Joint optimal train regulation and passenger flow control strategy for high-frequency metro lines

Li, Shukai; Dessouky, Maged; Yang, Lixing; Gao, Ziyou

doi:10.1016/j.trb.2017.01.010

Cited by 146 publications

(54 citation statements)

References 35 publications

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“…Li et al [43] combine dynamic train regulation and passenger flow control to minimize the timetable and the headway deviations for metro lines, thereby reducing the operator profit loss and the passenger delay. The model predictive control and a quadratic programming algorithm are proposed to solve the problem, and stability analysis is conducted to verify the system convergence.…”

Section: Integrated Objectivesmentioning

confidence: 99%

Metro Timetabling for Time-Varying Passenger Demand and Congestion at Stations

Huang

Schonfeld

2018

Journal of Advanced Transportation

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For the train timetabling problem (TTP) in a metro system, the operator-oriented and passenger-oriented objectives are both important but partly conflicting. This paper aims to minimize both objectives by considering frequency (in the line planning stage) and train cost (in the vehicle scheduling stage). Time-varying passenger demand and train capacity are considered in a nonsmooth, nonconvex programming model, which is transformed into a mixed integer programming model with a discrete time-space graph (DTSG). A novel dwell time determining process considering congestion at stations is proposed, which turns the dwell times into dependent variables. In the solution approach, we decompose the TTP into a subproblem for optimizing segment travel times (OST) and a subproblem for optimizing departure headways from the shunting yard (OH). Branch-and-bound and frequency determining algorithms are designed to solve OST. A novel rolling optimization algorithm is designed to solve OH. The numerical experiments include case studies on a short metro line and Beijing Metro Line 4, as well as sensitivity analyses. The results demonstrate the predictive ability of the model, verify the effectiveness and efficiency of the proposed approach, and provide practical insights for different scenarios, which can be used for decision-making support in daily operations.

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Section: Integrated Objectivesmentioning

confidence: 99%

Metro Timetabling for Time-Varying Passenger Demand and Congestion at Stations

Huang

Schonfeld

2018

Journal of Advanced Transportation

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“…Moreover, the authors of [12] have developed a real-time control approach for a metro system which deals with stochastic passenger demand on the one side, and optimizes energy consumption, on the other side. However, the models [6], [11], [7], [8], [12] allow to represent only one train per inter-station [2], which limits their practical relevance, especially in networks with longer inter-station distances. The Max-plus approach features many advantages for the control of discrete event systems, like metro lines, which are discretized into segments.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

A discrete event traffic model explaining the traffic phases of the train dynamics on a linear metro line with demand-dependent control

Schanzenbacher¹,

Farhi

Leurent

et al. 2018

2018 Annual American Control Conference (ACC)

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In this paper we present a mathematical model of the train dynamics in a linear metro line system with demanddependent run and dwell times. On every segment of the line, we consider two main constraints. The first constraint is on the travel time, which is the sum of run and dwell time. The second one is on the safe separation time, modeling the signaling system, so that only one train can occupy a segment at a time. The dwell and the run times are modeled dynamically, with two control laws. The one on the dwell time makes sure that all the passengers can debark from and embark into the train. The one on the run time ensures train time-headway regularity in the case where perturbations do not exceed a run time margin.We use a Max-plus algebra approach which allows to derive analytic formulas for the train time-headway and frequency depending on the number of trains and on the passenger demand. The analytic formulas, illustrated by 3D figures, permit to understand the phases of the train dynamics of a linear metro line being operated as a transport on demand system.

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“…Lately, the authors of [12] have applied this model predictive control algorithm to a part of a mass transit railway line in the area of Paris. Still on the same basis, the authors of [13], [14] have proposed a traffic control algorithm for high density metro lines, taking into account the passenger demand.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Real-time control of metro train dynamics with minimization of train time-headway variance

Schanzenbacher¹,

Farhi

Leurent

et al. 2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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We present here a real-time control model for the train dynamics in a linear metro line system. The model describes the train dynamics taking into account average passenger arrival rates on platforms, including control laws for train dwell and run times, based on the feedback of the train dynamics. The model extends a recently developed Max-plus linear traffic model with demand-dependent dwell times and a run time control. The extension permits the elimination of eventual irregularities on the train time-headway. The resulting train dynamics are interpreted as a dynamic programming system of a stochastic optimal control problem of a Markov chain. The train dynamics still admit a stable stationary regime with a unique average growth rate interpreted as the asymptotic average train time-headway. Moreover, beyond the transient regime of the train dynamics, our extension guarantees uniformity in time of the train time-headways at every platform.

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Joint optimal train regulation and passenger flow control strategy for high-frequency metro lines

Cited by 146 publications

References 35 publications

Metro Timetabling for Time-Varying Passenger Demand and Congestion at Stations

Metro Timetabling for Time-Varying Passenger Demand and Congestion at Stations

A discrete event traffic model explaining the traffic phases of the train dynamics on a linear metro line with demand-dependent control

Real-time control of metro train dynamics with minimization of train time-headway variance

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