨大作用。这为交通分析的基础设施容量设计和工程提供了完美的背景。然而，由于道路网络的非欧几里得和复杂分布以及城市化道路网络的拓扑约束，交通预测仍然是一项艰巨的任务。为了解决这一挑战，本文提出了一种交通预测模型，该模型结合了图卷积网络、门控循环单元和多头注意力机制，能够有效地同时捕获和融合交通数据的时空相关性和拓扑序列中的动态变化。该模型在洛杉矶高速公路交通（Los-loop）测试数据上实现了 15 分钟交通预测的 91.8%的准确率和 SZ-taxi 测试数据集上 15 分钟和 30 分钟预测的 85%的 R2 分数，证明了它可以随着时间的推移学习交通数据的全局空间变化和动态时间序列。这使得 SZ-taxi 和 Los-loop 数据集的交通预测达到了最新水平。
