A data-driven time supplements allocation model for train operations on high-speed railways

Huang, Ping; Wen, Chao; Peng, Qiyuan; Lessan, Javad; Fu, Liping; Jiang, Chaozhe

doi:10.1080/23248378.2018.1520613

Cited by 9 publications

(5 citation statements)

References 21 publications

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“…Then, the delayrecovery chains should be established to derive the effectiveness of buffer times. Because of the close relationship between buffer time utilization and dispatching strategies, the assessment of the dispatching strategies can be realized [160].…”

Section: B Further Discussion: Opportunities and Challenges 1) Ml/dmentioning

confidence: 99%

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Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal

Wen

Huang

et al. 2019

IEEE Access

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Train dispatching (TD) is at the forefront of all rail operations that transport passengers or goods. Recent technological advances and the explosion of digital data have introduced data-driven methods (DDMs) in rail operations. In this study, DDMs on the TD problem are briefly explored, focusing on relevant studies on delay distribution, delay propagation, and timetable rescheduling. Data-driven TD methods, including statistical methods (SM), graphical models (GM), and machine learning (ML) methods are reviewed. Then, key issues in establishing different data-driven models for the TD problem are addressed. Subsequently, ML methods are considered to be among the most promising DDMs that lead to innovative TD methods, relying on rich data obtained from train operations. This study emphasizes the potentials for designing new alternatives in the three key fields of interest and provides directions for further research on TD. Future research, including the ML-driven TD and intelligent TD, were discussed in this study. INDEX TERMS Data-driven, delay distribution, delay propagation, timetable rescheduling, train dispatching, machine learning.

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Section: B Further Discussion: Opportunities and Challenges 1) Ml/dmentioning

confidence: 99%

“…Huang et al [160] established a data-driven BTA model based on the Wuhan-Guangzhou high-speed railway. A ML model named ridge regression model is proposed to explain delay recovery time regarding buffer times at stations, buffer times in sections, and the severity of the primary delays.…”

Section: C: MLmentioning

confidence: 99%

Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal

Wen

Huang

et al. 2019

IEEE Access

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“…Zhang [9] summarized some strategies for train operation plan under mixed traffic condition and analyzed them from both pros and cons aspects, which limited to a qualitative analysis of the mixed traffic structure rather than a quantitative optimization. Huang [10] presented a time supplements allocation method that incorporates historical train operation data to optimize buffer-time distribution in the sections and stations of a published timetable. is paper studies the structure of the mixed-speed train traffic for a cyclic timetable in a passenger railway line section, integrating sequencing with headway optimization for train lines.…”

Section: Introductionmentioning

confidence: 99%

“…is paper studies the structure of the mixed-speed train traffic for a cyclic timetable in a passenger railway line section, integrating sequencing with headway optimization for train lines. As was defined by UIC [10], in our research, a railway line section is the part of a line in which the traffic mix and/or the number of trains or the infrastructure and signaling conditions do not change fundamentally. Infrastructure elements can be modeled by using resources with different properties in terms of capacity [11], one of which is the kind of infinite capacity resource with headway (ICR + H).…”

Section: Introductionmentioning

confidence: 99%

Structure Optimization of Mixed‐Speed Train Traffic for Cyclic Timetable: Model and Algorithm Development

Zhang

2022

Discrete Dynamics in Nature and Society

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Trains can be optimally spread over the period of the cyclic timetable. By integrating sequencing issue with headway time together, this paper studies the structure optimization of mixed-speed train traffic for a cyclic timetable. Firstly, by taking it as a job-shop problem with sequence-dependent setup times on one machine, in the type of infinite capacity resource with headway (ICR + H), the problem is transformed to alternative graph (AG) and then recast to the mixed-speed train traffic planning (MSTTP) model. For the multiobjective in MSTTP, three indicators are optimized, i.e., heterogeneity, cycle time, and buffer time, which correspond to diversity of train service toward passenger, capacity consumption of rail network, and stability of train operation, respectively. Secondly, the random-key genetic algorithm (RKGA) is proposed to tackle the sequence and headway simultaneously. Finally, RKGA is coded with visual studio C# and the proposed method is validated with a case study. The rail system considered is a line section encompassing a territory of 180 km with 15 mixed-speed trains in each cycle of the timetable. Results indicate the comprehensively balanced train plan for all stakeholders from random variations of train sequence and headway time. Both the quantitative proportion of heterogeneity/homogeneity (e.g., 2.5) about the optimized distribution of the mixed train traffic and the link between train headway time and the sequence for each traffic scenario are found. All the findings can be used to arrange the mixed-speed train traffic more scientifically.

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“…Khoshniyat and Peterson tested [42] a concept of travel time-dependent reserved minimum headways with modified buffer times in aperiodic timetables by taking into account uncertainty of train arrival times and stations capacity. Huang et al [43] incorporated real-world train operation data in buffer time allocation procedure for sections and stations to increase timetable robustness without capacity loss and better recover from delays. Yang et al [44] suggested buffer time allocation at stations based on the impact of delay on trains by integrating the operational performance data in their model of delay time distribution.…”

mentioning

confidence: 99%

Method for Designing Robust and Energy Efficient Railway Schedules

2021

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The robustness of the timetable is a sensitive issue in the daily realization of railway operations. As shown in the paper, robustness is a function of time reserves that helps to prevent unscheduled stops resulting from traffic disruptions and causing a higher energy consumption. The correct handling of time reserves while scheduling is a multidimensional issue, and it has a significant influence on the energy consumption of railway traffic. Therefore, the paper aims to show a simulation-based method, taking into account failure occurring probabilities and their consequences to get an acceptable level of robustness, that can be quantified by the probability of no delay propagation. This paper presents a method for the addition of time margins to the railway timetable. The iterative time buffer adding method is based on operational data as a knowledge source, to achieve the punctuality target. It was verified on a real railway line. An analysis of energy consumption for unscheduled train stops depending on the added buffer time was conducted after the literature review and the presentation of the evaluation model. The paper ends with discussion of the results and conclusions.

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A data-driven time supplements allocation model for train operations on high-speed railways

Cited by 9 publications

References 21 publications

Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal

Train Dispatching Management With Data- Driven Approaches: A Comprehensive Review and Appraisal

Structure Optimization of Mixed‐Speed Train Traffic for Cyclic Timetable: Model and Algorithm Development

Method for Designing Robust and Energy Efficient Railway Schedules

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