Inferring and Focusing Areas of Interest from GPS Traces

Lerin, Pablo Martinez; Yamamoto, Daisuke; Takahashi, Nobutaka

doi:10.1007/978-3-642-19173-2_14

Cited by 1 publication

(3 citation statements)

References 7 publications

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“…The concepts underlying our proposed method and experimental results that confirm its feasibility have already been presented in our previous work [10]. The most important difference with this work is that while our previous work discussed only the location inference, this work discusses location and duration inference.…”

Section: Copyright C 2014 the Institute Of Electronics Information Amentioning

confidence: 64%

“…They are motivated by the two requirements defined in the previous section and the following four observations, which were described in detail in our previous work [10]: (1) Travelers usually move at a slower pace during visits. (2) Speed thresholds may be used to distinguish to some extent the different paces during a trip (e.g., a pace is traveled at a speed below 5 km/h or a speed between 5 km/h and 15 km/h).…”

Section: Overviewmentioning

confidence: 99%

“…Searching for more local minimums in f (s) allows us to identify more traveling paces used in the trip. In our previous work, two local minimums in f (s) are used to infer and highlight on a map screen the areas and subareas of interest of a travel trace [10].…”

Section: Slowest Pace Retrievalmentioning

confidence: 99%

See 2 more Smart Citations

Pace-Based Clustering of GPS Data for Inferring Visit Locations and Durations on a Trip

Lerin

Yamamoto

Takahashi

2014

IEICE Trans. Inf. & Syst.

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SUMMARYTravel recommendation and travel diary generation applications can benefit significantly from methods that infer the durations and locations of visits from travelers' GPS data. However, conventional inference methods, which cluster GPS points on the basis of their spatial distance, are not suited to inferring visit durations. This paper presents a pace-based clustering method to infer visit locations and durations. The method contributes two novel techniques: (1) It clusters GPS points logged during visits by considering the speed and applying a probabilistic density function for each trip. Consequently, it avoids clustering GPS points that are near but unrelated to visits. (2) It also includes additional GPS points in the clusters by considering their temporal sequence. As a result, it is able to complete the clusters with GPS points that are far from the visits but are logged during the visits, caused, for example, by GPS noise indoors. The results of an experimental evaluation comparing our proposed method with three published inference methods indicate that our proposed method infers the duration of a visit with an average error rate of 8.7%, notably outperforming the other methods. key words: clustering method, visit inference, place inference, duration inference, GIS

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Section: Copyright C 2014 the Institute Of Electronics Information Amentioning

confidence: 64%

Section: Overviewmentioning

confidence: 99%