AI increases Bikeshare scheme usage in three major cities

UK based software company, Stage Intelligence has released data which shows the how the use of AI affects rider usage in Bike Share Schemes. In a seeming win-win scenario, rider usage is increased, while bike re-deployment (by van) is reduced. Meaning a double hit for carbon reduction from the schemes.

One of the largest and most costly challenges in Bike Share Schemes is the redistribution of bikes across a city. Their BICO AI optimization platform enables Bike Share Schemes to ensure bikes are available where they are needed, and docks are free when a rider ends their journey.

Using the Stage Intelligence’s BICO AI Platform resulted in more rides per day: 

·      Divvy Bikes, Chicago: Ridership increased by 0.75 Rides Per Day

·      MIBICI, Guadalajara: Ridership increased by 1.5 Rides Per Day

 ·      City Bikes, Helsinki: Ridership increased by 5.5 Rides Per Day

 “When you see ridership growth increase, it means more riders are enjoying a reliable scheme and making it part of their daily routine while operators are increasing profitability. Each ride per day adds to a scheme’s bottom line and enables it to grow and scale effectively/efficiently,” said newly appointed Tom Nutley, CEO at Stage Intelligence. “We are seeing AI create more liveable cities with sustainable transportation solutions, reducing traffic and improving air quality. That will have a lasting impact on how we experience urban environments.”    

Over the last 12 months, the BICO AI optimization platform was able to reduce the number of miles driven by redistribution trucks by 10,000 miles with 100,000 less bikes being moved. With less redistribution trucks on the road, CO2 emissions caused by redistribution trucks were reduced by 10 metric tonnes, enabling schemes to reduce their carbon footprint while offering green transportation options moving one step closer to carbon neutral operations.   

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