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More rides, happier customers, fewer rebalancing tasks and higher fleet utilisation.

Updated: Feb 26

That might sound like your idea of heaven, but actually it's the outcome of Anadue’s biggest ever upgrade to Pathfinder’s Machine Learning rebalancing algorithm. Without you lifting a finger, your rebalancing teams can be working smarter and achieving unprecedented results.

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Relax, Anadue takes care of it

In side-by-side trials, Pathfinder’s rebalancing algorithm was already proving better at identifying when, where and how many vehicles should be moved to maximise revenue, compared to local rebalancing teams deciding where to drop vehicles, but our customers challenged us to do even better.


We accepted this challenge and decided it was an opportunity to take a new look at all the assumptions we had previously made when designing our first-generation rebalancing solution. Our second-generation solution is even better at predicting demand, and identifying how many vehicles are needed in each location to meet that demand.


New factors that we considered when creating our second-generation rebalancing solution were:

  • Compensating for GPS inaccuracy

  • Optimising the number of rebalancing tasks

  • Reducing the parking duration of vehicles that have been rebalanced


Profitability depends on more than having vehicles where people want to start rides. Costs need to be minimised and the return on investment you’ve made in the fleet needs to be maximised.


With Pathfinder’s second-generation rebalancing capabilities, you can relax, knowing that Anadue is working hard to maximise your profits.

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