{"ID":2883701,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.07881","arxiv_id":"2508.07881","title":"Adopting Road-Weather Open Data in Route Recommendation Engine","abstract":"Digitraffic, Finland's open road data interface, provides access to nationwide road sensors with more than 2,300 real-time attributes from 1,814 stations. However, efficiently utilizing such a versatile data API for a practical application requires a deeper understanding of the data qualities, preprocessing phases, and machine learning tools. This paper discusses the challenges of large-scale road weather and traffic data. We go through the road-weather-related attributes from DigiTraffic as a practical example of processes required to work with such a dataset. In addition, we provide a methodology for efficient data utilization for the target application, a personalized road recommendation engine based on a simple routing application. We validate our solution based on real-world data, showing we can efficiently identify and recommend personalized routes for three different driver profiles.","short_abstract":"Digitraffic, Finland's open road data interface, provides access to nationwide road sensors with more than 2,300 real-time attributes from 1,814 stations. However, efficiently utilizing such a versatile data API for a practical application requires a deeper understanding of the data qualities, preprocessing phases, and...","url_abs":"https://arxiv.org/abs/2508.07881","url_pdf":"https://arxiv.org/pdf/2508.07881v1","authors":"[\"Henna Tammia\",\"Benjamin Kämä\",\"Ella Peltonen\"]","published":"2025-08-11T11:55:32Z","proceeding":"cs.SE","tasks":"[\"cs.SE\"]","methods":"[]","has_code":false}