BikePredict is a mobile application that makes self-service bike riding easy by giving information on the number of available bikes and docks in real time and in the near future.
Our predictions are based on statistical analysis of four years of Velib data (docking stations check-in/check-out history and bike trips history, history of re-balancing docking stations), in relationship with time (hour, day), weather, etc.
Our ‘BikePredict’ API forecasts the number of bikes and slots that will be available in the short term at each station. It connects to multiple static and live data streams to provide the predictions using our statistical models.
Pass the predictions on to your users using your mobile app and enable them to make better-informed decisions. Or pass them on to your load balancing field teams so they can anticipate coming trends.
Our solution is scalable and deploys quickly on any bikeshare network, no matter how large or complex. It embeds our latest research so you always benefit from the best technology.
‘BikePredict API’ is already used by Keolis which operates the VCub bikeshare system in Bordeaux. They feature our predictive API in their own mobile app "La Bonne Station".
A balancing assistant for bikeshare systems operators
With their connected devices, your teams get access to optimal recommendations provided by our Artificial Intelligence. It adjusts the results in real time, taking into account recent interventions and current network usage. By anticipating demand, load balancing can be improved.
Our statistical algorithms allow the early detection and qualification of anomalies (e.g. flat tyre or malfunctioning brakes). The sooner they are noticed, the sooner maintenance teams can spend their time on high-value tasks.
No costly installation, no maintenance from your part: our systems overlays on yours. You do not need special hardware or software, and you always have access to our latest innovations.
Improve your load-balancing effectiveness and shorten your repair lead time
ComfortPredict is an Index assessing the comfort of pedestrians in public spaces, and a mobile application that gives citizens an opportunity to take part in the design of their cities.
In order to measure the impact of public spaces re-design, and allow for a more iterative and inclusive civil engineering work, Qucit has developped a big data index measuring how pedestrians perceive their environment. Developped under the Datacity project sponsored by the City of Paris and Cisco, in partnership with Numa, the tool has morphed into a mobile application that people can use to have a say in the design of their cities.
Initially targetted at the Place de la Nation in Paris, the tool can be made available in all places where data is available. Its goal is to help design the most pleasant public spaces and facilitate their re-appropriation by pedestrians.
Want to actively design your city? Join the thousands who have already provided feedback and answer the survey now!