Route-Based Geospatial Clustering With Unsupervised Machine Learning

Smartphones equipped with GPS and accelerometer sensors enable large-scale collection of spatiotemporal data, which can be leveraged for transportation research and urban analysis. In this study, the clustering of geospatial data collected along the route from Čačak to Kragujevac, Serbia, using an Android-based mobile application is investigated. The dataset comprises longitude, latitude, time, and speed attributes, enabling the identification of movement patterns through clustering techniques. DBSCAN and K-Means algorithms are applied to group data points based on location and visualize the resulting clusters using Folium maps. The findings provide insights into travel behavior and variations in movement dynamics.