TITLE:
Traffic Congestion and Duration Prediction Model Based on Regression Analysis and Survival Analysis
AUTHORS:
Yidan Liu, Chao Liu, Ziling Zheng
KEYWORDS:
Traffic Congestion Time, Multiple Linear Regression, Survival Analysis Method, Traffic Congestion Prediction Model, Traffic Congestion Duration Model
JOURNAL NAME:
Open Journal of Business and Management,
Vol.8 No.2,
March
30,
2020
ABSTRACT: 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!