Data-driven Exploration of Mobility Interaction Patterns

TL;DR

This paper introduces a data-driven method to analyze human mobility interactions by mining real-world movement data, revealing complex patterns that can enhance crowd simulation and emergency response models.

Contribution

It presents a novel data mining approach to identify and analyze mobility interaction patterns directly from movement data, bypassing preconceived behavioral models.

Findings

Identified complex, persistent interaction patterns in mobility data.

Demonstrated the approach on car and pedestrian case studies.

Provided insights for improving human mobility simulations.

Abstract

Understanding the movement behaviours of individuals and the way they react to the external world is a key component of any problem that involves the modelling of human dynamics at a physical level. In particular, it is crucial to capture the influence that the presence of an individual can have on the others. Important examples of applications include crowd simulation and emergency management, where the simulation of the mass of people passes through the simulation of the individuals, taking into consideration the others as part of the general context. While existing solutions basically start from some preconceived behavioural model, in this work we propose an approach that starts directly from the data, adopting a data mining perspective. Our method searches the mobility events in the data that might be possible evidences of mutual interactions between individuals, and on top of them looks for complex, persistent patterns and time evolving configurations of events. The study of these patterns can provide new insights on the mechanics of mobility interactions between individuals, which can potentially help in improving existing simulation models. We instantiate the general methodology on two real case studies, one on cars and one on pedestrians, and a full experimental evaluation is performed, both in terms of performances, parameter sensitivity and interpretation of sample results.