Addressing climate change requires radical transformation of our mobility behaviours. As a result, urban Mobility is rapidly transforming with new usages, vehicles and infrastructures.
Urban mobility infrastructures
Urban Mobility Infrastructure
Challenge: optimising the localisation of urban mobility solutions
But the deployment of new mobility infrastructure (such as shared bike stations or electric vehicles charging stations) raises important challenges. Indeed, the localisation of each station must consider environmental, social and economic criteria.
Mobility infrastructure is an important investment for a city or region. Their localisation must be planned to maximise their usage but also to ensure a fair access across social groups. A deployment must also take into account the environmental specificities of the territory: different CO2 footprint of urban and rural areas, local geographical constraints, points of interest, demography, typologies of neighbourhoods, and connection with existing services.
Approach: exploring and confronting various city datasets
Taking these different dimensions into account to deploy mobility infrastructure requires to solve a multi-parameter optimisation problem. Kentyou approach is to extract knowledge from available data and to use data to answer complex questions such as this one.
Cities often have access to large numbers of datasets. But they are generally considered individually and not integrated.
Solving an optimisation problem like this one requires to consider in parallel multiple datasets, coming from different domains and city services. That’s where the Eclipse sensiNact platform is essential. It has the ability to integrate different datasets rapidly and efficiently.
As part of the EUHubs4Data project, where Kentyou developed this usecase, we integrated over 260 datasets from the city of Barcelona. This allowed us to use demography, professional mobility, environment, culture/leisure, trade and employment datasets to optimize our solution.
Our methodology then focused on the exploration of these data. This allows us to detect key areas in the urban or rural tissue. From then we can extract valuable insights and finally provide recommendations for optimal locations.
This approach present an important innovation compared to the state of the art, and even resulted in a scientific publication at IE2022 18th international conference on intelligent Environments : S. Kleisarchaki, L. Gürgen , S.M. Kassa, M. Plociennik and D.G. Vidal. “Optimization of Soft Mobility Localization with Sustainable Policies and Open Data”, IE’2022.
Solution: recommendation engine for mobility infrastructure
As a result, we have integrated this recommendation engine in the Kentyou Eye hypervisor. The tool allows the city councillors to create a template policy, targeting specific types of populations and areas. They then receive recommendations for the localisation of new stations in the city within the budget they defined.
This tool is highly adaptable, and can easily adapt to new territories, integrating new data sets and taking into account local demands and constraints.
As such, our goal is to make from Kentyou Eye a complete solution for handling the mobility and its impact in a territory. This requires rapid integration of specific local datasets (enabled by Eclipse sensiNact), data analytics capabilities and finally AI solutions that can provide future perspectives and recommendations.
Kentyou will continue to work on this use case and its extensions to other cities and territories. If you’re interested by this use case, or by how we can help you make the most out of your data contact us.