Traffic Spatial Data Management System Based on Data Mining

Abstract

Data mining is an important research field emerging in the field of information technology in recent years. How to use advanced data mining to develop and implement a large-scale and diverse traffic spatial data management system has important research significance. The purpose of this paper is to study the traffic spatial data management system based on data mining. In this paper, the realization framework of the software system for short-term traffic flow prediction based on data mining and the application of intelligent traffic data mining platform are given. This paper proposes a road traffic flow spatial data mining algorithm, which uses the spatial distribution characteristics of traffic flow to divide the road traffic network into real-time and dynamic traffic areas. This paper focuses on the theory of data mining and big data, as well as the related processing technologies involved in the research process, and then expounds the application of data mining technology in the intelligent transportation spatial data management system, and the specific ideas and concepts used in the research process. In this paper, the absolute error is used to represent the size of the error, and the absolute error at all time points (time period 8:45 to 11:45) is statistically analyzed. The results show that the improved GM (1, 1) model is smaller than other methods. The GM (1, 1) model has better prediction stability.