Jätkäsaari (West Harbour) is a growing passenger and transport harbour and a new residential district construction site, right adjacent to the center of Helsinki. It is currently the world’s largest passenger port. The harbour is the main connection between Helsinki and Tallinn, with growing mobility and new terminal 2017. Annually 1 million private cars travel on the connection. Jätkäsaari is also a new development site for 18.000 new residents and 6.000 new jobs. Truck freight traffic from and to ferries provide economic feasibility of the ferry routes. A single road leads in and out of Jätkäsaari. This road feeds directly to the largest car commuting junction (70.000 cars daily) from the city center to the western suburbs of Helsinki, creating interference. The site is such that infrastructure investments (bridge, tunnel) are not economically feasible. Also, Jätkäsaari public transport (tram) is out of capacity at peak hours (nicknamed "Hate Tram" in local media).

The data system around mobility the district has been catalogued at Helsinki West Harbour Data and Interfaces (2017, on-line at https://www.hel.fi/hel2/ksv/julkaisut/los_2017-3.pdf), containing description of open data sets, real time data sets, and some closed data sets that are available and/or would be needed towards mobility needs. This catalogue is also the base line for the project but will be complemented with other data sets from private sources and other sectors’ data (like social data).

Actual application details will be co-designed with the city’s stakeholders responsible for mobility (participating units listed above), mobility users, and projects’ experts/researchers. Specific data analysis and machine learning algorithms are co-designed with this group in a process that entails both planning and shared learning. Only through this multi-stakeholder co-design process, we can get hands-on into all ethical, legal, societal and cultural aspects regarding big data and artificial intelligence on mobility planning and understand the attitude of all involved stakeholders towards the use of such technologies in the decision – making process, as well as how these affect their trust.

These aspects will be analysed when big data and artificial intelligence technologies to achieve the following:

-          Identifying Mobility Patterns - Going beyond origin-destination matrices: Better understanding of mobility patterns. Identification of key mobility issues based on data, and key opportunities based on new algorithms for mobility management. Understanding of clusters of mobility users, their needs, their mobility patterns, and current problems with their mobility from the data, focusing especially on user groups with transport poverty.

-          Enabling Machine Learning for Planning -Making mobility data machine-learning -ready: Going through the mobility data catalogue and analysing and improving it from the big data and machine learning opportunities perspective. While some data sets are immediately usable, others will require annotation process, and some data sets will require harmonization work.

-          Better urban planning: Collection of a multi-source big data will help City plan the Jätkäsaari district (and other future districts) better. For example, integrating air quality data, transport data, wellbeing data, locations of schools, eldercare services, with real-world big data where people actually move (e.g. based on 4G positioning), will help the City engage into user- and data-centric urban planning of roads, streets, etc.

-          Designing functional, manageable feed-back loops between mobility system data and mobility management and planning. Blueprints for new concepts, methods and frameworks for future data-driven systems mobility, creating opportunities to integrate mobility management and mobility planning in a real time learning smart city.