Fighting Traffic Gridlock

Can the use of self-driving electric robotaxis in a city like Munich with over one million inhabitants prevent imminent gridlock? An interview with Fares Agua, author of a study on this topic.

You are the author of the current study with the long name “Simulating an Urban Mobility Solution Based on Self-Driving E-Robotaxis in Munich” [Simulation einer urbanen Mobilitätslösung basierend auf autonom fahrenden E-Robotaxen in München]. What was the goal of this study?

Munich is a city with over one million inhabitants, making it the most densely populated and the fastest growing city in Germany. It is already well-known for its daily traffic jams, loud traffic noise and exhaust emissions, all of which make new ideas urgently necessary.

Together with the Chair of Vehicle Technology at the University of Technology of Munich, Matthias Kempf, a partner at Berylls Strategy Advisors, I have therefore worked out three practical scenarios for operating an electric robotaxi fleet that will cover the entire city center (which is currently traveled by car) as a mobility service. With this outstanding vision, we wanted to find out whether individual mobility in Munich could be made more efficient, greener, more convenient and less expensive.

What were the results?

It works. In scenario II of our study, we found that 18,000 e-robotaxis have the potential to replace 200,000 private cars in and around the Munich area. The good thing is that, in addition to city center traffic, the service also reduces roughly 20 percent of the commuter traffic whose origin or destination is outside the city limits. While today's privately owned cars are used less than five percent of the time, the time that e-robotaxis spend on the road is more than 50 percent. According to our calculations, an e-robotaxi fleet would make 593,000 trips with an average distance of 5.7 kilometers. As a result, the system is very customer-friendly with very short waiting times – less than three minutes. Even during morning commuter traffic, less than five percent of commuter customers had to wait for 18 minutes. E-robotaxis have little impact on average daily traffic because we were able to keep the number of empty runs low.

What can customers expect to pay for such service?

The entire process is very inexpensive. In our cost model, which takes into account the most diverse factors, we have estimated a price of 16 cents per customer kilometer. That is comparable to the price for using public transit and is considerably lower than driving one's own car. Based on this, a monthly mobility flat rate of EUR 99 can be offered to e-robotaxi users in Munich.

How are the e-robotaxis distributed throughout the city?

Our scenario follows the customer-friendly free-floating approach, which means there are generally no pre-determined stations from which travelers are picked up. If possible, the robotaxis remain at the destination of the most recent customer until they receive the next call or roll up to a charging station. This strategy is an important component in minimizing the number of empty runs.

Nevertheless, scenario II contains five so-called mobility hubs along the edges of the service area, similar to today's park & ride facilities. Why?

Commuters who stream into Munich mostly in the morning and then back to the suburbs in the evening are an important customer segment that is responsible for a majority of the average daily traffic at peak times. At the same time, commuters represent an enormous challenge for robotaxis due to this asymmetrical behavior because they require that the fleet is redistributed. We wanted to explore the handling of commuters at mobility hubs and found that e-robotaxis can redistribute themselves strategically well across the five hubs and do not have to be shifted by making additional empty runs. Perfect!

In your study, you looked at the use of a shared mobility solution for the 1.5 million inhabitants of Munich. How transferrable are your findings to megacities like Tokyo, New York or Shanghai?

We are convinced that even in cities with considerably larger areas to cover and higher demand than Munich, the use of e-robotaxis will work without restriction. Even the technological distance limit of 150 kilometers should not be an obstacle because it depends mainly on the average speeds driven.

Interested in learning more about the study?

Contact Matthias Kempf (matthias.kempf@berylls.com) (Partner) or Fares Agua (fares.agua@berylls.com) from Berylls Strategy Advisors.

About the study’s author Fares Agua

Studied mechanical engineering and business administration at the University of Technology of Munich and at the Center for Digital Technology and Management. During a seven-month sabbatical in Singapore, Fares worked on the simulation of electric taxis. Since 2017, he has worked as a consultant with Berylls Strategy Advisors, specializing in new mobility.

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