Code and Data Repository for Penalty Decomposition Methods for Second-Best Congestion Pricing Problems on Large-Scale Networks
针对次优拥堵定价问题，提出两种避免线性化非凸函数的分解方法，在真实路网实验中比现有方法能求解更大规模网络且速度更快。
he second-best congestion pricing (SBCP) problem is one of the most challenging problems in transportation due to its two-level hierarchical structure. In spite of various intriguing attempts for solving SBCP, existing solution methods are either heuristic without 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.