Citation

Fabella VM, Szymczak S. Infrastructures (Basel) 2021; 6(12): e174.

Copyright

(Copyright © 2021, International Society for Maintenance And Rehabilitation of Transport Infrastructures, Publisher MDPI: Multidisciplinary Digital Publications Institute)

DOI

10.3390/infrastructures6120174

PMID

unavailable

Abstract

A crucial step in measuring the resilience of railway infrastructure is to quantify the extent of its vulnerability to natural hazards. In this paper, we analyze the vulnerability of the German railway network to four types of natural hazards that regularly cause disruptions in German rail operations: floods, mass movements, slope fires, and tree falls. Using daily train traffic data matched with various data on disruptive events, we quantify the extent to which these four types of natural hazard reduce daily train traffic volumes. With a negative binomial count data regression, we find evidence that the track segments of the German railway network are most vulnerable to floods, followed by mass movements and tree-fall events. On average, floods reduce traffic on track segments by 19% of the average daily train traffic, mass movements by 16%, and tree fall by 4%. Moreover, when more than one type of natural hazard affects the track segment on the same day, train traffic on that segment falls by 34% of the average train traffic. Slope fires have an ambiguous and nonrobust effect on train traffic due to the reverse causality due to its triggering factors. This is the first study that attempts to rank different natural hazards according to their impact on railway traffic. The results have implications for the selection of resilience strategy and can help prioritize policy measures.

Language: en

Keywords

econometric modelling; natural hazards; railway traffic; regression analysis; resilience