Overview

As part of the "MUCmoves" challenge, Bachelor's and Master's students from the faculties of Computer Science and Business Administration at HM Hochschule München University of Applied Sciences worked together with the Mobility Department of the City of Munich. The mobility department has the vision "to change transport in Munich for a sustainable and liveable future" and hoped for creative ideas from the students to drive their vision forward.

Problem

To plan projects for traffic change and to make sustainable decisions, complete and reliable data is needed, e.g. on stationary and moving traffic. In addition, all modes of transport (cars, pedestrians, cyclists and public transport) should be mapped as far as possible.

The traffic planners in Munich still lack such reliable data. Attempts have already been made to collect data with the help of sensors, video recordings and manual counts but so far these results have not been sufficient. Likewise, calculations and assumptions are too imprecise and risky to rely on for an ambitious goal like a traffic change.

In addition, it would be important to have a uniform and easily understandable form of presentation. Because only if data can be compared properly, they offer added value for their viewers.

Solution approach

At the beginning, the problem had to be clearly defined with the help of a challenge statement. It said:

"How can we improve the data situation on flowing traffic (motor vehicle, pedestrian, bicycle and public transport) in Munich?"

To solve it in the best possible way, the team worked with the Scrum method and the working backwards approach, which is also used at Amazon. During their work process, the students received support from Amazon Web Services (AWS).

The digital solution that the students implemented, which provides both more data and better visualisation and comparability, is called "MUCmoves". MUCmoves is able to collect and standardise traffic data from a wide variety of sources in a central database. A filter function allows the selection of place, time, date and mode of transport, so that traffic planners can display the data relevant to them. Depending on preference, the data can then be visualised as a line, bar or radar chart or as a simple table.

The website that the students created for MUCmoves can be found under this link and is already fully functional. It can be used by anyone interested and for further internal processing, the traffic data can also be downloaded both as a table and as raw data.

In the case that other companies or private persons would like to make traffic data available, they can upload their data to MUCmoves and thus increase the platform's database. To ensure that only reliable data sources are fed in, the access rights that allow the input of data are assigned by the mobility department itself.

Next steps

To improve the visualisation of the data, the team worked on two future projects.

The dynamic view of traffic flows is to combine all traffic data available in the database. In this way, the map, analogous to a weather map, could reflect the data entered over time. Another idea was to show possible causes, such as bus or train delays or accidents, and thus explain increased traffic volumes.

The vision of MUCmoves also includes an interactive map that automatically displays the traffic counting points that are available in the database. These records would then appear as points on the map and clicking on a point would display all the information available on that count.

As an open-source project, we invite all tinkerers, coders and transport planning enthusiasts to help make MUCmoves fit for the future and to help realise our visions.