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Data fusion of traffic parameters such as speed and density is a crucial study in intelligent transportation system, yet complicated to formulate mathematically. The aim of this paper is to study and model the multi-source fusion traffic flow data. Based on the data detected in a mega-city, the regression model is developed to solve the defects in the traditional models in this paper. The result demonstrates that the proposed models can pass the effectiveness test and have a favorable fusion effect, whose fusion result can meet the demand for precision. Apart from the accuracy demand, the proposed models can solve the fusion problem briefly and effectively so that it can be used in the practical engineering application. This study concludes that the proposed model has expressed well the data fusion for the practical multi-source data detected in the mega-city, which proves the model has good effectiveness and applicability.

Research on Multi-source Traffic Flow Data Fusion Based on Linear Regression: A Case Study in Mega-city