A Passenger‐Oriented Model for Train Rescheduling on an Urban Rail Transit Line considering Train Capacity Constraint

Abstract

The major objective of this work is to present a train rescheduling model with train capacity constraint from a passenger-oriented standpoint for a subway line. The model expects to minimize the average generalized delay time (AGDT) of passengers. The generalized delay time is taken into consideration with two aspects: the delay time of alighting passengers and the penalty time of stranded passengers. Based on the abundant automatic fare collection (AFC) system records, the passenger arrival rate and the passenger alighting ratio are introduced to depict the short-term characteristics of passenger flow at each station, which can greatly reduce the computation complexity. In addition, an efficient genetic algorithm with adaptive mutation rate and elite strategy is used to solve the large-scale problem. Finally, Beijing Subway Line 13 is taken as a case study to validate the method. The results show that the proposed model does help neutralize the effect of train delay, with a 9.47% drop in the AGDT in comparison with the train-oriented model.

Optimization of Metro Trains Operation Plans Based on Passenger Flow Data Analysis

“…e number of passengers waiting for train l at station k is W wait (l,k) . It can be expressed by the number of passengers W remain (l−1,k) stranded at station k by the previous train l − 1 and the number of passengers λ k (d (l,k) − d (l−1,k) ) arriving in the travel interval between adjacent train l and train l − 1, where λ k is the passenger arrival rate in the interval between two adjacent trains (d (l,k) − d (l−1,k) ) [23].…”

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

“…Rescheduling methodologies which fulfil these requirements can be found in [129], which dealt with post-disruption operations at the station platform level and [130] which introduced capacity constraints for taking into account the fact that, especially in crowded contexts, not all passengers waiting on the platform are actually able to board the first arriving train. Furthermore, [131] developed a rescheduling framework for minimising the delay time of alighting passengers, as well as the penalty time of stranded passengers, and [132] proposed a dynamic passenger assignment model which implements an event-based simulation technique for modelling alighting and boarding. In particular, in the latter, passengers' en route travel decisions are considered and all phases occurring during a disruption event (i.e.…”

A Practical Method for Timetable Rescheduling in Subway Networks during the End-of-Service Period

“…An iterative algorithm was proposed to solve the model based on the model decomposition. Xu et al [19] proposed a passenger-oriented model for rescheduling on a subway line considering the limited train capacity. The delay time of alighting passengers and the penalty time of stranded passengers constitute the generalized delay time, which is expected to be minimized.…”