Multi-Objective Optimization for Skip-Stop Strategy Based on Smartcard Data Considering Total Travel Time and Equity

With the rise of urban sprawl, urban railways extend out further to the city's outer district, installing additional stations. Passengers who travel from the outer district to the center of the city therefore experience long travel times. Although skip-stop strategy helps save total travel time, deviation of travel time among all origin-destination pairs may be increased, leading to equity problems. This study aims to minimize the inequity and total travel time through train stop planning and train scheduling. A coefficient of variation is adopted as a measure of inequity. The problem is formulated as a multi-objective mixed integer nonlinear programming model. Origin-destination demand is extracted from smartcard data and a case study of four urban railway lines in Seoul is conducted. The results indicate that the number of transfer stations for equity-oriented skip-stop strategy is smaller than that for total-travel-time-oriented skip-stop strategy. We also discover that as the number of transfer stations rises, inequity increases and total travel time is reduced. For skip-stop strategy considering total travel time and equity simultaneously, average total travel time and the average deviation are reduced by up to 10.3% and 10.6%, respectively, compared with those of all-stop strategy. We analyze the gradient of Pareto optimal sets to find out which factors (equity or total travel time) are more significant. Skip-stop strategy on lines 5 and 9 can be designed based on equity, while line 4 can be planned based on total travel time.