Activity identification from GPS trajectories using spatial temporal POIs' attractiveness

Lian, Huang; Li, Qingquan; Yue, Yang

doi:10.1145/1867699.1867704

Cited by 58 publications

(26 citation statements)

References 13 publications

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“…The work of [5] aims to infer activities from users trajectories. This paper presents an approach using spatial temporal attractiveness of POIs to identify activity-locations and durations from raw GPS trajectory.…”

Section: Related Workmentioning

confidence: 99%

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Online recognition of people's activities from raw GPS data

Boukhechba

Bouzouane

Bouchard

et al. 2015

Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments

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The widespread use of GPS devices leads to an increasing availability of people traces. The GPS trajectory of a moving object is a time stamped sequence of latitude and longitude coordinates. The analysis and extraction of knowledge from GPS trajectories is important for several applications domains, ranging from traffic management to advertisement and social studies. We present an approach capable of incrementally extracting semantic locations from people's trajectories and inferring the activity done by users. We associate the places visited by people during their movements to a meaningful human activities using a novel algorithm that cluster incrementally user's moves into different type of activities. Studies using GPS records from a confined spatio-temporal region demonstrate that the proposal is effective and is capable of inferring human activities without depleting the phone resources.

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Section: Related Workmentioning

confidence: 99%

“…The envisioned goal of this tool is to produce a DB-file suited to be immediately used with some appropriate GIS application (e.g. QGIS 5 ).…”

Section: Accuracymentioning

confidence: 99%

Online recognition of people's activities from raw GPS data

Boukhechba

Bouzouane

Bouchard

et al. 2015

Proceedings of the 8th ACM International Conference on PErvasive Technologies Related to Assistive Environments

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show abstract

“…In literature, such information is typically used to assign single stops to a point of interest (e.g. [12]), mainly to enrich trajectories with activity information, rather than identifying recurrent locations. How to integrate that within a clustering-based location extraction process is an open problem that we leave as future work.…”

Section: Related Workmentioning

confidence: 99%

Tosca

Guidotti

Trasarti

Nanni

2015

Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

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One of the key tasks in mobility data analysis is the study of the individual mobility of users with reference to their personal locations, i.e. the places or areas where they stop to perform any kind of activities. Correctly discovering such personal locations is therefore a very important problem, which is yet not very well addressed in literature. In this work we propose a robust, efficient, statistically well-founded and parameter-free personal location detection process. The algorithm, called TOSCA (TwO-Steps parameter free Clustering Algorithm), combines two clustering strategies and applies statistical tests to drive the selection of the needed parameters. The proposed solution is tested against a large set of competitors and several datasets, including synthetic and real ones. The empirical results show its ability to automatically adapt to different contexts yielding good accuracy and a good efficiency.

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“…For example, Yin et al (2011) studied the distributions of some geographical topics (like beach, hiking, and sunset) from the geo-tagged photos acquired from Flickr. After that, Huang et al (2010) proposes a method to automatically detect activities using the spatial temporal attractiveness (STPA) of points of interest (POI). By comparing the sub-trajectories contained in each POI's STPA, the authors show that L Location-Based Recommendation Systems, Fig.…”

Section: Location-based Social Media Recommendationmentioning

confidence: 99%

Location-Based Recommendation Systems

Bao

Zheng

2015

Encyclopedia of GIS

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Synonyms Location-aware recommendation; Mobile recommendation DefinitionA location-based recommendation is an information filtering service, which selectively returns items (e.g., venues, travel routes, friends, or social media) to a user with the consideration of relevant spatial information (e.g., current/historical locations) and the personal preferences. The recommended results typically include k items with the highest predicated scores, which are calculated based on: (1) a recommendation technique/model (such as content-based filtering, link analysis, or collaborative filtering) and (2) the spatial relevance (like Euclidean distance or network distances). Historical BackgroundLocation-based recommendation system is one of the key social applications in urban space , which emerge from two lines of research: (1) location-based services and (2) recommendation services. The traditional locationbased services answer spatial queries, such as k nearest neighbor queries (i.e., kNN) and spatial range queries. However, in many cases, users do not satisfy with the results that just considering the geospatial distances and want to incorporate with more context and preferences. To this end, recommendation techniques are introduced to provide "best" results instead of the "closest."On the other side, the conventional recommendation services are very successful in providing the suggestions of generic items, like books, movies, and products. With the rapid development in GPS-embedded phones and wireless communications, more users are looking for recommendations related with geospatial information, e.g., suggestions for restaurants, travel routes, and activities. The user's current location is considered as one of the most important contexts for making recommendations. Moreover, a user's location history can also be viewed as a resource to extract a user's preferences. Finally, the aggregated location information generated by all the users in the system can reflect as the social opinions. To this end, the conventional recommendation services also have the urge to incorporate the relevant spatial information to achieve a higher quality of recommendations.The earliest attempts in location-based recommendations focus on providing stand-alone location suggestions (e.g., restaurants and shops) to a

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Activity identification from GPS trajectories using spatial temporal POIs' attractiveness

Cited by 58 publications

References 13 publications

Online recognition of people's activities from raw GPS data

Online recognition of people's activities from raw GPS data

Tosca

Location-Based Recommendation Systems

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