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Dynamic Routing Using Precipitation Data

Smart mobility is one of the cornerstones of smart cities and in particular, smarter routing decisions are desired and required in order to optimize urban, intermodal transports for individuals as well as businesses. Smart routing comes as a multi-criterion optimization problem, not only dealing solely with efficiency anymore but also with ecologic and economic aspects, as well as comfort, safety, fun etc. In this paper, we propose two new routing algorithms that dynamically re-evaluate rainless routes using up-to-date, high-resolution precipitation information, that additionally take user preferences into account and also consider optimal temporal offsets for departure. Thereby, we not only focus on, e.g., cyclist and pedestrians, who would obviously benefit from such routing, but also on more visionary use cases including aerial or water-borne vehicles. We implemented a middle-tier and a mobile application in order to proof the feasibility of our precipitation-based routing algorithms and demonstrate the advantages by presenting preliminary results of an evaluation using real historical precipitation data.