Understanding the Heterogeneity of Human Mobility Patterns: User Characteristics and Modal Preferences

Abstract

Characterizing individual mobility is critical to understand urban dynamics and to develop high-resolution mobility models. Previously, large-scale trajectory datasets have been used to characterize universal mobility patterns. However, due to the limitations of the underlying datasets, these studies could not investigate how mobility patterns differ over user characteristics among demographic groups. In this study, we analyzed a large-scale Automatic Fare Collection (AFC) dataset of the transit system of Seoul, South Korea and investigated how mobility patterns vary over user characteristics and modal preferences. We identified users’ commuting locations and estimated the statistical distributions required to characterize their spatio-temporal mobility patterns. Our findings show the heterogeneity of mobility patterns across demographic user groups. This result will significantly impact future mobility models based on trajectory datasets.

An LBS and agent-based simulator for Covid-19 research

“…Considering the fact that the virus transmission was demographically heterogeneous (such as age and gender 23 ), e.g., Covid-19 was significantly more lethal for older people than other populations 34 , we also assigned epidemic correlated attributes in the group pattern. These attributes were vital for virus transmission simulation, but missing in traditional human mobility data such as mobile phone location data and GPS data.…”

Revealing Daily Mobility Pattern Disparities of Monomodal and Multimodal Travelers through a Multi-Layer Cluster Analysis: Insights from a Combined Big Dataset

“…Characterizing a mobility pattern is critical for understanding the dynamics of travel behaviors and have been a hot topic in recent decades. Normally, different local patterns are driven from travel diary surveys [27]. Most surveys and empirical studies are conducted in developed countries.…”

Varieties of Mobility Measures: Comparing Survey and Mobile Phone Data during the COVID-19 Pandemic

“…Thus, localization is not based on the place of the base station itself but rather on the centroid of the area where the specific base station provides the best network coverage and thus mobile devices are most likely connected to it. The literature on device-based data mainly use six types of mobility metrics (e.g., Pappalardo et al 2015 ; Gauvin et al 2020 ; Pepe et al 2020 ; Wu et al 2021 ): number of transfers between areas or provinces, number of unique destinations, stay times at locations, travel times between locations, graphical mapping of movements (e.g., heat maps), and the radius of gyration (ROG). In our case, individual mobility is calculated using the radius of gyration, which is defined as the time-weighted root mean square distance between the center of gravity (i.e., the coordinate-wise time-weighted average) and each localization.…”