Helsinki's testbed for smart mobility.

The pilot with Dreamwaves addresses the lack of detailed accessibility information. The pilot was selected through the EIT Urban Mobility’s RAPTOR program for the challenge posed by Mobility Lab Helsinki (read more about the challenge here).

The solution is based on a mobile application that uses augmented reality and allows users to collect and annotate obstacles and issues on the streets, such as blocking objects like construction sites, e-scooters, potholes etc.

MarshallAI’s pilot project helps to understand, maintain and assess the demand for and future needs of bicycle parking services. Anonymous real-time data can be collected on the use of bicycle parking facilities using computer vision and video automation. The solution monitors the utilisation rate of bicycle parking facilities, bicycles parked at parking facilities for an extended period of time, as well as the number of bicycle thefts or attempted thefts.

The Norwegian Bikeloop AS‘s smart bicycle parking solution is a secure modular bicycle locker for eight bicycles. Booking bicycle parking is possible for anyone via their app. The bicycle locker design takes special groups into account, and the lights and voice guidance make the locker accessible.

In Gispo‘s experiment, the goal is to increase the potential use of the city’s 3D materials by enabling the utilisation of 3D data in QGIS for a wider user base within the city organisation. 

Webion participates in the experiment with the aim of developing the measurement of urban spaces. The technologies in the experiment can be used in the future to produce measurements of urban spaces that promote the development of digital twins of cities as well as maintenance. In the experiment, among other things, street-level imagery is refined using artificial intelligence. 

Sitowise is developing a new and easily scalable method in which data from various traffic infrastructure sources is analysed and classified automatically using commercial tools and artificial intelligence algorithms. 

The results of the pilot procurement will be used for the implementation of the CityGML 3.0 street environment test data in 2024.  

The three-month case study pilot was carried out in collaboration between Drover AI, Voi Technology, Vianova, VTT and Mobility Lab Helsinki. Drover’s PathPilot solution was installed on 25 of Voi’s e-scooters. In the first phase, it collected data about the infrastructure the e-scooters ride on. The second phase of the pilot introduced audio signals related to rider behaviour (such as alerts when riding on pedestrian streets, parking).

The case study produced a white paper analyzing the observed rider behaviour. The results were promising. For example, riding e-scooters on sidewalks was reduced during the test period. Continuation of testing with an expanded time and scope might be considered. Read more about the results.

“From the images collected by the scooters, we obtained accurate information about, among other things, potholes on sidewalks, which can be a safety risk for road users. Additionally, several situations were observed where, for example, a parked car blocked access to the bicycle lane,” says Juho Kostiainen, Project Manager of Mobility Lab Helsinki.