Optimo Technologies anticipates free spaces and available bikes in V'Lille stations

Changed on 05/05/2023
Supported by Inria Startup Studio, Optimo Technologies relies on the expertise of the Inria centre at the University of Lille in the field of optimisation. Optimo Technologies has developed a solution for predicting the number of available V'Lille bikes and the number of free spaces in each station of the network.
Optimo solution V'lille

Prediction for urban mobility

Capture écran Optimo - disponibilité V'Lille

Cycling for 20 minutes and having the unpleasant surprise of finding the destination station full on arrival, when it was empty 20 minutes earlier... V'Lille users experience this regularly.

Optimo Technologies relies on a hundred or so parameters to learn about the mobility behaviour of shared bikes, in order to accurately predict which spaces or bikes will be available at the V'Lille station within a given time frame, in 10-minute increments up to 1 hour.

This prediction, accessible via a dynamic cartography of the V'Lille stations of the European Metropolis of Lille (MEL), is calculated according to the context of each station.

In addition to the usage history provided by the MEL, the prediction model takes into account the geographical position (municipality, GPS coordinates, central or peripheral position), the meteorology (temperatures, precipitation, wind), current maintenance, etc.





Optimo Technologies

The objective of Optimo Technologies is to direct daily business trips towards more environmentally friendly transport. Optimo Technologies' ambition is to offer companies a "mobility assistant" that contains both a personalised and optimised route calculator and a wallet paid for by the employer to pay for all journeys. The end result is a reduction in the carbon footprint associated with these home-to-work journeys.

Geoffrey Pruvost, a doctor from the Bonus project team, co-founded the startup with Laurent Decool. The Bonus project-team is an expert in large-scale optimisation and large-scale calculation. The scientific programme of the Bonus project-team is the subject of numerous collaborations and lies at the interface of three research axes: decomposition-based optimisation, optimisation assisted by statistical learning and ultra-scale optimisation.

Geoffrey Pruvost, co-founder of Optimo Technologies and i-PhD 2022 winner, is planning other applications based on this same know-how:

The next step for Optimo Technologies will be to predict availability and incidents in other modes of transport such as bus, metro and train.

Eventually, the startup's ambition is to offer a solution capable of making journeys combining all these means of transport more reliable, with the aim of predicting the best mode of transport and journey to work.

More information

The Optimo Technologies solution is available on V'Lille Prevision.


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