## 1 Introduction  
Linear infrastructure plays a key role in the social and economic development of every nation. Road networks are typically the primary means of Italian national transportation, so ensuring their functionality is a challenging and, at the same time, demanding task that central and local authorities are asked to address [1]. Italy is a country strongly affected by natural hazards, such as landslides, which often cause casualties and physical damage to private and public property [2]. Among these hazards, slow-moving landslides can induce considerable damage to road networks, although they are rarely associated with the loss of human life [3, 4, 5, 6, 7, 8, 9, 10]. Road infrastructures often develop within complex geological contexts, where active slow-moving landslides may generate deformation, cracks and local failures [11, 12]. Moreover, the functionality and the safety conditions of the road network may be affected by long-term negative effects, including: (i) partial or complete destruction of a road stretch; (ii) traffic disruption due to the blockage of one or more road sections, which may affect the whole network, causing traffic congestion; and (iii) isolation of certain areas that cannot be reached by alternative routes [13]. Thus, the identification of the road sections most exposed to landslide risk is critical for reducing the population potentially exposed to risk and for minimizing the repair/replacement costs [14]. Several authors [15; 16; 17] also stress that the exposure of road networks to slope instabilities could increase as a consequence of climate change. Landslide risk analysis requires the following main steps: identification of the spatial distribution of the landslides of interest that exist or potentially may occur in the area (i.e., susceptibility estimation); evaluation of the probability of occurrence of the landslides and, for existing active phenomena, of their intensity in terms of kinematic behavior over time (i.e., hazard estimation); evaluation of the of the elements at risk and their vulnerability in relation to the considered phenomena (i.e., consequence estimation); and risk estimation [18]. The assessment of landslide risk along road corridors may be carried out by using several methods according to the scale of the analysis, data availability and the aims of the study [19]. Assessing and mapping landslide susceptibility along road corridors can be considered an established practice. Indeed, several studies evaluate landslide susceptibility by means of expert-driven or data-driven statistical analyses, which relate known landslide occurrences to relevant thematic layers [4; 5; 6; 7; 11; 12; 13; 14; 20; 21; 22; 23; 24; 25]. Recently, the technical advances in geographic information systems (GIS), remote sensing and soft computing allow a wider use of machine learning (ML) algorithms to reveal the correlations between landslide development and predisposing factors [26; 27; 28]. Different models have their advantages and drawbacks, and the performance of each model varies according to the input data, model structure and accuracy. Therefore, no consensus has been reached on the most suitable and efficient technique for every single region, and the prediction capability of a model largely depends on the available input data and local geo-environmental conditions [29]. Quantifying the level of risk along road corridors exposed to slow-moving landslides can be very challenging, due to several aspects. Landslide inventories containing objective and complete information regarding the past and current state of activity for the phenomena are not always available [30]. Assessing over large areas the temporal probability of landslides characterized by a given intensity, or the probability of damaging the elements at risk, may be not straightforward [14]. The development of large datasets of road displacements and damage through traditional techniques can be time-consuming, sometimes not affordable and provided on limited time/spatial extent [5].