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.
We present a simulation scenario of Luxembourg, which allows the simulation of vehicular Long-Term Evolution (LTE) connectivity with realistic mobility necessary for this dissertation.
In the first study, we 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%.
Further, we consider mobile network data for supporting demand estimation and synthetic population generation. In a study on Call Detail Records (CDR) data from Senegal, we estimate CDT distributions to allow generating the duration of user activities, validating them against a data set from China. Additionally, we show how mobile network signaling data can be used for initialising 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.
Members of the defence committee:
- Chairman: A-Prof. Dr. Francesco VITI, University of Luxembourg
- Vice-chairman: Prof. Dr. Falko DRESSLER, University of Paderborn
- Supervisor: Prof. Dr. Thomas ENGEL, University of Luxembourg
- Member: Dr. Raphaël FRANK, University of Luxembourg
- Member: Dr. Marco FIORE, National Research Council of Italy