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ISPRS Int. J. Geo-Inf. 2017, 6(3), 62; doi:10.3390/ijgi6030062

Usage of Smartphone Data to Derive an Indicator for Collaborative Mobility between Individuals

1
Faculty of Science, Technology and Communication, University of Luxembourg, Luxembourg L-1359, Luxembourg
2
Interdisciplinary Centre for Security, Reliability and Trust (SnT), University of Luxembourg, Luxembourg L-2721, Luxembourg
3
Academy of Economic Studies, Bucharest 010374, Romania
*
Author to whom correspondence should be addressed.
Academic Editors: Bin Jiang, Constantinos Antoniou and Wolfgang Kainz
Received: 21 December 2016 / Accepted: 21 February 2017 / Published: 24 February 2017
(This article belongs to the Special Issue Geospatial Big Data and Transport)
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Abstract

The potential of geospatial big data has been drawing attention for a few years. Despite the larger and larger market penetration of portable technologies (nomadic and wearable devices like smartphones and smartwatches), their opportunities for travel behavior analysis are still relatively unexplored. The main objective of our study is to extract the human mobility patterns from GPS traces in order to derive an indicator for enhancing Collaborative Mobility (CM) between individuals. The first step, extracting activity duration and location, is done using state-of-the-art automated recognition tools. Sensors data are used to reconstruct individual’s activity location and duration across time. For constructing the indicator, in a second step, we defined different variables and methods for specific case studies. Smartphone sensor data are being collected from a limited number of individuals and for one week. These data are used to evaluate the proposed indicator. Based on the value of the indicator, we analyzed the potential for identifying CM among groups of users, such as sharing traveling resources (e.g., carpooling, ridesharing, parking sharing) and time (rescheduling and reordering activities). View Full-Text
Keywords: human mobility patterns; collaborative mobility; geospatial big data; GPS traces; sensing systems human mobility patterns; collaborative mobility; geospatial big data; GPS traces; sensing systems
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MDPI and ACS Style

Toader, B.; Sprumont, F.; Faye, S.; Popescu, M.; Viti, F. Usage of Smartphone Data to Derive an Indicator for Collaborative Mobility between Individuals. ISPRS Int. J. Geo-Inf. 2017, 6, 62.

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