Paper Info
Title
Mining mobility user profiles for car pooling
Abstract
In this paper we introduce a methodology for extracting mobility profiles of individuals from raw digital traces (in particular, GPS traces), and study criteria to match individuals based on profiles. We instantiate the profile matching problem to a specific application context, namely proactive car pooling services, and therefore develop a matching criterion that satisfies various basic constraints obtained from the background knowledge of the application domain. In order to evaluate the impact and robustness of the methods introduced, two experiments are reported, which were performed on a massive dataset containing GPS traces of private cars: (i) the impact of the car pooling application based on profile matching is measured, in terms of percentage shareable traffic; (ii) the approach is adapted to coarser-grained mobility data sources that are nowadays commonly available from telecom operators. In addition the ensuing loss in precision and coverage of profile matches is measured.
Year
DOI
Venue
2011
10.1145/2020408.2020591
KDD
Keywords
Field
DocType
application domain,profile matching,private car,specific application context,mining mobility user profile,mobility profile,gps trace,proactive car,coarser-grained mobility data source,matching criterion,satisfiability
Data mining,Computer science,Telecom operators,Pooling,Robustness (computer science),Artificial intelligence,Global Positioning System,Application domain,Application Context,Machine learning
Conference
Citations 
PageRank 
References 
66
2.91
10
Authors
4
Name
Order
Citations
PageRank
Roberto Trasarti171045.82
Fabio Pinelli297250.96
Mirco Nanni3141284.47
Fosca Giannotti42948253.39