Abstract:In order to improve the problem of data missing due to the interference of the floating car GPS data during the collection process, the correlation between the floating car GPS data and the traffic flow state and road alignment was studied through the analysis method, the floating car based on the optimized random forest algorithm was proposed. In the random forest algorithm interpolation process, the interpolation result has volatility problems due to its own randomness, and a weight factor is introduced in the result output part. Through the linear optimization algorithm, the weight factor is adjusted to reduce the volatility of the output result while satisfying the road alignment characteristics. The experiment interpolated the travel trajectory data of 6 volunteers for 21 days, and the results showed that the average error of the model constructed in this paper was 12.3m, which decreased by 14.9m, 24.3m and 239.3m respectively compared with the random forest model, decision tree model and linear regression model.it is concluded that the interpolation model established by the optimized random forest algorithm can improve the accuracy of the floating car GPS data interpolation, and provide a data basis for applications such as traffic state analysis and map matching.