Reference : Usage of Smartphone Data to Derive an Indicator for Collaborative Mobility between In...
Scientific journals : Article
Engineering, computing & technology : Multidisciplinary, general & others
Computational Sciences
http://hdl.handle.net/10993/29952
Usage of Smartphone Data to Derive an Indicator for Collaborative Mobility between Individuals
English
Toader, Bogdan mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Sprumont, François mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Faye, Sébastien mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Viti, Francesco mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit >]
Popescu, Mioara mailto [Academy of Economic Studies, Bucharest]
24-Feb-2017
ISPRS International Journal of Geo-Information
MDPI AG
6
3
Geospatial Big Data and Transport
62
Yes
International
2220-9964
2220-9964
Basel
Switzerland
[en] human mobility patterns ; collaborative mobility ; geospatial big data ; GPS traces ; sensing systems
[en] 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).
Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/10993/29952
10.3390/ijgi6030062
http://www.mdpi.com/2220-9964/6/3/62
FP7 ; 618234 - INCOMMUNE - Incentivizing Collaborative Mobility by means of Multimodal Sharing Services
FnR ; FNR9220491 > Bogdan Toader > PLAYMOBeL > PLanning and Activity-travel analYtics for future Mobility Optimisation in BelvaL > 15/03/2015 > 14/03/2018 > 2014

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