4.7 Article

A faster path-based algorithm with Barzilai-Borwein step size for solving stochastic traffic equilibrium models

期刊

EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
卷 290, 期 3, 页码 982-999

出版社

ELSEVIER
DOI: 10.1016/j.ejor.2020.08.058

关键词

Transportation; Stochastic user equilibrium; Barzilai-Borwein step size; Path-based traffic assignment algorithm; Cross-nested logit

资金

  1. Natural Science Foundation of China [71801079]
  2. Research Grants Council of the Hong Kong Special Administrative Region [15212217]
  3. Research Committee of the Hong Kong Polytechnic University [1-ZVJV]
  4. Research Institute for Sustainable Urban Development at the Hong Kong Polytechnic University [1-BBWF]

向作者/读者索取更多资源

This paper explores a novel step size determination scheme, the Barzilai-Borwein step size, and applies it to solving the stochastic user equilibrium problem. Experimental results demonstrate that the BB step size outperforms current step size strategies in terms of computational efficiency and robustness.
Step size determination (also known as line search) is an important component in effective algorithmic development for solving the traffic assignment problem. In this paper, we explore a novel step size determination scheme, the Barzilai-Borwein (BB) step size, and adapt it for solving the stochastic user equilibrium (SUE) problem. The BB step size is a special step size determination scheme incorporated into the gradient method to enhance its computational efficiency. It is motivated by the Newton-type methods, but it does not need to explicitly compute the second-order derivative. We apply the BB step size in a path-based traffic assignment algorithm to solve two well-known SUE models: the multinomial logit (MNL) and cross-nested logit (CNL) SUE models. Numerical experiments are conducted on two real transportation networks to demonstrate the computational efficiency and robustness of the BB step size. The results show that the BB step size outperforms the current step size strategies, i.e., the Armijo rule and the self-regulated averaging scheme. (C) 2020 Elsevier B.V. All rights reserved.

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