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Taxi mobility information can be considered as an important source of mobile location-based information for making marketing decisions. So, studying the behavioral patterns of taxis in a Chinese city during the holidays using the global positioning system (GPS) can yield remarkable insights into people’s holiday travel patterns, as well as the odd-even day vehicle prohibition system. This paper studies the behavioral patterns of taxis during specific holidays in terms of pick-up and drop-off locations, travel distance, mobile step length, travel direction, and radius of gyration on the basis of GPS data. Our results support the idea of a polycentric city. It is concluded from the reporting results that there are no significant changes in the distribution of pick-up and drop-off locations, travel distance, or travel direction during holidays in comparison to work days. The results suggest that human travel by taxi has a stable regularity. However, the radius of gyration of movement by most of the taxis becomes significantly larger during holidays that indicate more long-distance travels. The current study will be helpful for location-based marketing during the holidays.

Community structure is one of the fundamental characteristics of complex networks. Many methods have been proposed for community detection. However, most of these methods are designed for static networks and are not suitable for dynamic networks that evolve over time. Recently, the evolutionary clustering framework was proposed for clustering dynamic data, and it can also be used for community detection in dynamic networks. In this paper, a multi-similarity spectral (MSSC) method is proposed as an improvement to the former evolutionary clustering method. To detect the community structure in dynamic networks, our method considers the different similarity metrics of networks. First, multiple similarity matrices are constructed for each snapshot of dynamic networks. Then, a dynamic co-training algorithm is proposed by bootstrapping the clustering of different similarity measures. Compared with a number of baseline models, the experimental results show that the proposed MSSC method has better performance on some widely used synthetic and real-world datasets with ground-truth community structure that change over time.

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Partly due to the di±culty of the access to a worldwide dataset that simultaneously captures the location history and social networks, our understanding of the relationship between human mobility and the social ties has been limited. However, this topic is essential for a deeper study from human dynamics and social networks aspects. In this paper, we examine the location history data and social networks data of 712 email users and 399 o®line events users from a mapediting based social network website. Based on these data, we expand all our experiment both from individual aspect and community aspect. We¯nd that the physical distance is still the most in°uential factor to social ties among the nine representative human mobility features extracted from our GPS trajectory dataset, although Internet revolution has made long-distance communication dramatically faster, easier and cheaper than ever before, and in turn, partly expand the physical scope of social networks. Furthermore, we¯nd that to a certain extent, the proximity of South-North direction is more in°uential than East-West direction to social ties. To the our best of our knowledge, this di®erence between South-North and East-West is thē rst time to be raised and quantitatively supported by a large dataset. We believe our¯ndings on the interplay of human mobility and social ties o®er a new perspective to this¯eld of study.

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