Resilience of Railway Transport to Four Types of Natural Hazards: An Analysis of Daily Train Volumes
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 operation...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Infrastructures |
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| Online Access: | https://www.mdpi.com/2412-3811/6/12/174 |
| _version_ | 1797503653062377472 |
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| author | Vigile Marie Fabella Sonja Szymczak |
| author_facet | Vigile Marie Fabella Sonja Szymczak |
| author_sort | Vigile Marie Fabella |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T03:53:47Z |
| format | Article |
| id | doaj.art-85623328b96d4fd1906c062c57271038 |
| institution | Directory Open Access Journal |
| issn | 2412-3811 |
| language | English |
| last_indexed | 2024-03-10T03:53:47Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Infrastructures |
| spelling | doaj.art-85623328b96d4fd1906c062c572710382023-11-23T08:51:57ZengMDPI AGInfrastructures2412-38112021-12-0161217410.3390/infrastructures6120174Resilience of Railway Transport to Four Types of Natural Hazards: An Analysis of Daily Train VolumesVigile Marie Fabella0Sonja Szymczak1German Centre for Rail Traffic Research, 01219 Dresden, GermanyGerman Centre for Rail Traffic Research, 01219 Dresden, GermanyA 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.https://www.mdpi.com/2412-3811/6/12/174railway trafficnatural hazardsresilienceeconometric modellingregression analysis |
| spellingShingle | Vigile Marie Fabella Sonja Szymczak Resilience of Railway Transport to Four Types of Natural Hazards: An Analysis of Daily Train Volumes Infrastructures railway traffic natural hazards resilience econometric modelling regression analysis |
| title | Resilience of Railway Transport to Four Types of Natural Hazards: An Analysis of Daily Train Volumes |
| title_full | Resilience of Railway Transport to Four Types of Natural Hazards: An Analysis of Daily Train Volumes |
| title_fullStr | Resilience of Railway Transport to Four Types of Natural Hazards: An Analysis of Daily Train Volumes |
| title_full_unstemmed | Resilience of Railway Transport to Four Types of Natural Hazards: An Analysis of Daily Train Volumes |
| title_short | Resilience of Railway Transport to Four Types of Natural Hazards: An Analysis of Daily Train Volumes |
| title_sort | resilience of railway transport to four types of natural hazards an analysis of daily train volumes |
| topic | railway traffic natural hazards resilience econometric modelling regression analysis |
| url | https://www.mdpi.com/2412-3811/6/12/174 |
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