Paper Info
Title
Temporal decomposition and semantic enrichment of mobility flows
Abstract
Mobility data has increasingly grown in volume over the past decade as localisation technologies for capturing mobility flows have become ubiquitous. Novel analytical approaches for understanding and structuring mobility data are now required to support the backend of a new generation of space-time GIS systems. It is increasingly important as GIS is becoming a decision support platform for operations in fleet management, urban data analysis and related applications. This paper applies the machine learning method of probabilistic topic modelling for semantic enrichment of mobility data recorded in terms of trip counts by using geo-referenced social media data. It further explores the questions of causality and correlation, as well as predictability of the obtained semantic decompositions of mobility flows on a real dataset from a bike sharing network.
Year
DOI
Venue
2013
10.1145/2536689.2536806
LBSN
Keywords
Field
DocType
temporal decomposition,bike sharing network,geo-referenced social media data,mobility flow,space-time gis system,semantic enrichment,fleet management,mobility data,semantic decomposition,decision support platform,urban data analysis,topic modelling
Data science,Data mining,Predictability,Social media,Computer science,Decision support system,Mobility model,Topic model,Probabilistic logic,Structuring,Fleet management
Conference
Citations 
PageRank 
References 
10
0.59
13
Authors
2
Name
Order
Citations
PageRank
Cathal Coffey1232.27
Alexei Pozdnoukhov221618.87