Comparative Study on Traffic Prediction Using Different Models - Jiao Zhtiofan (ITM Web of Conferences)
Traffic flow prediction (TFP) is a complex and critical field that is of great significance for urban planning, management, and resource allocation. This paper discusses the development history and optimization strategies of TFP models. This paper first introduces the importance of TFP and outlines the basic concepts and characteristics of traditional and modern TFP models. Subsequently, through comparative analysis, the prediction accuracy and applicability of the two models were discussed in depth. On this basis, the key factors affecting TFP accuracy are further analyzed, and the corresponding model optimization strategy is proposed. This paper proposes an improved method for fusion prediction by combining multiple data sources. Through these optimizations, the model is better able to respond to sudden traffic changes and improve the robustness and real-time prediction of the forecast. Finally, the research results are summarized and the future research directions are prospected. Through the systematic study of TFP models, this paper provides theoretical support and practical guidance for traffic management and planning, which has important academic value and application prospects.