Robust cooperative train trajectory optimization with stochastic delays under virtual coupling

Wang, Pengling; Zhu, YC Youcai; Zhu, Wei

doi:10.1049/itr2.12333

Cited by 10 publications

(2 citation statements)

References 28 publications

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“…The paper [124] investigated the cooperative train control problem under VC with consideration of the impact of stochastic initial delays and proposed MILP-based models for estimating the delay impact, detecting coupled running sections, and optimizing cooperative train trajectories. Moreover, reference [125] proposed an optimal control method based on the linear quadratic Gaussian (LQG) controller.…”

Section: Optimization and Optimal Controlmentioning

confidence: 99%

Virtual Coupling in Railways: A Comprehensive Review

Félez

Vaquero-Serrano

2023

Machines

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The current mobility situation is constantly changing as people are increasingly moving to urban areas. Therefore, a flexible mode of transport with high-capacity passenger trains and a high degree of modularity in the trains’ composition is necessary. Virtual coupling (VC) is a promising solution to this problem because it significantly increases the capacity of a line and provides a more flexible mode of operation than conventional signaling systems. This novel review, in which approximately 200 papers were analyzed, identifies the main topics of current railway-related VC research, and represents the first step toward the implementation of VC in future railways. It was found that industry research has mainly focused on the feasibility of VC implementation and operation, whereas in academia, which is coordinated with industry, research has focused on control and communication systems. From a technological perspective, the main challenges for VC were identified with regard to aspects such as safety, control technology, interlocking, vehicle-to-vehicle communication, cooperative train protection and control, and integrated traffic management. The important directions for future research that have been identified for future development include complete dynamic models, real-time controllers, reliable and secure communication, different communication topologies, cybersecurity, intelligent control, reinforcement learning, and Big Data analytics.

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Section: Optimization and Optimal Controlmentioning

confidence: 99%

Virtual Coupling in Railways: A Comprehensive Review

Félez

Vaquero-Serrano

2023

Machines

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“…This framework inherits the advantages of existing hierarchical structures in metros, where the reference trajectories are optimized considering specific objectives, for example, energy saving (Su et al., 2021; X. Wang et al., 2021) and delay recovery (P. Wang & Goverde, 2019; P. Wang et al., 2023).…”

Section: Hierarchical Control Approachmentioning

confidence: 99%

A hierarchical control approach for virtual coupling in metro trains

Liu,

Luo,

Tang

et al. 2023

Computer aided Civil Eng

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As an emerging technology, virtual coupling improves the efficiency and flexibility of metro services by forming multiple trains (units) as a virtually coupled train set (VCTS) without mechanical couplers. However, to realize the desired VCTS operation in practical metro services, a significant gap to be filled is that the implicit and nonlinear safety constraints are hard to be addressed in real‐time control. Thus, this paper proposes a hierarchical control approach with a two‐layer framework. In the upper layer, a trajectory planning method is designed, which addresses the safety constraints and prescribes a reference trajectory for each unit. In the lower layer, a model predictive control approach is constructed, by which each unit accurately tracks its reference trajectory in real time. Afterward, the proposed hierarchical control approach is employed to realize VCTS inter‐station operation in field tests. The average tracking error of speed is around 8 cm/s and the stopping error is less than 12 cm. The following distance between two units in VCTS is reduced to less than 105 m at the speed of 80 km/h, which is a breakthrough in the development of VCTS.

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Enhancing freight train delay prediction with simulation‐assisted machine learning

Minbashi,

Zhao,

Dick

et al. 2024

IET Intelligent Trans Sys

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Boosting the rail freight modal share is an ambitious target in Europe and North America. Yards, where freight trains are arranged, can be crucial in realizing this target by reliable dispatching to the network. This paper predicts freight train departures by developing a simulation‐assisted machine learning model with two concepts: general (adding all predictors at once) and step‐wise (adding predictors as they become available in sub‐yard operations) for hump yards with the conventional layout to provide a generalized model for European and North American contexts. The developed model is a decision tree algorithm, validated via 10‐fold cross‐validation. The model's performance on three data sets—a real‐world European yard, a baseline simulation, and an ultimate randomness simulation for a comparable North American yard—shows a respective of 0.90, 0.87, and 0.70. Step‐wise inclusion of the predictors results differently for the real‐world and simulation data. The global feature importance highlights maximum planned length, departure weekday, the number of arriving trains, and minimum arrival deviation as key predictors for the real‐world data. For the simulation data, the most significant predictors are departure yard predictors, the number of arriving trains, and the maximum hump duration. Additionally, utilization rates—except for the receiving yard—enhance the predictions.

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Robust cooperative train trajectory optimization with stochastic delays under virtual coupling

Cited by 10 publications

References 28 publications

Virtual Coupling in Railways: A Comprehensive Review

Virtual Coupling in Railways: A Comprehensive Review

A hierarchical control approach for virtual coupling in metro trains

Enhancing freight train delay prediction with simulation‐assisted machine learning

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