Human urban mobility, personal or global?

ABSTRACT

Unlike the past studies targeting overall mobility patterns, we examined a wide range of probability density functions among which Weibull found to better explain the human mobility rather than the widely tested power-law distribution. We further examined a variety of factors affecting human mobility such as people’s trip attributes and socio-demographics traits. Moreover, the models show that social media active people travel mainly short distances; while people traveling to and from areas with higher economic and social advantages are more likely to explore a wider area within a city.

KEYWORDS:

• Human mobility
• Social media
• mathematical modeling

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. $f\left(d\right)=\frac{1}{\pi \lambda \left[1+{\left(\frac{d-\gamma }{\lambda }\right)}^2\right]}$.

2. $f\left(d\right)=\frac{\gamma {\lambda }^{\gamma }}{{\left(d+\lambda \right)}^{\gamma +1}}$.

3. $f\left(d\right)=\frac{{\lambda }^{\frac{1}{\gamma }}{d}^{\frac{1}{\gamma }-1}}{\gamma {\left(1+{\left(\lambda d\right)}^{\frac{1}{\gamma }}\right)}^2}$.

4. $f\left(d\right)=\gamma \lambda d^{\gamma -1}{e}^{-\lambda d^{\gamma }}$.

5. $r_g=\sqrt{\frac{1}{n}{\sum }_{i=1}^n{\left(r_i-r_{cm}\right)}^2}$.

Additional information

Funding

This work was supported by the ARC [DE170101346].