Munich Case Study

Learn more about how Parknav® provided city of Munich the missing information to make a smarter, more green city





 The City

On a mission to become a greener city, the City of Munich is pursuing a vision of implementing changes to have the fewest number of cars on the road. In order to achieve these new multi-mobile solutions (buses, trains, shared cars, electric scooters, Uber, Lyft, cabs), and implement them into the city, understanding the specific demand of the city center is required. The demand includes not only how many people are traveling, but also how they are traveling, where they are traveling to, and when exactly they are traveling too. How can this data be collected?

 The Challenge

In partnership with T-Systems, Parknav was asked to find the most accurate estimate of how many passengers travel through some specific sections of Munich in a day. T-Systems served as the direct connection with the city of Munich and provided Parknav with the manpower to complete the challenge. It was divided up into three separate tasks in order to prove the complexity and adaptability of the technology, including the following:

  • To determine how many passengers travel daily on the subway, more specifically on the Studentenstadt – Freimann route in one day (7/20/21) in both directions on the Munich subway.‌
  • To determine how many people use each different means of transportation (walking, bus, bus, tram, or car) to make their way across the Reichenbach Bridge, again in both directions and on the same day as the first task (7/20/21). The information was to be provided within 15-minute intervals.‌
  • To determine how many people come and go from the main site of the Stadtwerke München in Moosach in a day (7/20/21). The information was to be provided also within 15-minute intervals with a source-target relationship and a spatial reference chosen as technically as possible.


 The Solution

How we did it

Using Parknav’s advanced GDPR compliant artificial intelligence technology, the same method was applied throughout all three settings and tasks, with a sensor set up to detect nearby mobile phones (it was all completely anonymized to respect privacy rights). This was done with the understanding from past studies by Parknav (in collaboration with Deutsche Telecom) that a direct correlation between mobile devices present and the actual number of people in the vicinity can be found using AI and Machine Learning.

Some additional materials were used as well for specific tasks to be more accurate, such as subway timetables and a previously trained model of human movement based on cell tower data.

The results

Parknav successfully completed the Munich challenge, outdoing all of its competitors by providing the most accurate predictions for all three tasks. This goes to show that Parknav’s advanced technology can be applied in a variety of settings, and can be efficiently deployed and adapted in any city. This proves especially true since 2 out of 3 locations were provided only on the day of the challenge! Talk about speed.

Parknav not only managed to provide the most accurate estimate of how many people travel through Munich in a day, but also managed to provide the results in three separate timelines: near-real-time (within 2 hours of data being collected), 1-day delay, and 1-week delay, each of those with increasing accuracy respective to the time difference.

While specific results can’t be provided for confidentiality reasons, the ultimate goal here was to collect the necessary data in order to start raising the level and diversity of mobility in Munich on a permanent basis to reduce traffic-related emissions – which is exactly what Parknav’s technology proved capable of.


After successfully estimating and predicting the passenger flow between train stations in this challenge, Parknav has proven how quickly adaptable it's AI parking prediction data is to other verticals - showing the promising potential of our technology to make cities of the future smarter, cleaner and greener.

Igal Elon Chemerinski, CRO Parknav

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