This challenge focuses on mapping the transport habits of school children to and from school. The aim is to provide decision-makers and planners with the necessary evidence base and analysis tools that can qualify both political objectives and physical planning.
European cities are working intensively to reduce the climate impact associated with mobility, often in combination with improvements in public health. A solid evidence base is essential for this work, and many cities and actors are therefore looking for data that can support their decisions.
Why you should join this challenge
If you want to be part of the future of AI-driven mobility this is a great opportunity to collaborate with cities and industry leaders to develop data-driven solutions that promote cycling and public transport. Read more about why this solution call offers unique opportunities when particpating, or just following, the City Challenge.
Register here for the online challenge:
🗓️ If you want to pitch a solution (for suppliers)
🗓️ If you only want to follow the challenge (for cities and other interested organisations)
Description of the case owner
Guldborgsund Municipality is a rural municipality in the Region of Zealand, Denmark, with approximately 60,000 inhabitants and an area of 901 km2. There are 13 primary and lower secondary schools in the municipality.
Challenge description
What insight should the data/analysis provide and what action should it potentially lead to?
Guldborgsund Municipality wants to get a clear picture of the transport habits of school children. This stems from a political desire to promote cycling and increase the use of public transport among school children, as this can benefit both the climate and public health. However, the municipality lacks knowledge regarding school childrens' modes of transportation to school, their precise routes, and the factors that influence their choices. There is a particular focus on whether road safety plays a role, and what measures may be relevant in that case.
The purpose of this challenge is therefore to create a solid decision-making basis that can support the municipality's goals of:
- Increasing the number of children cycling to school.
- Improving and optimizing public transport for school children.
- Ensuring safe school routes for all students.
- Measuring the effect of cycling campaigns.
- Optimizing bus planning.
- Gathering evidence for where new cycle paths or road safety measures should be built.
About the site
The current case is a school in a small town, with a larger rural area as an adjacent area/school district. The school has approximately 200 students from 0th to 10th grade. Download a PDF of the aerial view, and visit the location on Google Maps.
Which data points are to be collected and analysed
The following types of student transportation and preferences are of interest to be mapped:
- Route selection (flow analysis)
- Origin-destination
- Modal Segmentation:
-Walking
-Cycling
-Scooter
-Car (dropped off by parents or others)
-School bus
-Public transport (bus)
-Other
Special features or requests for the solution and the process
The solution should preferably be mobile so that analyses can easily be made at other sites.
The solution must be able to take into account seasonal change, weather conditions and the like. Ideally, the solution should map the selection of a given route (flow analysis) for each mode of transportation.
The solution must be able to provide information that supports the measurement of impact of public initiatives/campaigns. The solution does not need to be ready for implementation and can be developed in a collaboration - potentially between several different vendors and case owners.
Available data sources and systems
Attached maps show the school's area with parking space, kiss-and-ride area, bus stop, nearby kindergarten and institutions.
It will be possible to carry out various tests by agreement, possibly in cooperation with other partners. The municipal GIS map can support the use case. LoRaWAN infrastructure and an IoT platform (OS2iot) can be made available. As there is potentially personal data and artificial intelligence linked to the solutions, external expert assistance can be obtained through collaboration with CitCom.ai and regional European Digital Innovation Hubs (EDIH) to ensure compliance, validation of solutions and support for development.
