We’ve all been there.  It seems like every time you go downtown you end up stuck in traffic and then have to drive in circles for ten minutes searching blindly for a parking spot. Even if you have one of the “digital” parking apps you can only park in a limited number of “in-network” spots. We think we’ve got a solution for this mess. In the video, above you’ll ride along with a real driver during one of our field tests leveraging fetch.ai autonomous economic agents and AI-enabled smart parking garages. Further down in this article we’ll examine the environmental, social, and technical aspects of our “M-Zone Parking Liquidity Protocol” approach to solving the parking riddle in cities.

Currently, Parking is Like Flying Half Empty Planes

Today, the vast majority of parking spaces in cities are locked up in various forms of reserved parking. Much of this capacity is reserved 100% of the time regardless of whether it is needed which leads to parking spaces sitting unoccupied mere meters away from where demand for parking is very high. High demand leads to more parking infrastructure being built. This in turn causes massive CO2 emissions for the building materials required (namely cement which requires 900 kg of CO2 per ton to produce). Cement is the source of about 8% of the world’s carbon dioxide (CO2) emissions. In addition drivers Just in Germany, drivers spend an average of 41 hours a year searching for the elusive parking spot at a cost of €896 per driver in wasted time, fuel, and emissions and the country as a whole €40.4 billion. One of our basic assumptions is that if parking infrastructure must be built it should be used as intensively and efficiently as possible to prevent additional unnecessary infrastructure from being constructed. For this to be possible we need intelligent parking systems that provide the correct incentives and nearly perfect information about usage without sacrificing privacy.

Most people wouldn’t compare parking infrastructure to airplanes but it’s actually a relatively good comparison. We all know that aviation is a major contributor to C02 emissions and airlines make every effort to ensure that every flight is as full as possible including “codesharing” where two airlines sell tickets on the same plane to ensure the flight doesn’t fly empty. They also use dynamic pricing to alter customers’ demand curves for particular flights at a particular time and price. What we’re proposing is analogous in the world of parking.  Currently, the world of parking could be compared to flying all the planes half empty all the time and adding more capacity constantly despite increasing costs and environmental impact.

In this context, we can define waste as being any time that parking spaces are empty despite there being demand for those spots. Our Parking Liquidity Protocol allows us to recycle already existing capacity to meet current and future expected demand for parking instead of building new parking infrastructure and capacity.

Bringing the Vision of a Parking Liquidity Protocol to Life

Parking lots need to become aware of their full state and become able to communicate their fill state to users directly over a mobile wallet app AND to automatically incentivize these users to drive and park less by rewarding behaviors that are more sustainable. This vision led us to leverage the fetch.ai blockchain. The fetch blockchain includes “autonomous economic agents” which are essentially AI-powered programs that make economic decisions on behalf of users or machines and then execute economic transactions without human intervention on the blockchain. In partnership with the fetch.ai team, we conceived and built a number of edge computing devices with integrated uninterruptable power supplies, 4G modems for connectivity, and high-resolution cameras that can be deployed quickly and easily at parking garage entrances and exits.

Here we’re preparing the Autonomous Economic Agents for deployment on-site at the Connex buildings.

These edge computing devices (raspberry pi – based) are running computer vision algorithms that allow them to identify license plates on incoming and outgoing vehicles and to calculate how full the parking lot itself is.  They are networked together with one another and with a “coordinator” agent which aggregates the information from daughter nodes and determines dynamically which micro-incentives should be sent to any individual driver at any one time. We’ve also built a web app that allows drivers to see the fill status of the lots how much their earned micro incentives, reward rate, and how much this earning rate will be reduced by parking in a particular lot at a particular time. Not parking at all is rewarded most but parking where and when parking demand is low also gets some rewards. Last but not least there is a settlement layer that sums up the micro-incentives that a driver has earned through parking less and parking more efficiently and makes payments in FET tokens to the driver wallet.  These tokens are tradeable on the open market and are directly exchangeable for Euros or USD. It goes without saying that privacy by design is at the core of our system architechture.

