305 research outputs found
A railway timetable rescheduling approach for handling large scale disruptions
On a daily basis, relatively large disruptions require infrastructure managers and railway operators to reschedule their railway timetables together with their rolling stock and crew schedules. This research focuses on timetable rescheduling for passenger trains at a macroscopic level in a railway network. An integer programming model is formulated for solving the timetable rescheduling problem, which minimizes the number of cancelled and delayed trains while adhering to infrastructure and rolling stock capacity constraints. The possibility of rerouting trains in order to reduce the number of cancelled and delayed trains is also considered. In addition, all stages of the disruption management process (from the start of the disruption to the time the normal situation is restored) are taken into account. Computational tests of the described model on a heavily used part of the Dutch railway network show that we are able to find optimal solutions in short computation times. This makes the approach applicable for use in practice
Priority based technique for rescheduling trains
No AbstractKeywords: rescheduling; mathematical modelling; service disruptions; priorit
Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms
Abstract The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown
Reinforcement Learning for Scalable Train Timetable Rescheduling with Graph Representation
Train timetable rescheduling (TTR) aims to promptly restore the original
operation of trains after unexpected disturbances or disruptions. Currently,
this work is still done manually by train dispatchers, which is challenging to
maintain performance under various problem instances. To mitigate this issue,
this study proposes a reinforcement learning-based approach to TTR, which makes
the following contributions compared to existing work. First, we design a
simple directed graph to represent the TTR problem, enabling the automatic
extraction of informative states through graph neural networks. Second, we
reformulate the construction process of TTR's solution, not only decoupling the
decision model from the problem size but also ensuring the generated scheme's
feasibility. Third, we design a learning curriculum for our model to handle the
scenarios with different levels of delay. Finally, a simple local search method
is proposed to assist the learned decision model, which can significantly
improve solution quality with little additional computation cost, further
enhancing the practical value of our method. Extensive experimental results
demonstrate the effectiveness of our method. The learned decision model can
achieve better performance for various problems with varying degrees of train
delay and different scales when compared to handcrafted rules and
state-of-the-art solvers
Smooth and controlled recovery planning of disruptions in rapid transit networks
This paper studies the disruption management problem of rapid transit rail networks. We consider an integrated model for the recovery of the timetable and the rolling stock schedules. We propose a new approach to deal with large-scale disruptions: we limit the number of simultaneous schedule changes as much as possible, and we control the length of the recovery period, in addition to the traditional objective criteria such as service quality and operational costs. Our new criteria express two goals: the recovery schedules can easily be implemented in practice, and the operations quickly return to the originally planned schedules after the recovery period. We report our computational tests on realistic problem instances of the Spanish rail operator RENFE and demonstrate the potential of this approach by solving different variants of the proposed model
Application of an iterative framework for real-time railway rescheduling
Since disruptions in railway networks are inevitable, railway operators and infrastructure managers need reliable measures and tools for disruption management. Current literature on railway disruption management focuses most of the time on rescheduling one resource (timetable, rolling stock or crew) at the time. In this research, we describe the application of an iterative framework in which all these three resources are considered. The framework applies existing models and algorithms for rescheduling the individual resources. We extensively test our framework on instances from Netherlands Railways and show that schedules which are feasible for all three resources can be obtained within short computation times. This case study shows that the framework and the existing rescheduling approaches can be of great value in practice
Integrated optimization of train timetables rescheduling and response vehicles on a disrupted metro line
When an unexpected metro disruption occurs, metro managers need to reschedule
timetables to avoid trains going into the disruption area, and transport
passengers stranded at disruption stations as quickly as possible. This paper
proposes a two-stage optimization model to jointly make decisions for two
tasks. In the first stage, the timetable rescheduling problem with cancellation
and short-turning strategies is formulated as a mixed integer linear
programming (MILP). In particular, the instantaneous parameters and variables
are used to describe the accumulation of time-varying passenger flow. In the
second one, a system-optimal dynamic traffic assignment (SODTA) model is
employed to dynamically schedule response vehicles, which is able to capture
the dynamic traffic and congestion. Numerical cases of Beijing Metro Line 9
verify the efficiency and effectiveness of our proposed model, and results show
that: (1) when occurring a disruption event during peak hours, the impact on
the normal timetable is greater, and passengers in the direction with fewer
train services are more affected; (2) if passengers stranded at the terminal
stations of disruption area are not transported in time, they will rapidly
increase at a speed of more than 300 passengers per minute; (3) compared with
the fixed shortest path, using the response vehicles reduces the total travel
time about 7%. However, it results in increased travel time for some
passengers.Comment: 32 pages, 21 figure
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