Smooth and Controlled Recovery Planning of Disruptions in Rapid Transit Networks

Cadarso, Luís; Maróti, Gábor; Marı́n, Ángel

doi:10.1109/tits.2015.2399975

Cited by 27 publications

(12 citation statements)

References 22 publications

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“…Veelenturf et al (8) integrate the rescheduling of the rolling stock and the determination of a disposition timetable, by taking iterative passenger demand into account. A similar approach proposed in Cadarso et al (9) considers passenger flows as dynamic; passengers can update their route in a railway network in reaction to a disruption. Parbo et al (10) propose a genetic algorithm to reduce passengers' waiting times by changing the departure times of buses; the solutions are evaluated using a detailed passenger assignment model.…”

Section: Literature Reviewmentioning

confidence: 99%

Role of Timetable, Rolling Stock Rescheduling, and Information Strategies to Passengers in Public Transport Disruptions

Leng

Liao

Corman

2020

Transportation Research Record: Journal of the Transportation R

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In the event of public transport disruption, operating companies produce disposition timetables depending on different rescheduling strategies, such as retiming or rerouting, with services fully/partially cancelled, and also taking into account more complex, adjusted, feasible rolling stock circulation. The aim is to reduce passengers’ delays, thereby limiting detriment to passengers’ activities and their related satisfaction. The key relation between the supply of operating companies and passengers’ satisfaction is information disseminated about running services. This paper innovatively combines an optimization model and an agent-based micro-simulation model (MATSim) to explore passengers’ (dis)satisfaction with different disposition timetables and information strategies, which is helpful for operating companies to offer better services to passengers in cases of public transport disruption. Activity-based agent behaviors in a multi-modal network are simulated and agents’ delays and scores for the city of Zürich, Switzerland, analyzed. Passengers’ (dis)satisfaction is indicated by their delays in the directly affected (i.e., disrupted) trip and utility for their whole trips and activities estimated by a score function. Disruption results in immediate delays for passengers whose planned services fail to run, plus delays for passengers on the line where extra services are planned to run (rerouted). The earlier information on the disposition timetable is disseminated to passengers, the higher their satisfaction during disruption. Compared with full cancellation of train services, computing a precise feasible rolling stock circulation able to handle partial train cancellations can significantly benefit passengers, especially those whose planned services are disrupted, against minor delays incurred by other group of passengers.

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Section: Literature Reviewmentioning

confidence: 99%

Role of Timetable, Rolling Stock Rescheduling, and Information Strategies to Passengers in Public Transport Disruptions

Leng

Liao

Corman

2020

Transportation Research Record: Journal of the Transportation R

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“…Considering rotating maintenance, rolling stock circulation plans and a long-term maintenance policy, Canca et al [22] proposed a mixed integer programming model to minimize wasted capacity caused by train empty movements and to balance the workload of the maintenance operation. Cadarso et al [23] presented an integrated model for the recovery of the timetable and the rolling stock schedules, so that the recovery schedules could be easily implemented in practice and the operations could quickly return to the originally planned schedules after the recovery period. Cadarso et al [24] proposed an approach synthetically considering the influence of passengers' behavior, the timetable and rolling stock.…”

Section: Literature Reviewmentioning

confidence: 99%

Urban Rail Timetable Optimization to Improve Operational Efficiency with Flexible Routing Plans: A Nonlinear Integer Programming Model

Xue

Yang

et al. 2019

Sustainability

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At present, most urban rail transit systems adopt an operation mode with a single long routing. The departure frequency is determined by the maximum section passenger flow. However, when the passenger flow varies greatly within different sections, this mode will lead to a low load factor in some sections, resulting in a waste of capacity. In view of this situation, this paper develops a nonlinear integer programming model to determine an optimal timetable with a balanced scheduling mode, where the wasted capacity at a constant departure frequency can be reduced with a slight increase in passenger waiting time. Then, we simplify the original model into a single-objective integer optimization model through normalization. A genetic algorithm is designed to find the optimal solution. Finally, a numerical example is presented based on real-world passenger and operation data from Beijing Metro Line 4. The results show that the double-routing optimization model can reduce wasted capacity by 9.5%, with a 4.5% increase in passenger waiting time, which illustrates the effectiveness of this optimization model.

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“…Works which, in addition to passenger needs, considered the operational costs of train companies are those proposed by [106,161]. In this context, it is also worth citing [162] which computed different measures of costs resulting from the disruption management process, such as total operational cost for passenger services, total operational cost for empty movements and total number of schedule changes (i.e. services, composition and inventory train changes), as indicators of the effort made by rail companies to put recovery strategies in place.…”

Section: The Rescheduling Problemmentioning

confidence: 99%

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

Botte

D’Acierno

2018

Urban Rail Transit

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The paper aims to provide an overview of the key factors to consider when performing reliable modelling of rail services. Given our underlying belief that to build a robust simulation environment a rail service cannot be considered an isolated system, also the connected systems, which influence and, in turn, are influenced by such services, must be properly modelled. For this purpose, an extensive overview of the rail simulation and optimisation models proposed in the literature is first provided. Rail simulation models are classified according to the level of detail implemented (microscopic, mesoscopic and macroscopic), the variables involved (deterministic and stochastic) and the processing techniques adopted (synchronous and asynchronous). By contrast, within rail optimisation models, both planning (timetabling) and management (rescheduling) phases are discussed. The main issues concerning the interaction of rail services with travel demand flows and the energy domain are also described. Finally, in an attempt to provide a comprehensive framework an overview of the main metaheuristic resolution techniques used in the planning and management phases is shown.

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Smooth and Controlled Recovery Planning of Disruptions in Rapid Transit Networks

Cited by 27 publications

References 22 publications

Role of Timetable, Rolling Stock Rescheduling, and Information Strategies to Passengers in Public Transport Disruptions

Role of Timetable, Rolling Stock Rescheduling, and Information Strategies to Passengers in Public Transport Disruptions

Urban Rail Timetable Optimization to Improve Operational Efficiency with Flexible Routing Plans: A Nonlinear Integer Programming Model

Dispatching and Rescheduling Tasks and Their Interactions with Travel Demand and the Energy Domain: Models and Algorithms

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