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

Purpose and goal:

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

This project intends to carry out two pilot projects to achieve an automated and general/scalable vehicle identification model - also for partial journeys.

Purpose is, among other things, to be able to better monitor behavioral change when we transition to sustainable mobility. Expected results and effects: ** Denna text är maskinöversatt ** - 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 used in follow-up off behavioral change in Stockholm´s and Lund´s decided system demonstrators for faster climate change Approach and implementation: Model development, Testbädd Göteborg (January - June 2024) Model validation, Testbädd Helsingborg (Januari-Juni 2024)

Automation and testing (January-Juni 2024) Internal communication and utilization in Gothenburg and Helsingborg (Mars-Juni 2024) Final reporting and presentation of results s ( August 2024)