Train Regulation Combined with Passenger Control Model Based on Approximate Dynamic Programming

Hao, Sijia; Song, Rui; He, Shiwei; Lan, Zekang

doi:10.3390/sym11030303

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…Hao et al [39] present a rescheduling approach based on a Markov decision process. It examines the interactive link between train operation and passenger behavior and reschedules trains considering dynamic passenger flow.…”

Section: Urban Rail Transit Reschedulingmentioning

confidence: 99%

A review of passenger-oriented railway rescheduling approaches

Sharma

Pellegrini

Rodríguez

et al. 2023

Eur. Transp. Res. Rev.

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Railway operations are highly susceptible to delays and disruptions caused by various factors, such as technical issues, operational inefficiencies, and unforeseen events. To counter these delays and ensure efficient railway operations during real-time management, several rescheduling approaches can be implemented. Among these approaches, passenger-oriented rescheduling considers train rescheduling while taking passenger data into account, as opposed to operation-oriented rescheduling. This paper provides an overview of the former group of approaches. Particular focus is put on different ways passenger data is exploited to optimize rescheduling and on the measures, the approaches can decide on. The rescheduling measures typically considered vary from decisions on maintaining transfers, canceling trains, adding emergency trains, changing routes and orders of trains, skipping or adding stops at stations, short-turning trains, applying speed control, and modifying rolling stock compositions. In this regard, the paper presents a comprehensive analysis of real-time rescheduling approaches adopted in both the conventional railway and urban rail transit and points out possible directions for further research in the field.

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Section: Urban Rail Transit Reschedulingmentioning

confidence: 99%

A review of passenger-oriented railway rescheduling approaches

Sharma

Pellegrini

Rodríguez

et al. 2023

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

show abstract

“…Focusing on the challenge of large passenger fow in interchange stations during peak periods, Li et al [32] constructed a collaborative optimization model of passenger fow control and train rescheduling with the goal of minimizing the weighted waiting time of passengers outside and inside stations. For large passenger fow scenarios, Hao et al [33] proposed a management strategy that can regulate the train operation time and control the number of passengers boarding a train with the help of the Markov decision process. Focusing on train delays of the high-demand and high-frequency line, Li et al [34] suggested a model predictive control-based method by taking the minimum of total delay as objective and the passenger control as constraints.…”

Section: Introductionmentioning

confidence: 99%

Joint Optimal Train Rescheduling and Passenger Flow Control for Speed Limit and High-Demand Scenarios of Urban Rail Transits

Yuan,

Zhao,

Chu

2023

Journal of Advanced Transportation

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In the operation of urban rail transits, train delays occur frequently, and emergency management is one of the key factors to ensure the service quality. For speed limit scenarios with high demand, this paper takes the train running in the restricted manual (RM) mode in the speed limit zone as an example and discusses a coping method by jointing train rescheduling and passenger flow control. With the goal of maximizing the number of passengers served and minimizing total train delay, a nonlinear optimization model is constructed by taking the train operation-related requirements and passenger flow control-related indicators as constraints, and the model is reformulated to a mixed-integer programming (MIP) model with quadratic constraints, which can be solved by the Gurobi solver. In order to obtain effective solutions faster, a two-stage approach is discussed, which first obtains a rescheduling timetable and then dynamically adjusts the requirement of boarding equalization to obtain the passenger flow control scheme. The validity of the model and the solution approach are discussed with the help of numerical experiments. The results suggest that the model and solution approach are feasible. When the number of trains is fixed, the reasonable implementation of passenger flow control will help to increase the number of passengers served, and the pursuit of a higher equalization of boarding is not conducive to the number of passengers served and the full utilization of transport capacity. The two-stage approach has certain advantages over the direct computing method. The methods in this paper have a guiding value for emergency decision-making in similar delay scenarios.

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“…As pioneer studies of metro train regulation, Campion et al [2] and Van Breusegem et al [3] addressed the state feedback control of metro lines without considering operational constrains. Due to the large-scale, nonlinear, constrained, and stochastic features of metro transportation systems [4][5][6], model predictive control (MPC) [7][8][9] has become a widely used method in solving the train regulation problem. Grube and Cipriano [10] compared MPC strategy with the heuristic rule-based strategy for metro train regulation, showing that MPC strategy has better performance.…”

Section: Introductionmentioning

confidence: 99%

Energy-Saving Train Regulation for Metro Lines Using Distributed Model Predictive Control

2020

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Due to environmental concerns, the energy-saving train regulation is necessary for urban metro transportation, which can improve the service quality and energy efficiency of metro lines. In contrast to most of the existing research of train regulation based on centralized control, this paper studies the energy-saving train regulation problem by utilizing distributed model predictive control (DMPC), which is motivated by the breakthrough of vehicle-based train control (VBTC) technology and the pressing real-time control demand. Firstly, we establish a distributed control framework for train regulation process assuming each train is self-organized and capable to communicate with its preceding train. Then we propose a DMPC algorithm for solving the energy-saving train regulation problem, where each train determines its control input by minimizing a constrained local cost function mainly composed of schedule deviation, headway deviation, and energy consumption. Finally, simulations on train regulation for the Beijing Yizhuang metro line are carried out to demonstrate the effectiveness of the proposed DMPC algorithm, and the results reveal that the proposed algorithm exhibits significantly improved real-time performance without deteriorating the service quality or energy efficiency compared with the centralized MPC method.

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Train Regulation Combined with Passenger Control Model Based on Approximate Dynamic Programming

Cited by 8 publications

References 28 publications

A review of passenger-oriented railway rescheduling approaches

A review of passenger-oriented railway rescheduling approaches

Joint Optimal Train Rescheduling and Passenger Flow Control for Speed Limit and High-Demand Scenarios of Urban Rail Transits

Energy-Saving Train Regulation for Metro Lines Using Distributed Model Predictive Control

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