Railway incidents undermine both punctuality and capacity. As traffic volumes increase, the frequency of these incidents is also expected to increase, driven by higher asset utilization, reduced time for maintenance, and further worsened by climate change. This highlights the importance of efficient incident management and corrective maintenance. This study uses a combination of exploratory data analysis and random forest regression to investigate restoration time − a key delimiter of corrective maintenance. Using data from the Swedish railway network, the study investigates the driving factors of restoration times. The maintenance contractor and the type of incident including repair action, failure cause, and the number of concurrent incidents were found to have the highest influence on restoration times respectively. Weather parameters only show discernible influence (both direct and indirect influence) beyond marked thresholds. For the Swedish railway network, these thresholds are −20 °C and 23 °C maximum daily temperatures, 18 mm maximum daily precipitation, and 20 m/s maximum windspeed. Precipitation's and windspeed's direct effects become more prominent beyond 50 mm and 35 m/s respectively. An understanding of the factors influencing restoration times informs the process of designing corrective maintenance protocols. Moreover, the method used in this study can be adapted to other railway networks.