Mobile Network Data in Future Transport Systems
| Reference number | |
| Coordinator | Linköpings universitet - Institutionen för teknik och naturvetenskap |
| Funding from Vinnova | SEK 3 827 386 |
| Project duration | November 2016 - December 2020 |
| Status | Completed |
Important results from the project
The aim of the project has been to develop new methods and processes to enable analysis of transport systems based on large-scale and spatiotemporal low-resolution data from mobile networks. In the project, new methods for extracting travels, aggregate travel demand, route selection, and travel time and mode selection using mobile network data have been developed and evaluated. The methods have been published in a number of scientific publications, including a licentiate thesis and a doctoral dissertation, in five international conferences and about ten national conferences.
Expected long term effects
The project has confirmed the usefulness of mobile network data for analysis of transport systems. To achieve socio-economic efficiency and long-term sustainable transport, the project results need to be communicated to mobile operators and end users. The platform developed in the project has already been used in four separate research projects and the project consortium has also developed a web-based demonstration tool. In addition, the project has conducted extensive dialogues with two other mobile operators, the Traffic Analysis Authority, Statistics Sweden, and the Police.
Approach and implementation
To use of the result in practice, the developed methods should be tested on large-scale data. A large portion of the time in the project has been devoted to developing the platform for privacy-secure processing of data according to "bring code to data". The platform has also provided the opportunity to analyze mobile network data for the project participants´ own SIM cards, which has enabled more controlled experiments. The platform has given both project partners and external end users an understanding of important properties of mobile network data for transport analysis.