The scaling of human mobility by taxis is exponential

TL;DR

This study analyzes 20 million taxi trajectories in Beijing and finds that urban human mobility displacements and times follow exponential distributions, contrasting with the commonly observed power-law patterns, revealing the bursty nature of human activity.

Contribution

The paper presents a novel finding that urban taxi mobility patterns follow exponential distributions, challenging the prevalent power-law assumption in human mobility modeling.

Findings

Taxi displacements follow exponential distribution.

Elapsed travel times are also exponential.

Interevent times show bursty human activity patterns.

Abstract

As a significant factor in urban planning, traffic forecasting and prediction of epidemics, modeling patterns of human mobility draws intensive attention from researchers for decades. Power-law distribution and its variations are observed from quite a few real-world human mobility datasets such as the movements of banking notes, trackings of cell phone users' locations and trajectories of vehicles. In this paper, we build models for 20 million trajectories with fine granularity collected from more than 10 thousand taxis in Beijing. In contrast to most models observed in human mobility data, the taxis' traveling displacements in urban areas tend to follow an exponential distribution instead of a power-law. Similarly, the elapsed time can also be well approximated by an exponential distribution. Worth mentioning, analysis of the interevent time indicates the bursty nature of human mobility, similar to many other human activities.