Characterizing preferred motif choices and distance impacts

Abstract

People’s daily travels are structured and can be expressed as networks. Few studies explore how people organize their daily travels and which behavioral principles result in the choices of specific network types. In this study, we first reconstruct location networks and activity networks for numerous individuals from high-resolution mobile phone positioning data and define frequent networks as motifs. The results suggest that 99.9% of people’s travels can be characterized by a limited set of location-based motifs and activity-based motifs. The results further reveal that the least effort principle governs the preferred motif choices through quantifying the rank-frequency properties. The scaling properties of distance characteristically impact motifs, and their scaling differences by node numbers and motif types coincide with the popularities of motifs, verifying the self-adaptions in motif choices; that is, although individuals travel with unique propensities, they always tend to choose the motif with the lowest consumption that satisfies their demand.

Mining Daily Activity Chains from Large-Scale Mobile Phone Location Data

“…Third, these methods usually involve a large number of records. For instance, mobile phone location datasets often contain millions of users and billions of records ( Cao et al, 2019 ; Deville et al, 2014 ). Even an auxiliary dataset, such as travel surveys, could involve tens of thousands of households or hundreds of thousands of users.…”

Revealing temporal stay patterns in human mobility using large‐scale mobile phone location data

“…For example, Cao et al. (2019) found that the displacement distance affected the choice of spatial mobility motifs. Therefore, further studies should be conducted to address the factors that could have an impact on temporal stay characteristics.…”

“…According to the above rules, individual travel networks can be constructed for mobile phone users, and human mobility motifs extracted from these travel networks to uncover the frequent mobility patterns of citizens (Cao et al., 2019).…”

“…Based on the individual daily stop location sequence, a pioneering work constructed travel networks for thousands of individuals, and 17 unique networks, called motifs, were extracted that uncover daily mobility patterns (Schneider, Belik, Couronné, Smoreda, & González, 2013). Thereafter, some literatures extend along this line of work by characterizing the relationship between motifs and travel distance (Cao, Li, Tu, & Wang, 2019; Schneider, Rudloff, Bauer, & González, 2013), and inferring activity patterns for trip chains using land use and survey data (Jiang, Ferreira, & Gonzalez, 2017; Widhalm, Yang, Ulm, Athavale, & González, 2015) and extracting work tour motifs (Joy & Samundeeswari, 2018). However, motifs could only generalize the mechanism of human mobility patterns from the perspective of a spatial stop location sequence, whereas temporal stay characteristics at stop locations are neglected in the process of extracting motifs.…”

“…A comprehensive analysis of individual spatiotemporal trajectories attempts to examine spatial displacements among individual daily stop locations in urban spaces and their changes over time in order to further enhance our understanding of how people exploit urban spaces. In this study, the spatial aim is to examine how people move among daily stop locations during the day; this is inspired by previous studies on spatial mobility motifs (Cao et al., 2019; Schneider, Belik, et al., 2013). The temporal motivation for the study is to reveal how people schedule their time at such spatial stop locations during a day, and investigate whether there are primary temporal stay patterns within each spatial mobility motif pattern.…”