An empirical study on human mobility and its agent-based modeling

Abstract

Abstract. This paper aims to analyze the GPS traces of 258 volunteers in orderto obtain a better understanding of both the human mobility patterns and the mechanism. We report the regular and scaling properties of human mobility for several aspects, and importantly we identify its Levy flight characteristic, which is consistent with those from previous studies. We further assume two factors that may govern the Levy flight property: (1) the scaling and hierarchical properties of the purpose clusters which serve as the underlying spatial structure, and (2) the individual preferential behaviors. To verify the assumptions, we implement an agent-based model with the two factors, and the simulated results do indeed capture the same Levy flight pattern as is observed. In order to enable the model to reproduce more mobility patterns, we add to the model a third factor: the jumping factor, which is the probability that one person may cancel their regular mobility schedule and explore a random place. With this factor, our model can cover a relatively wide range of human mobility patterns with scaling exponent values from 1.55 to 2.05.

Novel Multiscale Modeling Tool Applied to Pseudomonas aeruginosa Biofilm Formation

“…The resulting extension provides a simple interface between the NetLogo and MATLAB platforms that allows users to exploit the strengths of both languages in their models (Figure 1). While the following multiscale analysis is a biomedical example, this tool could readily find application in other fields for which integrated MATLAB and NetLogo analyses are of value such as ecology [31], finance [32], or behavioral science [33]. …”

Out-of-town shopping and its induced CO2-emissions

“…4 During the study period, the median number of trips per volunteer was three while one volunteer made as many as 12 trips to the shopping center. Jia, Jiang, Carling, Bohlin and Ban (2012a) found that the average number of halts per trip for all the volunteers' trips was about seven. However, the shopping trips were mostly directly from homes in the residential areas of Borlänge ( Figure 6).…”

Understanding the Functionality of Human Activity Hotspots from Their Scaling Pattern Using Trajectory Data

“…Hence, it is not a trivial task to derive stopping locations, because of the ambiguity of setting threshold values for the time intervals. In this study, we take the arithmetic mean value of the time intervals of each taxi in each month as its own time threshold value, which is different from a previous study [36] where only one mean value is used. This strategy can be justified by the great differences among taxis in their mean values of time intervals, which obey remarkable power law distributions with respect to the three months as shown in Figure 2a.…”

“…Hence, these newly derived POI types are aggregated to infer their functionality by either reweighting the importance among POIs or adding new types of POIs. It should be noted that we set the damping factor at the value of 0.85, which has been generally used in many studies on human mobility patterns and models [36,51]. In this context, it means that approximately 85% of stopping locations would be matched with POIs in the local activity hotspot and approximately 15% of them would be assigned to POI types in the global study area.…”