Mathematical Modeling and Simulation of Travel Time Prediction of Dense Traffic Flow in the city
Currently,urban traffic networks contain a large number of road intersections and vehicles.Due to the influence of multiple factors such as workdays and rest days,traffic flow is uncertain,which increases the difficulty of prediction.Therefore,this article proposed a mathematical modeling study on predicting trip time in dense urban traf-fic.Firstly,we sampled historical data of urban vehicles and then identified data interpolation points to interpolate ve-hicle operation data.Secondly,we used deep learning data normalization to calculate average travel time,travel time variance,and reliability indicators,thus extracting the features of dense urban traffic flow.Finally,based on the Kal-man filter,we transformed the prediction problem into a problem about spatial state calculation,thereby achieving the prediction of trip time in dense traffic.Experiment results prove that the model can accurately predict the travel time in dense urban traffic and help drivers plan their trips reasonably.Meanwhile,the average absolute percentage error is less than 2.3%.
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