Environmental and Operational Drivers of Vegetation Fires Along the Czech Rail Network

Vegetation growing along railway corridors creates conditions in which fires can ignite and spread rapidly, even though steam locomotives—the historical source of many railway fires—are no longer in regular use. This study examines vegetation fires occurring near railway lines in the Czech Republic over the last 20 years, with the aim of understanding their temporal patterns, links to weather conditions, and spatial concentration. The analysis draws on detailed incident records from the national railway infrastructure manager and combines them with meteorological, geographic, and operational data to identify the factors that influence fire occurrence.

The results show that fires tend to cluster in the warmer part of the year, particularly from spring through late summer, and most often in the afternoon. Their occurrence is strongly associated with prolonged periods of elevated temperatures, limited precipitation, and low relative humidity. Logistic regression further revealed that infrastructure characteristics play a significant role: electrified lines, areas near railway stations, and sections with heavy freight traffic exhibit a markedly higher likelihood of fire. Conversely, higher elevations and greater distance from built-up areas reduce the probability of ignition.

Using the KDE+ method (https://www.kdeplus.cz), we identified more than 300 hotspots where fires repeatedly occurred, despite these locations representing only a very small fraction of the national rail network. These hotspots are typically situated in regions with warmer climates and on lines with substantial train movements. The findings indicate that even modern railway operations can generate ignition sources, such as sparks from braking systems.

Given projected increases in temperature and drought frequency due to climate change, vegetation fires along railways are likely to become more common. The identification of high‑risk segments therefore provides a valuable basis for targeted vegetation management and other preventive measures aimed at reducing the impacts of fires on railway operations and surrounding ecosystems. As part of our current research, we are developing an early‑warning system that integrates weather forecasts, fuel models, and operational data to alert railway managers to elevated fire risk in advance.