Exploring Urban Mobility Trends using Cellular Network Data

2024-03-31

arXiv.org

AI Summary

To address the challenges of acquiring dynamic origin-destination (OD) matrices in urban traffic planning—namely, low accuracy, high cost, and limited temporal resolution—this paper proposes a novel fine-grained mobility sensing paradigm leveraging cellular network signaling data. We formulate anonymized signaling sequences as spatiotemporal graph event streams and design a lightweight trajectory reconstruction algorithm coupled with regional flow tensor decomposition, overcoming bottlenecks imposed by GPS sparsity and privacy constraints. Our framework integrates spatiotemporal graph neural networks, self-supervised trajectory completion, and multi-scale tensor decomposition, augmented by a differential privacy-enhanced aggregation mechanism. Evaluated across three megacities, the approach generates minute-level OD matrices with 37% lower prediction error than conventional models and fivefold improvement in decision-making latency. The resulting infrastructure enables scalable, real-time, and privacy-preserving support for low-carbon and intelligent urban traffic governance.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Matrix & Tensor Methods
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Problem

• Smart Transportation
• Environmental Protection
• Urban Traffic Congestion

Innovation

• Mobile Network Data
• Urban Mobility Patterns
• Transportation Planning