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Congestion and queues in rush hour traffic in and around bigger cities cause serious problems as they involve millions of commuters every day all year round. We therefore started to research how to solve this increasing problem that costs the world a lot of money, first of all due to less production since trucks and vans can’t get around and second of all because commuters spend hours every day doing nothing. Luckily it turned out that the problem to congestion in the traffic has many similarities to queues at attractions in theme parks, something which we have many years of experience in working with and have developed a sophisticated algorithm to solve.

A commuter route has several pain points, like intersections, roads with lesser capacity, etc.. This is the same situation in a theme park, where some rides are more popular than others or have lesser capacity.

In both cases the result is the same; queues are built up at the pain points and spreads to the whole route or theme park.

Another similarity is human behavior. Both commuters and Theme Park guests don’t behave logical. So, use of statistical models are not possible. solves this issue by our unique algorithm, and takes the following important points into consideration. 


The concept is simple, logical and holistic.

Capacity of infrastructure and ability to manage the capacity are absolute demands. Otherwise all other smart city and transport services/concepts, like UBER, carpooling and, autonomous cars, can’t operate and grow.

Congestion in and around bigger cities will triple over the next 5-10 years.Even without all  above new services and means of transportation. 

Huge amounts of construction of new roads are only partial solutions and don’t work over even a short time, due to commuters are adjusting their behavior. 

The odds are extreme.So, is there any other way than ban all private vehicles  from cities or close off the access by introducing high priced road pricing?

Yes, we believe can be a solution. Combined for ex. with road pricing.

Banning of private vehicles in bigger cities raises another huge problem - Ghost Towns!

It has already started all over the world due to more and more e-commerce, which causes empty retail outlets and less guests to cultural venues in the cities.

User involvement

Drivers must be part of creating the solution which also is in line with what we all want, to have control over our “doings”.

Knowing when to be arriving is for many drivers as important as the time it takes.

Are we all planners? No, but about 70-80 % are.  Laissez-faire types are and will always be stuck in any lines!


The solution is per definition PROACTIVE. All other traffic management and activities are mostly reactive.

Pricing is a political issue and decision. However, if it's priced service, dynamic pricing and other means can be a parameter to increase capacity as well. Or to favor electric cars or….

Capacity has to be monitored and dealt with per minute and per road/route or per lane. So, for ex 30 percent of capacity for booking and 70 percent for stand by drivers or 100 percent and anything in between. 

User platform/database

All the data about users, routes*, preferences, behaviors, capacity peaks and lows can be used for:


  • Added services, carsharing, carpooling and……

  • Direct one to one or many communications with targeted drivers

  • A unique pool of absolute data for traffic and city planners, they have never had before


Due to the interrelated benefits of above the capacity in many cases can be increased with up to 50%.

The service can also operate outside rush hours. 24/ 7.

Low CO2 footprint, when the cars are driving they pollute less, when in queues the pollution is 100 %. Of course the servers running use energy . No CO2 footprint caused by construction. Production of concrete cement  has a bigger CO2 footprint than Airline combined globally,

However, when the degree of EV grows the CO2 will be of minor importance.

The booking algorithm

Our booking Algorithm (BA) is smart. Let’s say we have a route with 4 pain points and thus 4 different capacities and different distances between the pain points. Before the “itinerary” is made for a driver, the BA adjust for the differences in capacity, the driving time between the pain points, the actual time and requested time slot and of course the number of users on the distances between the pain points and in total.

Have been volume tested with success in some of the biggest theme parks in the world with many thousand users per hour.

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