Learning transportation mode from raw gps data for geographic applications on the web

Zheng, Yu; Liu, Li-Ke; Wang, Longhao; Xie, Xing

doi:10.1145/1367497.1367532

Cited by 503 publications

(414 citation statements)

References 14 publications

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“…Many authors have suggested different extraction methods [8,18,20,39,38,40] based on clustering algorithms. Ashbrook et al [8] have proposed a two-step method to infer the significant locations.…”

Section: Related Workmentioning

confidence: 99%

On the properties of human mobility

Papandrea

Jahromi

Zignani

et al. 2016

Computer Communications

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The current age of increased people mobility calls for a better understanding of how people move: how many places does an individual commonly visit, what are the semantics of these places, and how do people get from one place to another. We show that the number of places visited by each person (Points of Interest -PoIs) is regulated by some properties that are statistically similar among individuals. Subsequently, we present a PoIs classification in terms of their relevance on a per-user basis. In addition to the PoIs relevance, we also investigate the variables that describe the travel rules among PoIs in particular, the spatial and temporal distance. As regards the latter, existing works on mobility are mainly based on spatial distance. Here we argue, rather, that for human mobility the temporal distance and the PoIs relevance are the major driving factors. Moreover, we study the semantic of PoIs. This is useful for deriving statistics on people's habits without breaking their privacy. With the support of different datasets, our paper provides an in-depth analysis of PoIs distribution and semantics; it also shows that our results hold independently of the nature of the dataset in use. We illustrate that our approach is able to effectively extract a rich set of features describing human mobility and we argue that this can be seminal to novel mobility research.

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Section: Related Workmentioning

confidence: 99%

On the properties of human mobility

Papandrea

Jahromi

Zignani

et al. 2016

Computer Communications

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“…Giannotti et al [13] propose sequential pattern mining algorithms for mining trajectories of moving objects. Frequently occurring sequential patterns have been analyzed in [9,10,14,15]. However, the short and frequent movements of users identify more important behaviors.…”

Section: Modeling User Behaviormentioning

confidence: 99%

An approach to compute user similarity for GPS applications

Mazumdar

Patra

Lock

et al. 2016

Knowledge-Based Systems

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The proliferation of GPS enabled devices has led people to share locations both consciously and unconsciously. Large spatio-temporal data comprising of shared locations and whereabouts are now being routinely collected for analysis. As user movements are generally driven by their interests, so mining these mobility patterns can reveal commonalities between a pair of users. In this paper, we present a framework for mining the published trajectories to identify patterns in user mobility. In this framework, we extract the locations where a user stays for a period of time popularly known as the stay points. These stay points help to identify the interests of a user. The statistics of pattern and check-in distributions over the GPS data are used to formulate similarity measures for finding K -nearest neighbors of an active user. In this work, we categorize the neighbors into three groups namely strongly similar, closely similar and weakly similar. We introduce three similarity measures to determine them, one for each of the categories. We perform experiments on a real-world GPS log data to find the similarity scores between a pair of users and subsequently find the effective K -neighbors. Experimental results show that our proposed metric outperforms existing metrics in literature.

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“…Another case is the project GeoLife proposed by Zheng Yu et al [3] from Microsoft Research Asia. The team conducted experiments using GPS data collected from 181 volunteers during a period of 2 years in the real world to mine valuable knowledge like individual life pattern [4] , transportation mode [5] and user similarity [6] , etc. Moreover, these GPS data are all shared freely on the Web.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Clustering for People's Movement Pattern Based on Crowd Souring Data

Chen

Zhang

et al. 2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:With the increasing availability of GPS-enabled devices, a huge amount of GPS trajectories recording people's location traces have been accumulated and shared freely on the Web. In this area, one of the most important research topics is to exploit trajectory-movement pattern about where and when people clustered based on the raw GPS data. In order to solve this problem, clustering is a good way to perform data mining tasks on trajectory data. This paper provides a clustering algorithm which aims at mining people's movement pattern about the clustered location and their temporal evolution characteristics. Firstly, the characteristic points of GPS trajectories were chosen. Based on the characteristic points, a trajectory has been partitioned into a group of line segments. These line segments can represent the movement pattern of trajectories much better than that of track points. Secondly, an improved density-based line clustering method was used for the individual partitioned line segments to find out individual clusters with similar track segments. In this step, the absolute time spot of people's trajectories was taking into account as a characteristic for the temporal evolution of people's trajectories. Finally, the representative clustered hot spots of multiple users' line segments achieved by above steps were output. Experiments were conducted with GPS trajectories data downloaded from the web to verify the effectiveness of the algorithm in this paper. According to the results, the spatial distribution and temporal evolution characteristics of people's stay hot spots were effectively discovered from people's GPS trajectories data.

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Learning transportation mode from raw gps data for geographic applications on the web

Cited by 503 publications

References 14 publications

On the properties of human mobility

On the properties of human mobility

An approach to compute user similarity for GPS applications

Trajectory Clustering for People's Movement Pattern Based on Crowd Souring Data

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