Data Analysis and Congestion Prediction Model in Intelligent Transportation Systems

Abstract

The relentless march of urbanization has led to a surge in population density and a dramatic increase in the number of motor vehicles, with traffic congestion emerging as a significant barrier to urban development. Intelligent Transportation Systems (ITS) utilize advanced information technology to monitor and analyze traffic flow in real time, playing a pivotal role in alleviating urban traffic pressures and enhancing road usage efficiency. This paper aims to propose a data analysis method based on machine learning algorithms for real-time traffic monitoring and congestion prediction, thereby providing scientific decision support for urban traffic management. The research encompasses methods of traffic data collection, including data on vehicle location, speed, and flow; preprocessing and feature extraction techniques for traffic data to improve data quality and extract features useful for congestion prediction; and the application of various machine learning algorithms to establish a traffic congestion prediction model. Additionally, this paper assesses and optimizes the model and tests it with actual traffic data to verify its effectiveness and practicality. The research findings indicate that the proposed machine learning-based method can effectively predict traffic congestion, providing a powerful tool for traffic management departments, aiding in preemptive measures to reduce traffic delays and improve the travel experience for citizens. This study not only enriches the field of intelligent transportation systems research but also provides theoretical foundations and technical support for urban traffic management practices.