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

Weiwei Sun, Hu Shao, Liang Shen, Ting Wu, William H.K. Lam, Baozhen Yao, Bin Yu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number114554
JournalExpert Systems with Applications
Volume170
DOIs
Publication statusPublished - 15 May 2021

Keywords

  • Bi-objective optimization
  • Covariance matrix
  • Origin–destination estimation
  • Surrogate-assisted genetic algorithm
  • Traffic count location

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence

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