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In many routing applications, it is unclear whether driving to a certain destination yields the desired success. For example, consider driving to an appointment and looking for a parking spot. If there are generally few parking spots in the area or if occupancy of spots is currently high, the search may not be successful. In this case, the search is continued, possibly into a different area, where chances of success are higher. We generalize this problem and introduce a probabilistic formalization to model the availability of resources at certain locations. Our probabilistic model considers short term observations (e.g., vacant parking spots) as well as long term observations (e.g., average occupancy time) to adapt to the level of information currently available. In contrast to previous models, we allow resources to reappear after a probabilistically modeled amount of time (e.g., a car leaves a spot). Based on this model, we propose the so-called probabilistic resource route query with reappearance. In order to compute feasible solutions to this query in interactive time, we propose two greedy approaches. Furthermore, we examine backtracking for computing exact solutions and extend the proposed method into a significantly more efficient branch and bound algorithm. In our experiments, we investigate two realistic applications, examine the benefit of our model, and compare algorithmic solutions w.r.t. result quality and computational efficiency.

Probabilistic Resource Route Queries with Reappearance