Clustering Pipeline For Vehicle Behavior In Smart Villages

Abstract

Smart cities and villages present a plethora of opportunities for fusing and managing multi-source data. However, in the analysis of mobility patterns, the use of only one data source (i.e., road sensors) without considering other contextual data sources, limits the understanding of the process. To address this gap, we propose a pipeline that integrates multiple data sources, providing valuable information for pattern extraction, mainly based on vehicle mobility behavior and provenance. Our research also highlights the critical role of selecting the appropriate normalization algorithm to scale input features from heterogeneous data sources, which has not received sufficient attention in the literature. We conducted our analysis using data from four License Plate Recognition (LPR) cameras, spanning nine months, and incorporating several databases that include provenance, gross income, and holiday information, resulting in a dataset of over 50,000 vehicles. Using this data and our clustering pipeline, we identified various traffic patterns among residents and visitors in a rural touristic area. Our findings assist data analysts in choosing algorithms for analyzing heterogeneous datasets. Moreover, policymakers could use our results to adjust the resources, such as new parking zones.