Universal Predictability of Mobility Patterns in Cities

TL;DR

This paper introduces a simple, parameter-free model that accurately predicts urban human mobility patterns across diverse cities using only population distribution data.

Contribution

The authors present a universal, population-weighted opportunities model that effectively predicts city-scale mobility patterns without adjustable parameters.

Findings

Model accurately predicts distance distribution and travel flux.

Universal applicability across different cities with diverse characteristics.

Outperforms country-level models at urban scale.

Abstract

Despite the long history of modelling human mobility, we continue to lack a highly accurate approach with low data requirements for predicting mobility patterns in cities. Here, we present a population-weighted opportunities model without any adjustable parameters to capture the underlying driving force accounting for human mobility patterns at the city scale. We use various mobility data collected from a number of cities with different characteristics to demonstrate the predictive power of our model. We find that insofar as the spatial distribution of population is available, our model offers universal prediction of mobility patterns in good agreement with real observations, including distance distribution, destination travel constraints and flux. In contrast, the models that succeed in modelling mobility patterns in countries are not applicable in cities, which suggests that there is a diversity of human mobility at different spatial scales. Our model has potential applications in many fields relevant to mobility behaviour in cities, without relying on previous mobility measurements.