Traffic Congestion and Duration Prediction Model Based on Regression Analysis and Survival Analysis

With the current situation of traffic congestion becoming more and more serious, how to accurately predict the time of traffic congestion has been widely concerned. In this article, we will build two models to better predict traffic congestion time. First, we use methods to collect the data we need, and through the preliminary cleaning, processing, deletion of missing data, combined calculation of data according to indicators and other steps to screen and integrate the data we need. Then, the multivariate linear regression method is used to construct the traffic prediction congestion model for the existing data, and the actual situation of traffic congestion is obtained. Secondly, the non-parametric method Kaplan-Meier model in the survival analysis method is used to obtain the survival function of traffic congestion duration, and the traffic congestion duration model is constructed. The software programming is solved by MATLAB, Stata, SPSS, etc., and the congestion prediction is obtained. The fitting degree between the predicted value and the actual value of the model is above 0.96, which can better quantify the conclusion that the road traffic operation congestion degree and congestion duration model can identify the characteristics of congestion distribution and duration. Finally, the paper evaluates the advantages and disadvantages of the model objectively, and considers the aspects that can be promoted and applied. I hope that this model can contribute to the prediction research of traffic congestion time!