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

arXiv: https://arxiv.org/abs/2607.03394 · HTML: https://mlsanity.com/paper/2607.03394/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 62 references that are in the map.
- First posted on arXiv 3 Jul 2026.

## Builds on (most cited first, 10 of 10)

- [Deep Learning for Spatio-Temporal Data Mining: A Survey](https://mlsanity.com/paper/1906.04928/) (2019)
- [Inferring transportation modes from GPS trajectories using a convolutional neural network](https://mlsanity.com/paper/1804.02386/) (2018)
- [A Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations](https://mlsanity.com/paper/2011.10187/) (2020)
- [Semi-Supervised Learning with Multi-Head Co-Training](https://mlsanity.com/paper/2107.04795/) (2021)
- [DETECT: Deep Trajectory Clustering for Mobility-Behavior Analysis](https://mlsanity.com/paper/2003.01351/) (2020)
- [DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion](https://mlsanity.com/paper/1802.00002/) (2018)
- [Smart Urban Mobility: When Mobility Systems Meet Smart Data](https://mlsanity.com/paper/2005.06626/) (2020)
- [DeepSpace: An Online Deep Learning Framework for Mobile Big Data to Understand Human Mobility Patterns](https://mlsanity.com/paper/1610.07009/) (2016)
- [Identifying Points of Interest and Similar Individuals from Raw GPS Data](https://mlsanity.com/paper/1904.09357/) (2019)
- [Mining Human Mobility Data to Discover Locations and Habits](https://mlsanity.com/paper/1909.11406/) (2019)

## Abstract (arXiv)

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 spatial and temporal resolutions, providing actionable insights for urban planning and strategic business decision making.