Identifying mode of transport for partial trips - when analyzing movement using mobile network data

This text is machine translated

The objective of the project is to develop a scalable, automated and self-learning pattern recognition model that shows which means of transport people choose during an entire journey - door to door. The model vill be based on the Movement Analytics-method of analysing human movement using mobile network data.

Expected results and effects 

• Better understanding of travel by means of transport in traffic planning
• The possibility to follow up behavioral changes when society changes to sustainable travel
• Make visible the consequences measures to reduce car use in city centers have on people´s travel habits - per means of transport

Results from this project will be able to be used in the follow-up of behavioral change in Stockholm and Lund's decided system demonstrators for faster climate transition.

Planned approach and implementation

• Model development, Test demonstrator Gothenburg (January - June 2024)
• Model validation, Test demonstrator Helsingborg (Januari-Juni 2024)
• Automation and testing (January-June 2024)
• Internal communication and utilization in Gothenburg and Helsingborg (March-June 2024)
• Final reporting and presentation of results ( August 2024)

Take part in the project's documentation