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Road networks are complex interconnected systems. Any sudden disruption can result in debilitating impacts on human life or the economy. In particular, road systems in mountain areas are highly vulnerable, because they often do not feature redundant elements at comparable efficiencies.

This paper addresses the impacts of network interruptions caused by landslide events on the (rural) road network system in Vorarlberg, Austria.

Based on a landslide susceptibility map we demonstrate the performance of agent-based traffic modelling using disaggregated agent data. This allows us to gain comprehensive insights into the impacts of road network interruptions on the mobility behaviour of affected people. Choosing an agent-based activity-chain model enables us to integrate the individual behavioural decision-making processes into the traffic flow model. The detailed representation of individual agents in the transport model allows optimisation of certain characteristics of agents and including their social learning effects into the system.

Depending on the location of the interruption, our findings reveal median deviation times ranging between several minutes and more than half an hour, with effects being more severe for employed people than for unemployed individuals.

Moreover, results show the benefits of using agent-based traffic modelling for assessing the impacts of road network interruptions on rural communities by providing insights into the characteristics of the population affected, as well as the effects on daily routines in terms of detour costs. This allows hazard managers and policymakers to increase the resilience of rural road network systems in remote areas.