Development Trend of Intelligent Transportation in the Era of Big Data

Abstract

The arrival of the big data era has brought great convenience to people’s real life. All aspects of our lives follow the development of the big data era. In the context of the era of big data, through data collection and analysis, the current urban transportation has been reasonably developed, laying a good foundation for the development of intelligent transportation in the future, and can solve various current problems. Based on the analysis of the traffic system, this article finds that the real-time road condition prediction of common functions is highly correlated with route guidance. Therefore, this paper uses a method based on historical data to predict road conditions, and uses the prediction results to dynamically allocate road segments, and then uses the relevant single-source shortest path algorithm to simulate the optimal path. By analyzing the application advantages of data mining in intelligent transportation, Baum Welch’s algorithm is used for data mining in a real-time traffic prediction system based on historical data. Real-time road conditions are matched with historical data models to predict road conditions. Finally, analyzed and designed the big data cloud platform framework applied to it to achieve higher efficiency. The experimental research results show that, by studying the development of urban intelligent transportation systems, the purpose of this paper is to make better use of existing resources to process data more efficiently, and then combine distributed data mining algorithms to optimize the processed data and apply it to in the intelligent transportation system, an intelligent management and control system that is more efficient, accurate, and user experience better can be established.