图神经网络驱动的交通预测技术:探索与挑战

Graph neural network driven traffic prediction technology:review and challenge

随着物联网及人工智能技术的快速发展,对交通数据进行精准的分析和预测成为智慧交通的首要环节。近年来,交通预测方法逐渐从经典的模型驱动转变为数据驱动,然而,如何通过大数据有效分析路网的时空特性是预测过程中面临的关键难题之一。时空大数据分析是交通预测的利器,将交通路网建模为图网络,将深度学习方法在图网络上进行扩展,通过图神经网络建立时空预测模型,采用图卷积的方式有效地获取路网传感器节点之间的时空相关性,可以显著提高交通预测模型的精度。针对图神经网络驱动的交通预测技术进行了探索,基于深度时空特性分析提炼了两大类交通预测模型,并通过实例进行分析和验证,探讨了图神经网络在交通预测领域的技术优势和主要挑战,挖掘了图神经网络预测机制的潜在研究方向。

With the rapid development of Internet of things and artificial intelligence technology,accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years,the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However,how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network,while the deep learning method can be extended on the graph network.Utilizing graph neural networks,we can build the spatiotemporal prediction model,and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution,which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored,and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.