A MODEL FOR DETECTING URBAN INFRASTRUCTURE PROBLEMS IN CITIZENS’ APPEALS BASED ON GEOLOCATION FEATURES
DOI:
https://doi.org/10.5281/zenodo.19246732Keywords:
geo-map, machine learning, interactive services, GPS, NLP.Abstract
This article proposes a model for identifying and classifying urban infrastructure problems based on geolocation
data derived from citizens’ appeals. The study extracts features from geolocation points submitted by citizens, including
timestamps and movement parameters.
Based on these features, methods have been developed for the automatic detection of issues such as traffic congestion,
road damage, waste accumulation, and traffic signal malfunctions. The proposed model enables the classification of
urban problems using machine learning algorithms.
The research results demonstrate that the use of geolocation-based citizen appeal data significantly enhances the
efficiency of identifying urban issues and supports faster and more informed decision-making processes in urban
management systems.
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