A Framework for Modeling Rail Transport Vulnerability

Abstract

Railroads represent one of the most efficient methods of long-haul transport for bulk commodities, from coal to agricultural products. Over the past 50 years, the rail network has contracted while tonnage has increased. Service, geographically, has been abandoned along short-haul routes and increased along major long-haul routes, resulting in a network that is more streamlined. The current rail network may be very vulnerable to disruptions, like the failure of a trestle. This paper proposes a framework to model rail network vulnerability and gives an application of this modeling framework in analyzing rail network vulnerability for the State of Washington. It concludes with a number of policy-related issues that need to be addressed in order to identify, plan, and mitigate the risks associated with the sudden loss of a bridge or trestle. Copyright (c) 2008 Copyright the Authors. Journal compilation (c) 2008 Wiley Periodicals, Inc..

Vulnerability and resilience of transport systems – A discussion of recent research

“…Selecting links according to the product of original link disruption probability and the number of trains using the link (a kind of betweenness measure) turned out to the most effective mitigation strategy. Peterson and Church (2008) develop a framework for modelling rail freight transport vulnerability in much the same way as Jenelius et al (2006) and Scott et al (2006) for road networks. The authors apply a simple routing model (a slight generalisation of the classical shortest-path algorithm) to find the best way of routing a shipment in the original network and rerouting it after a certain link has been disrupted.…”

Impact of transalpine rail network disruptions: A study of the Brenner Pass

“…In order for this method to be applicable, the following characteristics have to be met: (i) a high economic importance, e.g., high shipment volumes, to the study region via the disrupted railway link; (ii) alternative routes cause long detours and/or are highly utilized; (iii) openly available network data, e.g., OSM-data, are of high quality; and (iv) a threat of a disruptions is imminent. Potential examples include various mountain passes such as the 'Gotthard', 'Simplon' and 'Semmering' mountain passes in Central Europe as well as bridges (e.g., (Peterson and Church, 2008)) and coastal lines (e.g., (Dawson et al, 2016)) of high economic importance. Table 3 Average ADDT and TDDT per replication run resulting from a 24-hour disruption at the 'Brenner Pass' for industries located in Tyrol (sorted in ascending manner by the rail distance to the 'Brenner Pass').…”

“…By simulating a possible man-caused disruption, vulnerabilities and criticality of network links are highlighted. In the context of rail operations, a framework to model rail transport vulnerabilities is proposed in (Peterson and Church, 2008). Network and demand data are combined with routing models to investigate the impact of disruptions.…”

Location Science

“…Church (2008) proposed the BEAMR approach which attempts to eliminate some of the assignment variables. BEAMR attempts to utilize only the h j closest assignment variables for each demand node.…”