737 research outputs found
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
An Integrated Framework Integrating Monte Carlo Tree Search and Supervised Learning for Train Timetabling Problem
The single-track railway train timetabling problem (TTP) is an important and
complex problem. This article proposes an integrated Monte Carlo Tree Search
(MCTS) computing framework that combines heuristic methods, unsupervised
learning methods, and supervised learning methods for solving TTP in discrete
action spaces. This article first describes the mathematical model and
simulation system dynamics of TTP, analyzes the characteristics of the solution
from the perspective of MCTS, and proposes some heuristic methods to improve
MCTS. This article considers these methods as planners in the proposed
framework. Secondly, this article utilizes deep convolutional neural networks
to approximate the value of nodes and further applies them to the MCTS search
process, referred to as learners. The experiment shows that the proposed
heuristic MCTS method is beneficial for solving TTP; The algorithm framework
that integrates planners and learners can improve the data efficiency of
solving TTP; The proposed method provides a new paradigm for solving TTP
Solving the single-track train scheduling problem via Deep Reinforcement Learning
Every day, railways experience small inconveniences, both on the network and
the fleet side, affecting the stability of rail traffic. When a disruption
occurs, delays propagate through the network, resulting in demand mismatching
and, in the long run, demand loss. When a critical situation arises, human
dispatchers distributed over the line have the duty to do their best to
minimize the impact of the disruptions. Unfortunately, human operators have a
limited depth of perception of how what happens in distant areas of the network
may affect their control zone. In recent years, decision science has focused on
developing methods to solve the problem automatically, to improve the
capabilities of human operators. In this paper, machine learning-based methods
are investigated when dealing with the train dispatching problem. In
particular, two different Deep Q-Learning methods are proposed. Numerical
results show the superiority of these techniques respect to the classical
linear Q-Learning based on matrices.Comment: 12 pages, 4 figures (2 b&w
Hybrid railway vehicle trajectory optimisation using a nonâconvex function and evolutionary hybrid forecast algorithm
AbstractThis paper introduces a novel optimisation algorithm for hybrid railway vehicles, combining a nonâlinear programming solver with the highly efficient âMayfly Algorithmâ to address a nonâconvex optimisation problem. The primary objective is to generate efficient trajectories that enable effective power distribution, optimal energy consumption, and economical use of multiple onboard power sources. By reducing unnecessary load stress on power sources during peak time, the algorithm contributes to lower maintenance costs, reduced downtime, and extended operational life of these sources. The algorithm's design considers various operational parameters, such as power demand, regenerative braking, velocity and additional power requirements, enabling it to optimise the energy consumption profile throughout the journey. Its adaptability to the unique characteristics of hybrid railway vehicles allows for efficient energy management by leveraging its hybrid powertrain capabilities.</jats:p
Interactive reinforcement learning innovation to reduce carbon emissions in railway infrastructure maintenance
Carbon emission is one of the primary contributors to global warming. The global community is paying great attention to this negative impact. The goal of this study is to reduce the negative impact of railway maintenance by applying reinforcement learning (RL) by optimizing maintenance activities. Railway maintenance is a complex process that may not be efficient in terms of environmental aspect. This study is the world's first to use the potential of RL to reduce carbon emission from railway maintenance. The data used to create the RL model are gathered from the field data between 2016â019. The study section is 30Â km long. Proximal Policy Optimization (PPO) is applied in the study to develop the RL model. The results demonstrate that using RL reduces carbon emission from railway maintenance by 48%, which generates a considerable amount of carbon emission reduction and reduces railway defects by 68%, which also improves maintenance efficiency significantly
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