Resolving instability in railway timetabling problems

Abstract

A significant growth of the railway transportation demand is forecasted in the next decades which needs an increase of network capacity. Where possible, infrastructure upgrading can provide extra capacity; although in some cases, this is not enough to satisfy the entire transportation demand even if optimised timetabling is performed.We propose a heuristic model to develop a stable timetable which maximises the satisfaction of transportation demand in situations where network capacity is limited. In case the demand cannot be fully satisfied, the model relaxes the given line plan and timetable design parameters. The aim is to keep as many train services as possible and reduce the level of service minimally. We develop a mixed integer linear programming (MILP) model for minimising the cycle time to find an optimised stable timetable for the given line plan. The heuristic iteratively solves the MILP model and applies relaxation measures. We tested the model on the Dutch network. The results showed that the model can generate stable timetables by removing train services from the critical circuit, and also, higher transportation demand can be satisfied by additionally relaxing timetable design parameters.

Robustness and disturbances in public transport

“…( 2016 ) Microscopic and macroscopic network model Hierarchical framework for timetable design Beśinović et al. ( 2019 ) Issues of capacity extensions and stable timetables Heuristic model and MIP; case study for Netherlands railways Bettinelli et al. ( 2017 ) Train rescheduling Iterated greedy heuristic Cacchiani et al.…”

Estimating impacts of covid19 on transport capacity in railway networks

“…A detailed analysis of different aspects affecting capacity can be found in Abril et al (2008); Harrod (2009); Schmidt (2014); Shih et al (2014) and Lindfeldt (2015), while an empirical comparison of different capacity assessment methods can EJTIR 21(1), 2021, pp.1-18 4 Bešinović and Szymula Estimating impacts of covid19 on transport capacity in railway networks be found in Rotoli et al (2016). For more detailed overview of methods for railway traffic capacity we refer to Bešinović and Goverde (2018). Gentile et al (2016) reviewed transit assignment models and addressed both frequency-based and schedule-based variants, and possible approaches to represent the dynamic system such as steady state, macroscopic flows, and agent-based (route choice, flow propagation, arc performances).…”

“…Badii et al, (2020) and Sommer (2020) reported that there have been remarkable improvements in air quality and declines in petroleum use throughout the world as a result of the short-term and significant decline in freight and passenger travel. The first steps have been towards designing railway timetables for excessive passenger demand while satisfying the demand as much as possible for given infrastructure (Bešinović et al, 2019). Authors defined the demand implicitly representing it by predefined desired line plan and train line frequencies.…”

“…Transport capacity is defined by number of seats offered to passengers within a given period of time. In the existing literature, models for assessing railway traffic capacity have been mostly addressed (Bešinović and Goverde, 2018) and to this purpose various applications have been considered including analytical models, simulations, and optimization models. However, only limited research exists on estimating transport capacity of railway systems.…”