Simulating Road Traffic for Generating Cellular Network Logs in Urban Context

Name
Siim-Toomas Marran
Abstract
In the last years, the use of mobile phone data logs start to attract a lot of researchers’ attentions from various disciplines. Those logs help the scientist to understand and predict human behaviour. The mobility logs, like Call Detail Records and GPS data, show where to people commute, how often do they commute and, usually, those logs also say why. These logs hold knowledge about our society, from that data the knowledge could be extracted and used for multiple purposes. The scientists could analyse through the movement how to plan the road infrastructure, generate target advertisement based on forecasting peoples displacement, new positioning technology, population control software, etc.
But there are limits on the people's mobility data. Those information logs are heavily protected by the government privacy data laws to protect the personal rights. Additionally, the mobile operators are interested in their own commercial solutions and therefore their interest to share vital information is low. Here, in this thesis, we show that this cumbersome problem can be over-stepped by prototyping a cellular network behaviour simulator to generate the logs for us through different scientific commuting models inherited from the traffic simulation program.
The result of this thesis reveals that this approach is feasible and shows multiple expansion possibilities how to produce even more real-life like mobility logs. The development of the cellular network behaviour simulation has shown huge potential and even bigger possibilities than predicted in the beginning. Since, our cellular network behaviour simulation is integrated with already existing open-source, highly configurable, road traffic simulator basing on the scientific human behaviour models produce with considerable value data.
Graduation Thesis language
English
Graduation Thesis type
Master - Computer Science
Supervisor(s)
Amnir Hadachi, Artjom Lind
Defence year
2017
 
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