BORDEAUX, France, April 25, 2019 /PRNewswire/ --
Challenge: Improving bike-sharing operational efficiency
Cykleo operates the bike-sharing scheme V3 in Bordeaux Metropolis: 172 stations spread over 579 km2. The scheme needs Cykleo agents to rebalance stations so that users are always able to find a bike or dock when needed. To improve its rebalancing strategy, Cykleo chose to gain operational efficiency.
Solution: Artificial Intelligence
Today, Cykleo works with a unique predictive tool to manage its field operations: BikePredict Redistribution. Thanks to artificial intelligence algorithms, it provides a real-time picture of regulatory actions necessary at each station based on predicted risks.
Agents optimize their route by obtaining a choice of recommended tasks according to these priorities and their working zones.
Cykleo gets entirely digitized tools:
- Web interface with a list of tasks and priorities, a real-time view of the network on the map, a list of operations currently conducted and an analysis of the data to monitor operating indicators (availability of stations, number of bikes moved, working time)
- Mobile application for the field operators
"BikePredict Redistribution allows us to simplify the briefing at the start of the tour with the general overview. I can prioritize tasks. Then, my agents follow the mobile application that offers them a choice to act in an effective way. It is therefore an important management tool in our daily life. The other major use is training. New agents are delighted to have in their hands a product that allows them to quickly acquire in-field knowledge."
- Nicolas Meillan, South-West Operations Manager, Cykleo
Results:
- 21 % less moved bicycles
User-demand predictions with artificial intelligence enable agents to increase their movements efficiency and avoid, for example, unnecessary removal of bicycles at a station. It is an excellent digital way to optimize operating costs.
- 17% of improved availability
Overall, the bike share system is improved as there are less full or empty stations.
- Adaptation to changing in user behaviour
Users' behaviours are constantly changing. The artificial intelligence model developed by Qucit is based on machine learning, regularly recalibrated. This simple and efficient tool adapts constantly to the evolution of demand - including the arrival of e-bikes in May.
"We see a significant decrease in the number of moved bicycles. This progress in handling reduces professional risks. Moreover, this is not at the expense of availability rates."
- Nicolas Meillan
(Logo: https://mma.prnewswire.com/media/877297/Qucit_Logo.jpg)
Contact details
contact@qucit.com
