Why do people move? Enhancing human mobility prediction using local functions based on public records and SNS data

<div><p>The quality of life for people in urban regions can be improved by predicting urban human mobility and adjusting urban planning accordingly. In this study, we compared several possible variables to verify whether a gravity model (a human mobility prediction model borrowed from Newtonian mechanics) worked as well in inner-city regions as it did in intra-city regions. We reviewed the resident population, the number of employees, and the number of SNS posts as variables for generating mass values for an urban traffic gravity model. We also compared the straight-line distance, travel distance, and the impact of time as possible distance values. We defined the functions of urban regions on the basis of public records and SNS data to reflect the diverse social factors in urban regions. In this process, we conducted a dimension reduction method for the public record data and used a machine learning-based clustering algorithm for the SNS data. In doing so, we found that functional distance could be defined as the Euclidean distance between social function vectors in urban regions. Finally, we examined whether the functional distance was a variable that had a significant impact on urban human mobility.</p></div

Importance cumulative sum of principal components.

<p>Importance cumulative sum of principal components.</p

Temporal and spatial distributions of tweets.

<p>(a) The number of tweets by hours (b) Tweet Distribution by dong.</p

Functional differences among urban regions through SNS data.

<p>(a) Major residential districts (b) Major business districts.</p

Regression results of functional distance models with ZINBPML estimator.

<p>Regression results of functional distance models with ZINBPML estimator.</p

Major topics, words and ratio of clusters.

<p>Major topics, words and ratio of clusters.</p

The relationship between residential population and floating population.

<p>(a) Inter-city cases, (b) Intra-city cases.</p

Linear regression results of distance terms.

<p>Linear regression results of distance terms.</p

Distributions of features in Seoul.

<p>(a) Residential population (b) The number of employees.</p

The number of datasets by type.

<p>The number of datasets by type.</p