Doctoral thesis (Dissertations and theses)
Mobile Network Data Analytics for Intelligent Transportation Systems
Derrmann, Thierry
2018
 

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Keywords :
Mobile Network; Data Analysis; Machine Learning; Transportation; Intelligent Transportation Systems; Handovers
Abstract :
[en] In this dissertation, we explore how the interplay between transportation and mobile networks manifests itself in mobile network billing and signaling data, and we show how to use this data to estimate different transportation supply and demand models. To perform the necessary simulation studies for this dissertation, we present a simula- tion scenario of Luxembourg, which allows the simulation of vehicular Long-Term Evolu- tion (LTE) connectivity with realistic mobility. We first focus on modeling travel time from Cell Dwell Time (CDT), and show – on a synthetic data set– that we can achieve a prediction Mean Absolute Percentage Error (MAPE) below 12%. We also encounter proportionality between the square of the mean CDT and the number of handovers in the system, which we confirmed in the aforementioned simulation scenario. This motivated our later studies of traffic state models generated from mobile network data. We also consider mobile network data for supporting synthetic population generation and demand estimation. In a study on Call Detail Records (CDR) data from Senegal, we estimate CDT distributions to allow generating the duration of user activities, and validate them at a large scale against a data set from China. In a different study, we show how mobile network signaling data can be used for initializing the seed Origin- Destination (O-D) matrix in demand estimation schemes, and show that it increases the rate of convergence. Finally, we address the traffic state estimation problem, by showing how handovers can be used as a proxy metric for flows in the underlying urban road network. Using a traffic flow theory model, we show that clusters of mobile network cells behave characteristically, and with this model we reach a MAPE of 11.1% with respect to floating-car data as ground truth. The presented model can be used in regions without traffic counting infrastructure, or complement existing traffic state estimation systems.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Networking Research Group (NetLab)
Disciplines :
Computer science
Author, co-author :
Derrmann, Thierry ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC)
Language :
English
Title :
Mobile Network Data Analytics for Intelligent Transportation Systems
Defense date :
07 February 2018
Institution :
Unilu - University of Luxembourg, Luxembourg
Degree :
Docteur en Informatique
Promotor :
President :
Jury member :
Frank, Raphaël 
Fiore, Marco
Dressler, Falko
Focus Area :
Computational Sciences
FnR Project :
FNR5825301 - Multimodal Mobility Assistance, 2013 (01/04/2014-31/03/2017) - Thomas Engel
Funders :
FNR - Fonds National de la Recherche [LU]
Available on ORBilu :
since 25 October 2018

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