Commuting 2.0

[tervo]

Summary (~100 words)

Quality of life is affected greatly by our usual day to day activities and how we commute to and from our place of work plays a big part. Fortunately, this chore has been made very easy by routing apps that use data and routing algorithms to tell us how to get from A to B. However, especially for bicycles, pedestrians, and other light traffic, there is a dimension overlooked by routing applications: conditions and weather. There is a wide array of observation and forecast data about our environment that can be used to provide a great service to commuters.

Key issues to questions to answer/investigate

We are looking for teams to produce prototype routing applications that, broadly speaking, make use of "comfort" as a factor in the routing equation.

• How can data be used to assess the comfort level of a path?
• How can comfortable environment-friendly commuting be increased?
• How to assess safety and conditions of paths using data?
• How can the comfort/safety data be best used to help commuters?
• Can these methods be applied to help someone who wants exercise instead of comfort

Required knowledge and skills

• Experience with routing algorithms and software
• Software development skills necessary to modify/augment the routing software
• Practical experience of commuting with bicycles will probably help you

Offered datasets

• Forecast data on current and future weather (FMI)
• Current and historical data on measured weather (FMI)
• HSY air quality sensor data (Vaisala)

The weather datasets are available at http://beta.fmi.fi/data/3/wfs/sofp. The data from FMI is licensed under Creative Commons Attribution 4.0 (https://en.ilmatieteenlaitos.fi/open-data-licence)

Other relevant datasets

• OSM or other base map for urban routing
• Elevation models (National Land Survey of Finland)
• HSL Developer Community

New technologies to test or evaluate

• Core data is delivered through an experimental WFS 3.0 server (https://github.com/vaisala-oss/sofp-core)
• Data is in a new JSON data model specifically for observations and measurements (https://github.com/opengeospatial/omsf-profile)

Note. WFS 3.0 implementation list is available at https://github.com/opengeospatial/WFS_FES/blob/master/implementations.md

Offered personal resources

• Finnish Meteorological Institute, Vaisala and Spatineo will each offer 1 working day per week to help teams
• We will provide a slack channel for all participating team members

Offered or suggested tools

• Open source routing software

Desired outcome and presentation

• Online demo or proof-of-concept software delivered through a web application is preferred
• Ideas relevant for the challenge that was not implemented due to time constraints or technical issues should be included in a presentation
• Comments on the data encoding (OMSF) and delivery method (WFS 3)

Offered benefits for the teams

• Unknown at the time, will provide information later

Background & context

• This challenge is about helping urban living by using data on weather conditions
• Parts of the weather information are INSPIRE data
• Vaisala has participated in Junction 2018 with the theme “Actually Useful Weather” (https://www.vaisala.com/en/blog/2019-02/winners-vaisalas-challenges-junction-2018-hacked-weather-and-sauna)

Cooperation with other Challenge Partners

• This is a co-operation between Vaisala, Finnish Meteorological Institute and Spatineo
• Data providers with relevant data are welcomed to join

Submitting organisations

• FMI and contact point: Roope Tervo ([email protected])
• Vaisala: Jani Kettunen ([email protected])
• Spatineo: Ilkka Rinne ([email protected])