Efficient Data Processing Pipelines for Mobility Data Using MongoDB

The increasing availability of mobility data presents new opportunities and challenges for urban studies and related fields. This paper introduces a data processing pipeline developed to handle large-scale Network Signaling Data (NSD) using MongoDB. Our method addresses key challenges in data preprocessing, such as timestamp compression and noise filtering, to enhance the accuracy and efficiency of mobility pattern analysis. We applied this pipeline to a NSD dataset from Orange Romania, focusing on Cluj County for October 2023. By leveraging MongoDB’s aggregation framework, our approach significantly reduced data size and improved computational efficiency, enabling the extraction of meaningful mobility insights. Our findings highlight the importance of advanced data processing techniques in deriving reliable Origin-Destination matrices and demonstrate the potential for further applications in urban planning and smart city initiatives.