A discrete-event model of the train traffic on a linear metro line

Farhi, Nadir

doi:10.1016/j.apm.2021.03.012

Cited by 5 publications

(5 citation statements)

References 16 publications

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“…As a result, the headway is stabilized by allowing trains to run faster between stations. However, this constraint is relaxed by Farhi ( 10 ) to apply the model to lines where no speed change is possible for trains.…”

Section: Literature Reviewmentioning

confidence: 99%

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Impact of a First-In–First-Out Rule on the Merge of a Metro Line with a Junction

Farrando

Farhi

Christoforou

et al. 2023

Transportation Research Record: Journal of the Transportation R

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On a metro line with a junction, the merge of the line is a key point for the operation. This paper proposes a discrete event traffic model for a metro line with a junction. It is based on the line’s existing signaling system and determines trains’ departure times at all line nodes. Furthermore, the model estimates trains’ average headway and frequency. We apply our model to Paris metro line 13, which is currently not operated with a FIFO (first-in–first-out) rule on the merge. Thus, we seek to evaluate the effect of a FIFO rule on the nominal frequency. Compared to the current line operation, we also test the FIFO rule as a control strategy for daily disturbances. At the steady state, we show that the train frequency is maximized regardless of the distribution of the trains on the three parts of the line (the central part and two branches). Then, we study the train frequency in two disturbed situations that often occur on a metro line. We show that the FIFO rule reduces the impact of these disturbances in time and intensity. This research is motivated by the Paris metro operator, as the considered line is expected to be fully automated in the coming years. The operator wants to better understand the potential benefits of new operating rules and how operating protocols can improve the throughput and reliability of the system. This work is the first step in seeing frequency evolution on a line with a junction.

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Section: Literature Reviewmentioning

confidence: 99%

“…Our model uses the mathematical formulation developed by Farhi ( 10 ), Farhi et al ( 19 – 21 ), and Farrando et al ( 22 ). These papers study the frequency of trains on a loop (non-junction) metro line.…”

Section: Literature Reviewmentioning

confidence: 99%

Impact of a First-In–First-Out Rule on the Merge of a Metro Line with a Junction

Farrando

Farhi

Christoforou

et al. 2023

Transportation Research Record: Journal of the Transportation R

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“…Specifically, the train dynamics are represented by a discrete event system that captures various traffic constraints such as dwell and safe-separation time constraints [16][17][18]. Additional research has extended this approach to incorporate passenger demand [19][20][21][22][23][24], model traffic on mass transit lines with junctions [25][26][27], and simulate mass transit lines with skip-stop policies [28,29]. In our work, we extend the existing traffic model by introducing the control of passenger inflows to platforms.…”

Section: Overview Of the Proposed Approachmentioning

confidence: 99%

“…We extended the modeling of the train and passengers dynamics, as well as the modeling of their interactions, based on the works cited previously [17,23,24].…”

Section: Contributionsmentioning

confidence: 99%

Deep Reinforcement Q-Learning for Intelligent Traffic Control in Mass Transit

Khozam,

Farhi

2023

Sustainability

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Traffic control in mass transit consists of the regulation of both vehicle dynamics and passenger flows. While most of the existing approaches focus on the optimization of vehicle dwell time, vehicle time headway, and passenger stocks, we propose in this article an approach which also includes the optimization of the passenger inflows to the platforms. We developed in this work a deep reinforcement Q-learning model for the traffic control in a mass transit line. We first propose a new mathematical traffic model for the train and passengers dynamics. The model combines a discrete-event description of the vehicle dynamics, with a macroscopic model for the passenger flows. We use this new model as the environment of the traffic in mass transit for the reinforcement learning optimization. For this aim, we defined, under the new traffic model, the state variables as well as the control ones, including in particular the number of running vehicles, the vehicle dwell times at stations, and the passenger inflow to platforms. Second, we present our new deep Q-network (DQN) model for the reinforcement learning (RL) with the state representation, action space, and reward function definitions. We also provide the neural network architecture as well as the main hyper-parameters. Finally, we give an evaluation of the model under multiple scenarios. We show in particular the efficiency of the control of the passenger inflows into the platforms.

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“…Thus, headway control systems for metro lines addressing passenger demand variability have been proposed by several authors (e.g. Bueno-Cadena, Muñoz, & Tirachini, 2020;Carrel, Mishalani, Wilson, Attanucci, & Rahbee, 2010;Farhi, 2021;Meng & Zhou, 2011).…”

Section: Introductionmentioning

confidence: 99%

Headway variability in public transport: a review of metrics, determinants, effects for quality of service and control strategies

Tirachini

Godachevich²,

Cats

et al. 2021

Transport Reviews

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The most relevant issues related to headway variability in public transport planning, operations and quality of service are reviewed in this paper. We discuss the causes and consequences of headway variability, the alternative metrics that have been proposed to measure it, the preventive and reactive strategies to control headway variability in both research and practice, including the role of drivers and of present and future technology, and how service provision contracts might deal with headway variability through metrics and financial incentives. The most influential elements that explain headway variability along a route are the irregularity at which vehicles are dispatched, the scheduled frequency, the distance travelled or route length, the passenger demand and associated dwell times, and the number of stops. We conclude that there is a large gap between the state-of-the-art and the state-of-practice in terms of identification of headway variability issues, as well as in the development of mitigation and control measures. It is therefore paramount that future research will contribute to closing this gap by addressing organisational, contractual and technological barriers in the implementation of measures aimed at mitigating headway variability in public transport services.

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A discrete-event model of the train traffic on a linear metro line

Cited by 5 publications

References 16 publications

Impact of a First-In–First-Out Rule on the Merge of a Metro Line with a Junction

Impact of a First-In–First-Out Rule on the Merge of a Metro Line with a Junction

Deep Reinforcement Q-Learning for Intelligent Traffic Control in Mass Transit

Headway variability in public transport: a review of metrics, determinants, effects for quality of service and control strategies

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