Adopting Road-Weather Open Data in Route Recommendation Engine

TL;DR

This paper explores how to effectively utilize Finland's open road-weather data API to develop a personalized route recommendation engine, addressing data challenges and demonstrating real-world validation.

Contribution

It introduces a methodology for processing large-scale road-weather data and applies it to create a personalized routing system validated with real-world data.

Findings

Efficient data preprocessing enables personalized route recommendations.

The system successfully identifies optimal routes for different driver profiles.

Validation shows practical effectiveness of the approach.

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.