Reduce idle times of micromobility vehicles. Increase revenue.
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Know demand patterns ahead of time
We provide you with detailed demand predictions to facilitate smart mobility. You’ll know exactly when and where you should be distributing micromobility vehicles to increase the number of rides per day. With Lanterne you get demand predictions:
- through an API, dashboard or emails – tailored to suit your operations
- on an hour-by-hour basis
- down to the street-level
Get the most valuable tasks done
Our smart tasks solution tells you exactly what the highest impact interventions on your fleet are at any given point in time.
We tell you which battery swapping and rebalancing tasks to prioritise in what order, so you can optimise your fleet more efficiently.
Become a pilot partner
Become a pilot partner and get demand predictions and optimisation recommendations tailored to your fleet.
Boost rides by up to 30 %
With Lanterne you get
- 30 % more rides on fleet level and
- more than double the rides per vehicle per day after a rebalancing intervention.
How it works
We use proprietary and open-source data in combination with machine learning to forecast demand patterns with unprecedented accuracy.
Our solution is easy to integrate
The operator provides access to their rides and supply data feed (e. g. a MDS feed).
Lanterne generates demand predictions and actionable fleet optimisation recommendations.
The operator receives predictions and recommendations through our dashboard or an API.
The operator rebalances the fleet to close the demand-supply gap.
Book your call
Book your call to receive a demo of our system and learn more about our services.
Lanterne's Covid-19 response
Lanterne developed Crowdless, a free platform with global coverage that helps people with social distancing. It provides real-time data on how crowded supermarkets are so that people can avoid crowds, choose the least busy place and best time to visit. The platform uses a combination of existing data sources and crowdsourced data in combination with machine learning.
