From Mobile Data to Business Insights An End-to-End Analytics Framework for Large-Scale Urban Mobility Analysis and Decision Support

Jul 16, 2026

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Real-time smartphone-derived location data supports urban planning tasks such as tourism planning, parking management, bus route optimization, and resource allocation, while also enabling commercial decision-making in location-based services, market share analysis, and behavioral profiling. The study targets smartphone user behaviors across tourism, transportation, and retail by building an end-to-end analytics platform with use-case formulation, system architecture, and modular implementation. It integrates data anonymization, ETL pipelines, Google BigQuery and Vertex AI, and reusable analytical components, complemented by Power BI visualizations. Mobility models cover profiling, trajectory mining, influence analysis, anomaly detection, and origin–destination patterns.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages32

Reading Time1 h 21 min

Abstract

Introduction

Data platform purpose

Modular analytics architecture

arXiv :2607 .03394v 1 [ cs .AI] 3 Jul 2026 From Mobile Data to Business Insights: An End-to-End Analytics Framework for Large-Scale Urban Mobility Analysis and Decision Support Thiago Andrade 1 , Shazia Tabassum 1 , Miguel E. P. Silva 1 , Ricardo Dinis2 and Joo Gama 1 1LIAAD, INESC-TEC, Porto, Portugal. 2Mobile Network Analytics, NOS SGPS, Lisboa, Portugal. Corresponding authors: [shazia.tabassum@inesctec.pt](shazia.tabassum@inesctec.pt) ; Abstract Real-time location data derived from mobile applications is a powerful tool for addressing various urban challenges, including tourism planning, parking management, bus route optimization, and resource allocation. Besides, it offers invaluable insights for shaping strategic decisions in commercial domains such as location-based services, market share analysis, and behavioral profiling. In this expansive study, we aim to address all of the aforementioned challenges by investigating the behaviors and patterns of smartphone users within urban environments, particularly in the domains of tourism, transportation, and retail. Our approach encompasses the development of a sophisticated data platform from inception to implementation, which includes the formulation of use cases, architectural design, and implementation of modules. We employ state-of-the-art techniques and technologies, including data anonymization, ETL pipelines, and utilizing Google BigQuery and Vertex AI for data processing and machine learning model development. A modular architecture based on reusable analytical building blocks was developed to generate data products that support multiple stakeholder-driven use cases. Additionally, we apply interactive data visualization techniques via Power BI to facilitate the effective interpretation of analytical findings by stakeholders. The developed models address a wide range of mobility analytics tasks, including mobility profiling, frequent trajectory mining, area-of-influence analysis, traffic anomaly detection, and origin–destination pattern analysis. The results demonstrate the framework’s ability to capture user mobility dynamics at fine 2 1 INTRODUCTION spatial and temporal resolutions, providing actionable insights for urban planning and strategic business decision-making. Overall, the proposed work offers a scalable and reusable solution for transforming raw mobility data into operational intelligence, supporting informed decision-making across urban planning, tourism management, transportation systems, and commercial analytics. Keywords: Urban Mobility, Mobile Communication, Trajectory Mining 1 Introduction Mobile communications are an essential pillar of a digital society, with smartphones, in particular, as a mandatory accessory for most of the population, as both their main means of communication and point of access to digital content. Society’s reliance on smartphones makes these devices an extension of the self, enabling user profiling from the data associated with the phone and its communications. Mobile phone operators are then in a privileged position to exploit this information through technology embedded in communication networks that fulfil services required by users. Their access to mobile communication data, specifically device location, enables mobile phone operators to capture, store and ubiquitously analyze massive amounts of data to generate insights on user patterns and profiles. These insights have a myriad of applications, from social good applications (in case of natural catastrophe, they can help civil authorities in planning evacuation routes or understanding tourist movement patterns to improve tourism routes and identify points of interest) to being sold as data monetization services (for commerce, banking or insurance companies to understand client movement patterns) . The pervasive reliance of society on mobile phone communications means that the quantity of mobile communications data generated per day is massive, i

FAQs

What types of insights can mobile-derived location data provide for urban and commercial use?

It supports urban challenges like tourism planning, parking management, bus routing, and resource allocation, and it also informs commercial decisions such as location-based services, market share analysis, and behavioral profiling.

How does the framework process and prepare mobile data for analytics?

It uses a data platform approach that includes data anonymization and ETL pipelines, together with Google BigQuery and Vertex AI for data processing and machine learning model development.

Which mobility analytics tasks are covered by the proposed models?

The models address mobility profiling, frequent trajectory mining, area-of-influence analysis, traffic anomaly detection, and origin–destination pattern analysis, producing actionable insights at fine spatial and temporal resolutions.

Jul 16, 2026

Upload by Eliana