Adopting Road-Weather Open Data in Route Recommendation Engine
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908484419715072 |
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| author | Tammia, Henna Kämä, Benjamin Peltonen, Ella |
| author_facet | Tammia, Henna Kämä, Benjamin Peltonen, Ella |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_07881 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adopting Road-Weather Open Data in Route Recommendation Engine Tammia, Henna Kämä, Benjamin Peltonen, Ella Software Engineering 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. |
| title | Adopting Road-Weather Open Data in Route Recommendation Engine |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2508.07881 |