Passenger-oriented rescheduling of trains and rolling stock for handling large passenger demand: linearized models with train capacity constraint

This paper focuses on adjusting the offline-planned schedules for urban rail networks in case of large passenger demand. We simultaneously reschedule train services, adjust rolling stock plans, and find best route passengers updated schedule. The goal is to improve transport performance balance it with operating cost, while respecting operational constraints. A mixed-integer nonlinear programming (MINLP) model first proposed. Approximate exact methods are further introduced reformulate term MINLP model, resulting linear (MILP) models. In models, emergency services can be added if necessary. Short-turning stop-pattern adaptation speed up circulation (i.e. metro vehicles). Passengers same characteristics gathered into a group, group may follow different routes, depending resource availability. Experimental results small-scale study demonstrate better three reformulation methods, i.e. they feasible solution much faster (within 180 s) obtain solutions higher quality within certain computation time, comparison other proposed approximate methods. Moreover, identify improved operation passengers, 13% improvement when properly shortening headway time 69% trains.