VELO - measurement of cycle flows

Purpose and goal

The objective of the project is to create and refine an ML model that identifies cycling behaviors through mobile mast data to create a nationally scalable tool for data-driven decisions about cycling infrastructure. Despite cycling being a central part of Sweden´s sustainable transport goals, there is currently a lack of a uniform and comprehensive method for measuring cycling traffic and cycling behaviors. Existing techniques provide valuable but fragmented information.

Expected effects and result

Short-term effects: • Improved understanding of how cycling takes place in participating municipalities • Better decision-making basis for new cycling investments • Increased opportunity to evaluate effects of cycling promotion measures Long-term effects: • Increased share of cycling trips • Reduced carbon dioxide emissions from the transport sector • Improved public health through increased physical activity • More even distribution of cycling resources between different demographic groups

Planned approach and implementation

The VELO project is a pilot to develop and test a service for identifying cycling behaviour. The project uses an agile development method with iterative improvement of the model based on continuous feedback from the municipalities. Tests are carried out against existing cycle counters to validate the reliability of the model. Established machine learning methods are used for data analysis, with a particular focus on pattern recognition of movement profiles.