Application Research of Big Data Technology in Intelligent Prediction of Urban Traffic Flow

Abstract

With the development of urbanisation and the spread of information-sensing technology, the data produced by urban traffic systems has begun to show the characteristics of big data, such as a large volume, high dimensionality, heterogeneity and strong spatiotemporal dependence. There are some good prospects and problems in the field of traffic flow prediction now. The old prediction model is no longer suitable for the current type of data and cannot handle the irregular fluctuations in space and time. The purpose of this paper is to introduce the general structure of big data technology applications in intelligent prediction of urban traffic flow. First, this paper will introduce the main characteristics of urban traffic big data and the development ideas of intelligent prediction algorithms. Next, we will present the method of multi-source data fusion processing for prediction, the structure of a spatiotemporal prediction model based on deep learning (such as graph neural networks and attention mechanisms), and a dynamic prediction integration system that supports real-time stream processing. Finally, we will introduce some application cases of the above technology in short-term high-precision prediction, simulation and deduction of congestion propagation, as well as system performance analysis and uncertainty evaluation. Big Data has been used to develop some intelligent prediction methods that can find hidden patterns in traffic conditions easily, and a good data foundation has been provided for dynamic perception and control of traffic.