文献类型：期刊文献

英文题名：Penalty Decomposition Methods for Second-Best Congestion Pricing Problems on Large-Scale Networks

作者：Guo, Lei[1];Zhou, Wenxin[2];Wang, Xiaolei[2];Yang, Hai[3];Fan, Tijun[1]

机构：[1]East China Univ Sci & Technol, Sch Business, Shanghai 200237, Peoples R China;[2]Tongji Univ, Sch Econ & Management, Shanghai 200092, Peoples R China;[3]Hong Kong Univ Sci & Technol, Dept Civil & Environm Engn, Hong Kong, Peoples R China

年份：2025

卷号：37

期号：6

起止页码：1542

外文期刊名：INFORMS JOURNAL ON COMPUTING

收录：;EI(收录号：20260119856683);WOS:【SCI-EXPANDED(收录号:WOS:001385186100001)】；

基金：Funding: This work was supported by the National Natural Science Foundation of China [Grants 72032001, 72431007, 72131007, 72021002, and 12271161] . L. Guo was also supported by the Natural Science Foundation of Shanghai [Grant 22ZR1415900] . X. Wang was also supported by the Funda-mental Research Funds for the Central Universities and CCF-DiDi GAIA Collaborative Research Funds for Young Scholars.

语种：英文

外文关键词：congestion pricing; bilevel program; large-scale network; decomposition method

摘要：The second-best congestion pricing (SBCP) problem is one of the most challenging problems in transportation because of its two-level hierarchical structure. In spite of various intriguing attempts at solving SBCP, existing solution methods are either heuristic without a convergence guarantee or suitable for solving SBCP on small networks only. In this paper, we first reveal some convexity-based structural properties of the marginal value function reformation of SBCP, and then, by effectively exploiting these structural properties, we propose two dedicated decomposition methods for solving SBCP on large-scale networks, which are different from existing methods in that they avoid linearizing nonconvex functions. We establish the convergence of the two decomposition methods under commonly used conditions and provide the maximum number of iterations for deriving an approximate stationary solution. The computational experiments based on a collection of real road networks show that in comparison with three existing popular methods, the two proposed methods are capable of solving SBCP on larger-scale networks, and for instances that can be solved by existing methods, the two proposed methods are substantially faster.