Stage Intelligence, a leading artificial intelligence (AI) platform provider, has released new data showing how AI can contribute to the growth, efficiency and sustainability of bicycle share schemes.
Stage Intelligence, which uses AI and self-organising algorithms to solve complex problems within the shared mobility ecosystem, oversees the BICO bike share AI programmes currently running in Chicago, Guadalajara and Helsinki. BICO uses AI optimisation to predict demand for cycles, ensuring that they can be redistributed in areas where they are most needed.
Since deploying Stage Intelligence’s AI systems, which use real time data based on such diverse factors as weather conditions, local events, public holidays and public transport closures to anticipate demand, all three of the cities surveyed showed a notable increase in bike share uptake. Chicago’s Divvy Bikes saw ridership growth of 0.75 rides per day; while MIBICI in Guadalajara and City Bikes in Helsinki both saw growth of five rides per day.
Tom Nutley, CEO of Stage Intelligence, said: “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. 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.”
By enabling users to collect, manage and visualise data in order to infer deeper insights into consumer behaviour, BICO provides its users with the capability to automate processes and better meet consumers’ needs. Over the last 12 months, Stage Intelligence’s figures show, BICO has reduced the distance driven by cycle redistribution trucks by 10,000 miles, moving 100,000 fewer bikes and reducing the CO2 emissions produced by redistribution trucks by around 10 metric tonnes.