Risk Averse Shortest Path Planning in Uncertain Domains

Real world problems, e.g. from transport domain, are typically non-deterministic and uncertain. Although there are some approaches, which try to forecast uncertain parameters like travel time, the uncertainty is rarely included in the planning process. In this paper a probabilistic forecasting method for travel time in a railway network is introduced which considers the dependencies between decisions during the planning process. The information provided by forecasting is used to develop a risk averse shortest path algorithm which minimizes the risk of delay.