A survey on robustness in railway planning

Lusby, Richard Martin; Larsen, Jesper; Bull, Simon Henry

doi:10.1016/j.ejor.2017.07.044

Cited by 98 publications

(44 citation statements)

References 79 publications

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“…Following Cacchiani and Toth [2012], the most prominent are that the timetable should be (1) 'efficient' and (2) 'robust'. Overviews on approaches to deal with robustness can be found in Cacchiani and Toth [2012], Lusby et al [2018]. Efficiency can be aimed both at costs and travel time aspects (or a trade-off of both).…”

Section: Timetabling and Passenger Routingmentioning

confidence: 99%

Timetabling for Strategic Passenger Railway Planning

Polinder

Schmidt²,

Huisman

2020

SSRN Journal

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In research and practice, public transportation planning is executed in a series of steps, which are often divided into the strategic, the tactical, and the operational planning phase. Timetables are normally designed in the tactical phase, taking into account a given line plan, safety restrictions arising from infrastructural constraints, as well as regularity requirements and bounds on transfer times. In this paper, however, we propose a timetabling approach that is aimed at decision making in the strategic phase of public transportation planning and to determine an outline of a timetable that is good from the passengers' perspective. Instead of including explicit synchronization constraints between train runs (as most timetabling models do), we include the adaption time (waiting time at the origin station) in the objective function to ensure regular connections between passengers' origins and destinations. We model the problem as a mixed integer quadratic program and linearise it. Furthermore we propose a heuristic to generate starting solutions. We illustrate the type of solutions found by our approach on two case studies based on the Dutch railway network and analyse trade-offs that are made to balance dwell times and regularity of trains.

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Section: Timetabling and Passenger Routingmentioning

confidence: 99%

Timetabling for Strategic Passenger Railway Planning

Polinder

Schmidt²,

Huisman

2020

SSRN Journal

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“…The past decades have seen an accelerated development within network interdiction. Recently, network interdiction models have been widely applied to diverse areas involving competition, such as supply chain networks [15], counter-terrorism operations [16], facilities fortification [17], nuclear-weapons project interdiction [18], transportation networks [19], and electrical grid analysis [3,20]. The shortest-path network interdiction is a typical representative network interdiction problem [11].…”

Section: Related Workmentioning

confidence: 99%

A Decomposition Approach for Stochastic Shortest-Path Network Interdiction with Goal Threshold

Wei

Jiao

et al. 2019

Symmetry

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Shortest-path network interdiction, where a defender strategically allocates interdiction resource on the arcs or nodes in a network and an attacker traverses the capacitated network along a shortest s-t path from a source to a terminus, is an important research problem with potential real-world impact. In this paper, based on game-theoretic methodologies, we consider a novel stochastic extension of the shortest-path network interdiction problem with goal threshold, abbreviated as SSPIT. The attacker attempts to minimize the length of the shortest path, while the defender attempts to force it to exceed a specific threshold with the least resource consumption. In our model, threshold constraint is introduced as a trade-off between utility maximization and resource consumption, and stochastic cases with some known probability p of successful interdiction are considered. Existing algorithms do not perform well when dealing with threshold and stochastic constraints. To address the NP-hard problem, SSPIT-D, a decomposition approach based on Benders decomposition, was adopted. To optimize the master problem and subproblem iteration, an efficient dual subgraph interdiction algorithm SSPIT-S and a local research based better-response algorithm SSPIT-DL were designed, adding to the SSPIT-D. Numerical experiments on networks of different sizes and attributes were used to illustrate and validate the decomposition approach. The results showed that the dual subgraph and better-response procedure can significantly improve the efficiency and scalability of the decomposition algorithm. In addition, the improved enhancement algorithms are less sensitive and robust to parameters. Furthermore, the application in a real-world road network demonstrates the scalability of our decomposition approach.

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“…A stochastic programming model was developed to identify robust schedules for every period. We refer to two comprehensive surveys by Cacchiani and Toth (2012) and Lusby and Larsen (2018) that address the nominal and robust train planning and timetabling problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Balancing a one‐way corridor capacity and safety‐oriented reliability: A stochastic optimization approach for metro train timetabling

Yin

Yang

Zhou

et al. 2019

Naval Research Logistics

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In urban rail transit systems of large cities, the headway and following distance of successive trains have been compressed as much as possible to enhance the corridor capacity to satisfy extremely high passenger demand during peak hours. To prevent train collisions and ensure the safety of trains, a safe following distance of trains must be maintained. However, this requirement is subject to a series of complex factors, such as the uncertain train braking performance, train communication delay, and driver reaction time. In this paper, we propose a unified mathematical framework to analyze the safety‐oriented reliability of metro train timetables with different corridor capacities, that is, the train traffic density, and determine the most reliable train timetable for metro lines in an uncertain environment. By employing a space‐time network representation in the formulations, the reliability‐based train timetabling problem is formulated as a nonlinear stochastic programming model, in which we use 0‐1 variables to denote the time‐dependent velocity and position of all involved trains. Several reformulation techniques are developed to obtain an equivalent mixed integer programming model with quadratic constraints (MIQCP) that can be solved to optimality by some commercial solvers. To improve the computational efficiency of the MIQCP model, we develop a dual decomposition solution framework that decomposes the primal problem into several sets of subproblems by dualizing the coupling constraints across different samples. An exact dynamic programming combined with search space reduction strategies is also developed to solve the exact optimal solutions of these subproblems. Two sets of numerical experiments, which involve a relatively small‐scale case and a real‐world instance based on the operation data of the Beijing subway Changping Line are implemented to verify the effectiveness of the proposed approaches.

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A survey on robustness in railway planning

Cited by 98 publications

References 79 publications

Timetabling for Strategic Passenger Railway Planning

Timetabling for Strategic Passenger Railway Planning

A Decomposition Approach for Stochastic Shortest-Path Network Interdiction with Goal Threshold

Balancing a one‐way corridor capacity and safety‐oriented reliability: A stochastic optimization approach for metro train timetabling

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