Managing Peak-Hour Congestion in Urban Rail Transit with the Sub-Train Price Adjustment Strategy

Table 1

A summary of models.

References	Objective function	Solving algorithm
Tang et al.	The total equilibrium costs	Sequential iterative solution algorithm
Huan et al.	Max the sum of the operator and com-muter surplus and min the peak ridership	Genetic algorithms
Huang et al.	Maximization of the social welfare	Hybrid artificial bee colony algorithm
Wu et al.	Maximize the total ticket revenue	A two-stage algorithm
Sun et al.	Maximize transit operator’s profit	Sensitivity-based descent search method
This paper	Minimize the total number of passengers exceeding the full-load rate (NEFR)	Simulated annealing algorithm