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Article

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Published version

Publication date

2020-07-21

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cc-by (c) Cavallaro, Claudia et al., 2020
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/174901

Corridor Detection from Large GPS Trajectories Datasets

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https://doi.org/10.3390/app10145003

Abstract

Given the widespread use of mobile devices that track their geographical location, it has become increasingly easy to acquire information related to users' trips in real time. This availability has triggered several studies based on user's position, such as the analysis of flows of people in cities, and also new applications, such as route recommendation systems. Given a dataset of geographical trajectories in an urbanmetropolitan area,we propose a algorithmto detect corridors. Corridors can be defined as geographical paths, with a minimum length, that are commonly traversed by a minimum number of different users. We propose an efficient strategy based on the Apriori algorithm to extract frequent trajectory patterns from the geo-spatial dataset. By discretizing the data and adapting the roles of itemsets and baskets of this algorithm to our context, we find the longest corridors formed by cells shared by a minimum number of trajectories. After that, we refine the results obtained with a subsequent filtering step, by using a Radius Neighbors Graph. To illustrate the algorithm, the GeoLife dataset is analyzed by following the proposed method. Our approach is relevant for transportation analytics because it is the base to detect lacking lines in public transportation systems and also to recommend to private users which route to take when moving from one part of the city to another on the basis of behavior of the users who provided their logs.

Subject

Serveis de geolocalització, Dades geoespacials

Subject (English)

Location-based services, Geospatial data

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Articles publicats en revistes (Matemàtiques i Informàtica)

Citation

CAVALLARO, Claudia and VITRIÀ I MARCA, Jordi. Corridor Detection from Large GPS Trajectories Datasets. Applied Sciences. 2020. Vol. 10, num. 14. ISSN 2076-3417. [consulted: 8 of August of 2026]. Available at: https://hdl.handle.net/2445/174901

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