Critically, these edge nodes are managed by a Kubernetes-based container orchestration system which allows us to do over-the-air updates to the hardware without retrieving it from the field. This greatly increases the scalability of our system because it allows us to install the hardware which provides intelligence to the parking garages once and never touch it again unless physical maintenance is required.

A two-node system has been field-tested successfully at the Connex building complex in Munich.  These buildings are owned by Datarella Partner Hammer AG with whom we ready partnered to execute one of the first regulatory-compliant real estate tokenization projects last year (ConnexCoin). The money for the driver micro-incentives comes from the savings of both commercial real estate developers like Hammer AG and their tenants.  Now with our system, they have the means to share parking capacity across nearby buildings. Hammer AG alone has 5 buildings on the same street in Munich within the Connex complex so it’s really realistic to encourage drivers to distribute parking load across the neighborhood and walk a few minutes further to reach their end destination.

What’s next?

We’ve got a lot on our plate for the next months.  We’re looking to build on the success of the field trials to augment the parking liquidity protocol with a bunch of new components. We’re working on integrating a self-sovereign identity framework to beef up the privacy of our authentication methods. Parallel to this, we’re building out the user interfaces and onboarding processes working with our partners to expand the M-Zone parking liquidity protocol for payment and reservation. On top of that, we’re designing an open protocol tech stack to enable the search and discovery of parking lot ID’s and states in a chain agnostic manner. Keep an eye out for a technical deep dive in the coming days where we’ll get into the nitty-gritty of how the system works!

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M-ZONE will be launched in the Connex Buildings and will utilize multi-agent blockchain-based AI digitization services to unlock data and provide smart mobility solutions in its commercial real estate properties in the city center.

“Landlords, as well as the City Council, are interested in optimizing the parking space management, to allow for available parking for all employees of corporate tenants while organizing the traffic flow and preventing commuter traffic jams,” said Michael Reuter, CEO of Datarella. “Our system incentivizes community use of public transport through a tokenized incentive system while reducing the congestion that accounts for a great deal of Munich’s CO2 emissions.”

M-ZONE will solve the needs of various stakeholders:

• Individual commuters and drivers: save time, money, and reduces driver stress

• Property owners: simplified approval for new development projects

• Tenants: lower rent through dynamic parking spot sharing

• City Council: optimized traffic flows

• Environment: less negative impact through saved CO2

Upon implementation, AEAs (Autonomous Economic Agents) will support the sustainable and efficient use of city infrastructure in Munich through an application where they will autonomously negotiate the ‘price’ of parking spaces between the holders of parking spots, and those looking for parking spaces. Users can earn rewards in the digital currency if they choose less popular or in-demand parking spaces (or do not use the parking lot at all on some days). The Carpark AEA determines the reward levels to maximize resource usage.

“Fetch.ai provides a decentralized framework for building and customizing autonomous AI agents to carry out complex coordination tasks,” said Humayun Sheikh, CEO of Fetch.ai. “Our vision is to connect digital and real-life economies in order to enable automation over a decentralized network and change the way we use data.”

Users are incentivized to reduce their number of car trips to the Connex and adjacent corporate offices through a reward system which is measured by the utilization of parking spaces. Each registered user who is a regular car park user will be rewarded with a certain amount of tokens per minute for not parking at the parking lot. As soon as a car or its related wallet address is registered as parked by the Carpark AEA, the token airdrops to this wallet stop/slow. The number of tokens rewarded per wallet and minute depends on the current utilization of the parking lot.

“Assuming there is a 10% reduction in car usage across Munich alone, the city would see a 34.000 tonnes annual CO2 emission reduction,” continued Reuter. “Scaled up to cover all of Germany, that equates to 1.7 million tonnes CO2 reduction, annually. This smart city solution has the potential to penetrate huge markets simply by tapping into wasted data and utilizing it efficiently.”

For more information, please contact us!

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