Investigate graph and network algorithms in transport vehicle GPS data to detect and quantify hubs and flow

C. Dudas, C. Engström, J. Karlsson, F. Nellros, L. Qi-Gautier, S. Silvestrov, J. Ulke. Swedish study group Mathematics in Industry, September 4, 2015.


The aim of this project is to investigate how graph and network algorithms together with vehicle GPS data can be used efficiently, and in the longer run support the Scania’s customers with route optimization and logistics planning. Clustering methods and graph methods will be studied to identify appropriate methods that fulfi ll the requirements of the application at Scania. These methods can be used to identify bottlenecks or other points of interest in transportation networks, to find more optimal routes, and/or anomaly detection. The motivation is to apply the results to transport planning tasks in the future. Some examples are in improved driver coaching by having more relevant comparison between vehicles or route planning taking into account traffic flows, popular stops, fuel consumption, etc. which could be of use for Scania’s customers.

Authors and Affiliations

  • Catarina Dudas, Fraunhofer Chalmers Centre
  • Christopher Engström, Mälardalen University
  • Johan Karlsson, Fraunhofer Chalmers Centre
  • Frida Nellros, Scania
  • Luyuan Qi-Gautier, KTH Royal Institute of Technology
  • Sergei Silvestrov, Mälardalen University
  • Jesper Ulke, Scania

Please, see also the other projects in the study group

Photo credits: Nic McPhee