This project investigated new methods to enhance the use of micromobility vehicles, focusing on the growing use of bike and pedestrian road networks. It is a preliminary study that utilized such vehicles for gathering images and applied machine learning to expand knowledge of infrastructure. The study successfully demonstrated the feasibility of collecting images of the road network, but also identified unique challenges such as varying angles and specific obstacles like gravel that need attention. Due to GDPR concerns, external cameras were preferred over built-in ones in vehicles like e-scooters. The project also looked into how data could be shared back with users and stakeholders. It found established methods for handling static data but noted a lack of standards for dynamic obstacles. Efforts to participate in a U.S. Department of Transportation-led standardization initiative have begun. Additionally, the project crafted a preliminary policy for delivery robots. This policy includes geofencing, speed limits, and operating schedules, which Helsingborg has translated into machine-readable code using the Mobility Data Specification. Hugo Delivery have adjusted its platform to comply with these digital policies.

The final hypothesis of the project revolves around a pilot concept for autonomous delivery robots. This concept is grounded in on-site testing, workshops with municipal and commercial stakeholders, reviews of existing global delivery robot pilots, and Helsingborg’s sustainability goals. It also takes into account the strengths and weaknesses of delivery robots. The idea is to pilot these robots as mobile delivery lockers in suburban areas, avoiding city centers with high traffic. This approach, termed 'community robots', envisions the robots operating within a specific area, with daily battery swaps. By shifting parcel deliveries from vans to these robots, a significant reduction in traffic accidents and emissions in residential areas is anticipated. Moreover, parcel delivery companies are expected to see a marked increase in transport efficiency. In a fully scaled system, this could lead to a reduction of about 40-50% in the last-mile fleet.