Bi-objective traffic count location model for mean and covariance of origin-destination estimation

This paper describes a bi-objective optimization model for the traffic count location problem in stochastic origin-destination (OD) traffic demand estimation. Two measures are defined to capture the maximum possible absolute error of the mean and the covariance of the estimated OD demand. The bounds of these two measures are mathematically deduced, and then the bi-objective optimization model is formulated to minimize the two upper bounds simultaneously. A surrogate-assisted genetic algorithm is proposed to solve this model, and a series of numerical examples are presented to demonstrate the applicability of the proposed model and the efficiency of the proposed algorithm.

Automated summary

This paper introduces a bi-objective optimization model for the traffic count location problem, aiming to minimize the maximum possible absolute error of the mean and the covariance of the estimated OD demand. A surrogate-assisted genetic algorithm is employed to solve this model, and numerical examples are provided to demonstrate the applicability of the model and the efficiency of the algorithm.