A Crowdsourced Data Integration Model for Safer Transport Solutions: Challenges and Lessons from Danish Municipalities

In recent years, crowdsourcing has become a valuable tool for municipalities to engage citizens in policymaking, particularly in transport and road safety. This approach is especially relevant in Denmark, a country with strong commitments to sustainable mobility and civic participation. This paper investigates the potential of crowdsourced data within an integrated model designed to support public administrations in transport safety planning. The model combines citizen reports (from January 2023 to June 2025) collected via Giv et praj platforms with official road accident data from 2023 and 2024. Its goal is to forecast 2025 traffic accidents, identify integration challenges, and highlight areas of convergence and divergence between subjective and objective data. The approach is applied to two Danish municipalities: Odense, Denmark's fourth largest municipality (200,000 inhabitants), and Gentofte, located in the Copenhagen region (70,000 inhabitants). The study examines the feasibility of predictive modelling with partial 2025 crowdsourced data and discusses methodological challenges related to semantic classification, sentiment analysis, and data reliability. The findings show both the opportunities and the limits of integrating citizen-generated and official data, opening a debate on how municipalities can leverage crowdsourcing to enrich decision-making and promote safer, more responsive mobility systems.