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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.07881 |
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Table of 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.