Optimization of Relevant Functions of Urban Computing in the Direction of Traffic Flow Prediction

As an important application direction of urban computing, traffic flow prediction plays an important role in modern traffic management, urban planning and sustainable development. In recent years, many cutting-edge studies in the field of traffic flow prediction have had a significant impact and promoted the development of practical applications in this field. This paper mainly focuses on the research results of various traffic prediction directions. According to the actual environment and the functional characteristics of the research results, the research is classified into three aspects: data acquisition, feature engineering, and prediction model optimization. It also summarizes the optimization effects of research on traffic flow prediction in sensor data acquisition, data outlier processing, neural network prediction technology, etc. This paper first proposes three important aspects that affect traffic flow prediction and classifies recent research results. Then, the functions and impacts are analyzed from various aspects, and the advantages and progress of the research results are analyzed by comparing most mainstream methods. Then, the problems and limitations of the research are analyzed and discussed in combination with the actual road environment. Finally, the future research direction and development trend of this field are prospected, and the full text is summarized.