Citation

Janic M. Transportation (Amst) 2018; 45(4): 1101-1137.

Affiliation

Air Transport and Operations Department, Faculty of Aerospace Engineering, Delft University of Technology, Stevinweg 1, 12628 BX Delft, The Netherlands.

Copyright

(Copyright © 2018, Holtzbrinck Springer Nature Publishing Group)

DOI

10.1007/s11116-018-9875-6

PMID

30956368

PMCID

PMC6417382

Abstract

This paper deals with modelling the dynamic resilience of rail passenger transport networks affected by large-scale disruptive events whose impacts deteriorate the networks' planned infrastructural, operational, economic, and social-economic performances represented by the selected indicators. The indicators of infrastructural performances refer to the physical and operational conditions of the networks' lines and stations, and supportive facilities and equipment. Those of the operational performances include transport services scheduled along particular routes, their seating capacity, and corresponding transport work/capacity. The indicators of economic performances include the costs of cancelled and long-delayed transport services imposed on the main actors/stakeholder involved-the rail operator(s) and users/passengers. The indicators of social-economic performances reflect the compromised accessibility and consequent prevention of the user/passenger trips and their contribution to the local/regional/national Gross Domestic Product. Modeling resulted in developing a methodology including two sets of analytical models for: (1) assessing the dynamic resilience of a given rail network, i.e., before, during, and after the impacts of disruptive event(s); and (2) estimation of the indicators of particular performances as the figures-of-merit for assessing the network's resilience under the given conditions. As such, the methodology could be used for estimating the resilience of different topologies of rail passenger networks affected by past, current, and future disruptive events, the latest according to the "what-if" scenario approach and after introducing the appropriate assumptions. The methodology has been applied to a past case-the Japanese Shinkansen HSR network affected by a large-scale disruptive event-the Great East Japan Earthquake on 11 March 2011.

Language: en

Keywords

HSR (high speed rail) case; Indicators; Performances; Rail passenger transport networks; Resilience