A generalized vector-field framework for mobility

TL;DR

This paper introduces a vector-field framework for modeling human mobility that captures both flow intensity and direction, providing new insights into urban movement patterns and a benchmark for future models.

Contribution

It presents a novel general vector-field approach for mobility, integrating individual trajectories and exploring spatial behaviors with four models of exploration.

Findings

Distance optimization is key for long displacements.

Local exploration resembles random patterns.

Empirical data from China and NYC validate the models.

Abstract

Trip flow between areas is a fundamental metric for human mobility research. Given its identification with travel demand and its relevance for transportation and urban planning, many models have been developed for its estimation. These models focus on flow intensity, disregarding the information provided by the local mobility orientation. A field-theoretic approach can overcome this issue and handling both intensity and direction at once. Here we propose a general vector-field representation starting from individuals' trajectories valid for any type of mobility. By introducing four models of spatial exploration, we show how individuals' elections determine the mesoscopic properties of the mobility field. Distance optimization in long displacements and random-like local exploration are necessary to reproduce empirical field features observed in Chinese logistic data and in New York City Foursquare check-ins. Our framework is an essential tool to capture hidden symmetries in mesoscopic urban mobility, it establishes a benchmark to test the validity of mobility models and opens the doors to the use of field theory in a wide spectrum of applications.