Multivariate analysis of traffic flow using copula-based model at an isolated road intersection

Investigating the correlation relationship among traffic flow parameters (speed, time headway, volume) and accurately estimating their distribution function at urban road intersections are critical to provide theoretical support for traffic simulation in traffic management and control. Previous studies usually investigated distributions of traffic flow parameters separately, and they did not take into account the correlation between these parameters. In this study, a copula-based approach is proposed to analyze the dependence structure and model the multivariate distribution of traffic flow parameters during peak and off-peak hours in three directions at the intersections. First, the correlation between the traffic flow parameters/variables is examined by Kendall's tau and Spearman's rho to measure the correlation relationship. Then, fitting marginal distributions for traffic variables is conducted. The distribution function with the best fitting performance of each traffic variable is selected based on AIC and K-S tests. Moreover, copula-based models that employ Gaussian, FGM, Gumbel, Clayton, Frank and AMH copula functions are used to construct the bivariate joint distribution. The optimal copula model is selected by using the log-likelihood and AIC to evaluate fitting results. Finally, by comparing the observed data with the same size simulated data generated from the best fitting copula model, we find that the copula-based model could accurately construct the bivariate joint distribution and reproduce the dependence structure between the variables. Overall, the findings of this study provide an in-depth perspective on the dependence structure of traffic variables at urban road intersections. (C) 2022 Elsevier B.V. All rights reserved.