Design and implementation of real-time traffic flow prediction algorithms for intelligent transportation systems

The advancement of Intelligent Transportation Systems (ITS) plays a crucial role in easing urban traffic congestion and improving the efficiency of traffic flow, one of its key technologies is the prediction of traffic flow in real time. This study proposes an innovative algorithm designed to predict congestion based on incremental urban traffic flow data, addressing challenges such as processing large-scale data and ensuring real-time performance in urban traffic scenarios. The shortcomings of existing traffic flow prediction algorithms are analyzed, and an improved model is developed accordingly. By incorporating advanced time series analysis and big data parallel computing techniques, the algorithm enhances both the real-time processing capabilities and prediction accuracy. The proposed algorithm is applied to real-world traffic scenarios, demonstrating its effectiveness and adaptability in traffic flow prediction. Results confirm that the algorithm significantly improves prediction performance and provides critical technical support for the planning and management of intelligent transportation systems.