Spatio-Temporal Behavior Analysis and Pheromone-Based Fusion Model for Big Trace Data

Abstract

People leave traces of movements that might affect the behavior of others both online in cyberspace and offline in real space. Previous studies, however, have used only questionnaires, network data, or GPS data to study spatio-temporal behaviors, ignoring the relationship between online and offline activities, and overlooking the influence of previous activities on future behaviors. We propose a Pheromone-based Fusion Model, viewing human behaviors as similar to insect foraging behaviors to model spatio-temporal recreational activity patterns, on and offline. In our model, website data were combined with GPS data to evaluate the attractiveness of destinations over time using twenty-nine landscapes in Beijing, China; big website data and GPS trajectories were gathered from 181 users for 57 months. The datasets were portioned into two periods. Online and offline recreational pheromones were calculated from the first period, and the visitation rates were extracted from the second period. These data were subsequently applied in a regression analysis to determine unknown parameters and estimate the attractiveness of destinations. The proposed method was compared with two other approaches that use either GPS data or online data alone, in order to verify effectiveness. The results show that the proposed method can estimate future behaviors, based on real world and online past actions.

A generic paradigm for mining human mobility patterns based on the GPS trajectory data using complex network analysis

“…In the Graph Theory, the complex network of GPS trajectories is composed of many trajectory points with GPS coordinates and segments. Those coordinates and trajectory segments involve much information of human mobility patterns, such as the range of travel, the interest locations and paths, the critical locations and paths, the optimal paths, and the transportation modes 14–16 . The GPS trajectory complex network can assist to interpret the intersections and distributions of trajectories.…”

“…Those coordinates and trajectory segments involve much information of human mobility patterns, such as the range of travel, the interest locations and paths, the critical locations and paths, the optimal paths, and the transportation modes. [14][15][16] The GPS trajectory complex network can assist to interpret the intersections and distributions of trajectories. Moreover, several network analysis metrics, such as the degree centrality, Betweenness, Closeness, and shortest path, can be employed to mine the human mobility patterns.…”

“…Currently, various methods such as the machine learning algorithms, statistical methods, and semantic analysis are widely applied to identify and extract human mobility patterns, including the interest points, travel range, transportation modes, origins and destinations, traffic flows, and track shapes 14–16 …”

Urban Vitality Measurement and Influence Mechanism Detection in China

“…Urban vitality research is facing the transformation of the research paradigm and the innovation of data and methods. The rapid development and popularity of wireless communication, mobile positioning, and Internet technologies make it possible to obtain human behavior and activity patterns based on massive spatio-temporal trajectories at individual granularity [19,20]. Mobile phone signaling data [21], GPS trajectory [22], social media data [23], etc., have been gradually applied by scholars in the study of urban vitality measurement.…”

An Integrated Framework Combining Multiple Human Activity Features for Land Use Classification

“…With the rapid development of information and communication technologies (ICT), massive amounts of crowdsourced data (e.g., mobile phone record data, taxi trajectory data and social media check-in data) are well captured. These data are plentiful and accessible, and they contain a wealth of information about human activities and socioeconomics, providing strong support for understanding urban land use [38][39][40]. Reades et al [41] analyzed the relationship between mobile phone data and business land and identified different mobile phone usage patterns between business and residential land.…”