Online distributed cooperative model predictive control of energy-saving trajectory planning for multiple high-speed train movements

Xiao, Yan; Cai, Baigen; Ning, Bin; Shangguan, Wei

doi:10.1016/j.trc.2016.05.019

Cited by 67 publications

(29 citation statements)

References 37 publications

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“…Chevrier et al [1], Shoichiro and Koseki [2], Yang et al [3,4], Wang and Goverde [5], Ning et al [6], Zhang et al [7] and Yang et al [8] optimized the train timetable by adjusting the train running path, the arrival and departure time or the passing time of the train, and realized energy-saving operation. Albrecht et al [9][10][11], Scheepmaker and Goverde [12], Yan et al [13], Ye and Liu [14], Luan et al [15], Yang et al [16], Fernández-Rodríguez et al [17] under the constraints of train characteristics, ramps, curves and speed limits, achieved optimal operating conditions and reduced energy consumption by adjusting the acceleration, cruising, coasting and braking phase.…”

Section: Introductionmentioning

confidence: 99%

“…Fernández-Rodríguez et al [17] used detailed train simulation models to nd energy-saving speed curves. Yan et al [13] used the ant colony algorithm to obtain energy-saving operation modes of each train under di erent conditions by exchanging the trajectories of multi high-speed trains. Albrecht et al [9] looked for optimal operating conditions for leading and following trains on at railway by existing train intervals.…”

Section: Introductionmentioning

confidence: 99%

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Optimal High-Speed Railway Timetable by Stop Schedule Adjustment for Energy-Saving

Chen

et al. 2019

Journal of Advanced Transportation

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Timetable optimization techniques offer opportunity for saving energy and hence reducing operational costs for high-speed rail services. The existing energy-saving timetable optimization is mainly concentrated on the train running state adjustment and the running time redistribution between two stations. Not only the adjustment space of timetables is limited, but also it is hard for the train to reach the optimized running state in reality, and it is difficult to get feasible timetable with running time redistribution between two stations for energy-saving. This paper presents a high-speed railway energy-saving timetable based on stop schedule optimization. Under the constraints of safety interval and stop rate, with the objective of minimizing the increasing energy consumption of train stops and the shortest travel time of trains, the high-speed railway energy-saving timetable optimization model is established. The fuzzy mathematics programming method is used to design an efficient algorithm. The proposed model and algorithm are demonstrated in the actual operation data of Beijing-Shanghai high-speed railway. The results show that the total operating energy consumption of the train is reduced by 3.7%, and the total travel time of the train is reduced by 11 minutes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal High-Speed Railway Timetable by Stop Schedule Adjustment for Energy-Saving

Chen

et al. 2019

Journal of Advanced Transportation

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“…Prognostics and Health Management (PHM) is considered as an important and efficient way for increasing the safety, benefit, reliability and efficiency of transportation systems, relying on past and current information on environmental, operational and usage records to detect [30] and diagnose [17] degradation, to predict future conditions [23] and schedule proper maintenance interventions [37], [42]. By the recording of data on the system conditions, data-driven methods have been widely integrated for analyzing and managing faults and accidents in transportation systems [28], [31], [41].…”

Section: Introductionmentioning

confidence: 99%

KNN-FSVM for Fault Detection in High-Speed Trains

Liu

Zio

2018

2018 IEEE International Conference on Prognostics and Health Management (ICPHM)

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Prognostics and health management can improve the reliability and safety of transportation systems. Data collected from diverse sources provide a chance and at the same time a challenge for data-driven PHM methods and models. The data often exhibit challenging characteristics like imbalanced data on normal and faulty conditions, noise and outliers, data points of different importance for the data-driven model, etc. In this paper, a k nearest neighbors-based fuzzy support vector machine is proposed for reducing the computational burden and tackling the issue of imbalance and outlier data, in fault detection. Fault detection is mathematically a classification problem. In this paper, the reverse nearest neighbors technique is adopted for detecting outliers and the k nearest neighbors technique is used to identify the borderline points for defining the classification hyperplane in support vector machines. Considering the position of each data point and the distribution of its nearest neighbors, a new method is proposed for calculating their estimation error costs. A real case study concerning fault detection in a braking system of a highspeed train is considered.

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“…In this case, the trajectories of both trains would be better optimised simultaneously. Research on this issue is still limited so far, as noted in A̧ıkba̧ and S̈ylemez (2008), Albrecht et al (2015a), Lu and Feng (2011), Miyatake and Ko (2010), Wang et al (2014), Yan et al (2016) and Zhao et al (2015). Specifically, Lu and Feng (2011) used genetic algorithm to optimise the control strategies of leading and following trains in a four-aspect fixed-block signalling system.…”

Section: Introductionmentioning

confidence: 99%

“…Albrecht et al (2015a) provided theoretical analysis and solution algorithm for the train separation problem based on the optimal control theory, in which two trains were operated under the fixed-block system with specified starting and finishing times on level tracks without speed limit. Yan et al (2016) developed an online distributed cooperative approach for optimising the control of multiple trains based on model predictive control and ant colony optimisation, where the trains are assumed to share their states and decisions with each other through radio.…”

Section: Introductionmentioning

confidence: 99%

A multiphase optimal control method for multi-train control and scheduling on railway lines

Liu

2016

Transportation Research Part B: Methodological

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We consider a combined train control and scheduling problem involving multiple trains in a railway line with a predetermined departure/arrival sequence of the trains at stations and meeting points along the line. The problem is formulated as a multiphase optimal control problem while incorporating complex train running conditions (including undulating track, variable speed restrictions, running resistances, speed-dependent maximum tractive/braking forces) and practical train operation constraints on departure/arrival/running/dwell times.Two case studies are conducted. The first case illustrates the control and scheduling problem of two trains in a small artificial network with three nodes, where one train follows and overtakes the other. The second case optimizes the control and timetable of a single train in a subway line. The case studies demonstrate that the proposed framework can provide an effective approach in solving the combined train scheduling and control problem for reducing energy consumption in railway operations.

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Online distributed cooperative model predictive control of energy-saving trajectory planning for multiple high-speed train movements

Cited by 67 publications

References 37 publications

Optimal High-Speed Railway Timetable by Stop Schedule Adjustment for Energy-Saving

Optimal High-Speed Railway Timetable by Stop Schedule Adjustment for Energy-Saving

KNN-FSVM for Fault Detection in High-Speed Trains

A multiphase optimal control method for multi-train control and scheduling on railway lines

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