Analysis and Prediction of Urban Traffic Congestion Based on Big Data

Abstract

With the rapid development of big data technology, its application has become more and more extensive. The application of big data technology in intelligent transportation systems is the best way to solve traffic congestion in big cities. The paper analyses in detail the main causes of traffic congestion in big cities and the classification and evaluation of traffic congestion. Utilizing the Internet of Things and modern communication technologies, large-scale traffic data and related data based on GPS are acquired, and data analysis is carried out to construct a traffic prediction vehicle prediction model. The forecasting model is used to predict the traffic flow in each direction of traffic intersections at a certain time, predict the possibility of congestion at a certain time at a certain intersection, the traffic flow and congestion probability of a certain section at a certain time, and the travel trajectory and travel habit forecast of pedestrians. At the same time, consider the impact of non-motorized vehicles and pedestrians on traffic congestion. Use forecasting results and real-time traffic information monitoring to solve traffic congestion problems. Combined with traffic control and optimization strategy control for traffic collaborative management, it provides valuable reference for decision-making in metropolitan traffic congestion solutions.