Combining robustness and recovery in rapid transit network design

Abstract

When designing a transport network, decisions are made according to an expected value for network state variables, such as infrastructure and vehicle conditions, which are uncertain at a planning horizon of up to decades. Because disruptions, such as infrastructure breakdowns, will arise and affect the network on the day of operations, actions must be taken from the network design. Robust network designs may be implemented but they are extremely expensive if disruptions do not realise. In this paper, we propose a novel approach to the network design problem where robustness and recovery are combined. We look for the trade-off between efficiency and robustness accounting for the possibility of recovering from disruptions: recoverable robust network design. Computational experiments drawn from fictitious and realistic networks show how the presented approach reduces the price of robustness and recovery costs as compared to traditional robust and non-robust rapid transit network design approaches.

Protecting and Expanding Railway Networks to Hedge against Disruptions: A Model with Travel Segments

“…Among them, Laporte et al [58] propose an integer program to design a robust railway that provides different routes to address arc failures and limit congestion. Cadarso and Marín [59] introduce a network design model to trade-off between robustness and recovery of a transportation system subject to a set of disruption scenarios. Luo and Xu [60] propose a stochastic model with the aim of minimizing the postdisruption unsatisfied demand of a rail-bus system, by integrating the original network with new additional bus connections.…”

A Disruption Recovery Model for Time-Dependent Vehicle Routing Problem With Time Windows in Delivering Perishable Goods

“…Still and all, certain unexpected disruptive events may cause troubles for vehicle travelling en route, such as terrible traffic block, vehicle breakdown, which would result in enormous transportation delay leading the initial routing plan no longer optimal or even infeasible. When such disruptions occur, a disruption recovery scheme must be generated rapidly based on the instant situation [4]. Fortunately, resorting to advanced equipment and communication technologies, including GPS/GIS/GPRS, site data related to traffic and vehicles can be acquired in real-time [5], and efficient vehicle rerouting decisions can be provided for drivers in time.…”

Assessing road network vulnerability: A user equilibrium interdiction model

“…The problem can be inspected from different angles. For example, some researchers have focused on the design stage, to build infrastructure that are inher-ently resilient (Cadarso and Marín, 2016;Laporte et al, 2011;Martel et al, 2013;Tran et al, 2017). Focusing on road networks, design models have been studied investigating aspects such as robustness and resiliency in presence of different types of uncertainties.…”