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Abstract. 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.

Expectations are that automated and connected mobility will increase road safety and traffic efficiency. However, due to possible shortcomings of new technologies , road users may be confronted with disturbances and potential safety risks. The mitigation of such risks will bring necessary changes to road infrastructure, vehicles and road-users' behavior. In a traffic environment that was built to fit the human perception, preemptive simulation of parametrized scenarios can provide guidelines for what changes and difficulties are to be expected. Utilizing SUMO in varied scenarios, this paper outlines the creation of virtual models that correspond to interaction hot spots on the Austrian road network -from digitizing the infrastructure, to calibrating a simulation scenario with congruent traffic measurements -while it concludes with the evaluation of scenario simulation results. The approach is demonstrated for a selected motorway ramp scenario, varying rates of automated vehicles and different infrastructure layouts. Performance indicators like vehicle speed distributions and traffic disruptions are defined and analyzed to investigate how adaptations can mitigate risks, influence traffic flow and hence support progressing vehicle automation.

This work combines data from studies on threat perception by cyclists with data of actual traffic accidents involving cyclists: it presents survey results on perceived dangers of urban cycling based on interviews with cyclists and polls conducted in the city of Vienna (Austria) over the last two years. These results are contrasted with an analysis and evaluation of records of injury accidents over the last 10 years. While the number of bicycle accidents per year has remained stable, the data shows increases of up to 225% for certain accident characteristics. Additionally, the study also reveals potential extensions to the accident database which would enable more detailed analyses of certain aspects such as differences in cyclist safety on different types of bicycle routes.