Human-Centred Twin Smart Cities Living Lab

When the European Parliament compared 468 European cities, Helsinki was ranked within the top six. Propelled by agile city development policies, Helsinki is planning to stay at the cutting edge in the future: open data platforms, smart grid technologies, networked LED street lights, urban mobility, smart garbage bins, energy efficient buildings, water management up to autonomous buses (Robobus) are among the Helsinki Smart City solutions.

Within IoT-NGIN, we plan to step further towards Mobility as a Service (MaaS) in Twin Smart Cities use cases. The ambition of trial#2 is to adopt an innovative cross-border-by-default twin city context with the city of Helsinki in Finland and the city of Tallinn in Estonia. The use case will be built on top of Finest Twin Cities platform, which facilitates collaboration and open innovation via cities’ common data models for AI data capturing and processing on urban level. Geographically, it will be hosted at the Jätkäsaari Mobility Living Lab, the location of the ferry terminal for commuting ferries between Helsinki and Tallinn, where the number of visitors is doubled during the peak hours, leading to congestion and sub-optimized travel experience. Jätkäsaari is a new residential area with 20.000+ future inhabitants and workplaces for 6000 people, including various hotels and office facilities. It also encompasses the main part of Helsinki’s passenger harbour. However, geographical features and historical development significantly limit connections to the mainland and obstruct traffic in the area.

Use case Applications

Traffic Flow Prediction & Parking Prediction:This use case will utilize basic geography, street-level, public transportation, weather and noise data, along with historical data to model and train distributed AI models on traffic flow and parking prediction in a greedy layer wise fashion. Traffic and parking prediction ML models will be federated at the edge cloud, while 5G communications will be used for interconnecting sensors, IoT data will be gathered in real-time and stored at the edge, while smart AI-based simulations will perform what- if analysis when transport is interrupted, e.g. due to extreme weather, man-made or technical hazards.
Crowd management:This use case will demonstrate the use of open data, user data and IoT data on traffic fluency through cameras and radars installed at the bottleneck intersections for crowd steering based on the application of AI. The use case will also consider crowd management of the busy Helsinki-Tallinn cross-border commuter harbour both during peak hours and in case of unexpected events, based on predictive algorithms.
Co-commuting solutions based on social networks:This use case will combine IoT data with virtual citizen generated IoT data from social networks to demonstrate the use of advanced AI in provision of co-commuting solutions both in neighbourhood and cross-border level. The use case will exploit the Urban Open Platform and Lab (UOP.Lab) developed in Finest Twins project.

IoT-NGIN as Twin Smart Cities IoT Differentiator

Agile and adaptable open cross-border smart city platform application for multiple user groups and use contexts
Combination of AI and heterogeneous data sources including user volunteer data on social networks for the delivery of optimized on-demand services
Standardised cross-border data models for pilot replication and novel, distributed IoT business models through active engagement with the local developer communities in Helsinki region and Estonia