mlsanityData to 7 Oct 2026

Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

arXiv 1510.04026 ↗First posted 14 Oct 2015cs.NIcited by 18

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Abstract (arXiv)

Understanding mobile traffic patterns of large scale cellular towers in urban environment is extremely valuable for Internet service providers, mobile users, and government managers of modern metropolis. This paper aims at extracting and modeling the traffic patterns of large scale towers deployed in a metropolitan city. To achieve this goal, we need to address several challenges, including lack of appropriate tools for processing large scale traffic measurement data, unknown traffic patterns, as well as handling complicated factors of urban ecology and human behaviors that affect traffic patterns. Our core contribution is a powerful model which combines three dimensional information (time, locations of towers, and traffic frequency spectrum) to extract and model the traffic patterns of thousands of cellular towers. Our empirical analysis reveals the following important observations. First, only five basic time-domain traffic patterns exist among the 9,600 cellular towers. Second, each of the extracted traffic pattern maps to one type of geographical locations related to urban ecology, including residential area, business district, transport, entertainment, and comprehensive area. Third, our frequency-domain traffic spectrum analysis suggests that the traffic of any tower among the 9,600 can be constructed using a linear combination of four primary components corresponding to human activity behaviors. We believe that the proposed traffic patterns extraction and modeling methodology, combined with the empirical analysis on the mobile traffic, pave the way toward a deep understanding of the traffic patterns of large scale cellular towers in modern metropolis.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Oct 2015.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1; 2025: 7; 2026: 6.

18 papers.

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  1. 2021Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Controlcited by 7
  2. 2024UoMo: A Universal Model of Mobile Traffic Forecasting for Wireless Network Optimizationcited by 4
  3. 2025Unveiling the Power of Noise Priors: Enhancing Diffusion Models for Mobile Traffic Predictioncited by 4
  4. 2025TGT: A Temporal Gating Transformer for Smartphone App Usage Predictioncited by 2
  5. 2025A Survey of Physics-Informed AI for Complex Urban Systemscited by 2
  6. 2025Invisible Walls in Cities: Designing LLM Agent to Predict Urban Segregation Experience with Social Media Contentcited by 1
  7. 2025Multi-Scale Diffusion Transformer for Jointly Simulating User Mobility and Mobile Traffic Patterncited by 1
  8. 2026MISApp: Multi-Hop Intent-Aware Session Graph Learning for Next App Predictioncited by 1
  9. 2021A Distributed Intelligence Architecture for B5G Network Automationcited by 0
  10. 2022TLETA: Deep Transfer Learning and Integrated Cellular Knowledge for Estimated Time of Arrival Predictioncited by 0
  11. 2023Topological Graph Signal Compressioncited by 0
  12. 2025Topology-Aware Differential Privacy in Hierarchical Federated Learningcited by 0
6 more
  1. 2025Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecastingcited by 0
  2. 2026U-MASK: User-adaptive Spatio-Temporal Masking for Personalized Mobile AI Applicationscited by 0
  3. 2026Cellular Predictions on the Move: What about Data?cited by 0
  4. 2026Adaptive Two-Stage Online Learning for Service-Affecting Failure Detection in Mobile Core Networkscited by 0
  5. 2026Spatiotemporal Graph Transformer for Traffic Intelligence in Edge Computingcited by 0
  6. 2026Diff-DDoS: Realistic Cyber-Physical Attack Synthesis and Robust Detection for 5G-Enabled CPS Using Tabular Diffusion Modelscited by 0