Improve the users’ experience of bike-share by anticipating their demand.

Who benefits from our products?

Bike-Share operators

They are responsible for ensuring an excellent user experience, meeting the service levels imposed by their contracts and managing their teams and budgets. The use of bikes depends on many external factors. Uses and users are changing faster and faster. It is not easy to integrate them and make the right decision quickly at the right time.

Cities and public transport operators

They want to promote the use of active or soft modes. Unlike buses or the metro, the demand for bike-share is complicated to anticipate because it depends on many external factors (events near a station, weather, etc.). It is not easy to provide reliable information, in advance, on the availability of bikes to users.

Our products

Our 3 products are all based on the same technology. It combines both contextual data to represent the city in which the bike share stations are located and data on the system (station occupancy rates, rebalancing tasks, damaged bicycles). Since 2014, we have developed a proprietary algorithm that allows us to anticipate demand according to the daily context.

We collect data from over 200 bike-share systems more than 200 cities around the world.

The machine learning technology used allows to take into account: the changing context of urban areas (new schools, events, changes in mobility policy, etc.) and obtain reliable information that is updated in near-real time.

BikePredict User

  • Anticipates the availability of bicycles up to 12 hours in advance – compatible with systems with dock and dockless systems.
  • Anticipates the availability of station up to 12 hours in advance
  • Available as an API (API’s Doc)

BikePredict Redistribution

  • Anticipates the optimum number of bicycles to be removed/added for a balanced system
  • Customizable according to your system (sectorization, service level…)
  • Available as an API, dashboard
  • Option: mobile application for each of your operators with a list of tasks updated in real time

BikePredict Placement

  • Models the current activity of your stations
  • Predicts the virtual life (evolution of station filling over one day) of a station that you want to add according to its location and the surrounding context.

They trust us

Their testimonials

“Integration of bike share data into Vianavigo’s itinerary navigator makes it possible to meet the challenges of the Ile-de-France Urban Transport Plan (PDUIF), to encourage cycling for all. Thanks to Qucit’s predictive solution, Vianavigo is able to respond with precision to users’ itinerary research by ensuring the reliability of the service provided.”

Jean-Luc Prat,
In charge of the communication of Multimodal Information
Île de France Mobilités

“Qucit helped Keolis because it was able to analyze the data and propose tools that were useful both for the travellers and for us, internally in order to improve the service.”

See the video of the testimony

Paul Chaperon,
Director of Commercial Marketing and Intermodality
Keolis Bordeaux Metropole

“It is in this approach that the choice of the predictive is inscribed. We must have the most advanced tools possible to meet this political ambition. The customer journey is one of the key elements of a smart city. Qucit helps us to improve it and ensure good intermodality by offering a comfortable service. It’s useful even when traveling in groups of friends, you’re sure to find the right number of bikes at the DiviaVélodi station of your choice!”

Marie-Noëlle Zanin,
Head of Customer Relations & External Communication
Keolis Dijon Mobilités

Interested in BikePredict?

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