Since the first coin operated Bike Share Scheme was introduced in Copenhagen in 1995, the health and environmental benefits of Bike Sharing have been widely recognised. If people choose to ride a bike, they aren’t driving a car, riding in a taxi or using mass transit systems. They’re exercising and reducing congestion across a city.
That’s all true but we’re seeing that Artificial Intelligence (AI) is able to maximise the positive environmental impact of Bike Share Schemes and make them even more sustainable. Bike Share Schemes that implement an AI platform can push their schemes towards carbon neutrality and demonstrate that they are taking climate change seriously.
We have seen it in the schemes that our BICO AI optimisation platform is deployed in. The data shows that AI can directly impact how a Bike Share functions within a city and optimise its operations, reducing CO2 emissions.
The biggest impact AI can have is simply getting more people to use a scheme. As mentioned, that means they aren’t using another mode of transport and a city and its citizens are healthier. BICO ensures that users can get the bikes they want where they want them and docks are available at the end of their journey. That makes it convenient to use a Bike Share Scheme and incorporate it into their daily routine.
We’ve seen ridership growth across these schemes after they’ve deployed BICO:
- 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 Rides Per Day
These numbers are per bike, per day. That’s a huge jump in places like Helsinki, which has a big impact on the benefit the scheme is delivering. This is also combined with more efficient redistribution of bikes across a scheme that translates into a reduced carbon footprint for the schemes operations.
Our platform enables the redistribution of bikes to be optimised so that fewer trucks take fewer trips to achieve the best possible results for users. Over the last year, Bike Share Schemes that use BICO have reduced the amount of time redistribution trucks spend on the road while also cutting the distance they travel by 10,000 miles. That means less CO2 generated by the trucks and a lower carbon footprint for the scheme.
BICO is also responsible for 100,000 less bikes needing to be moved, due to the precise and optimal decision making of AI. By streamlining the processes of multiple Bike Share Schemes, we have seen the reduction of up to 10 metric tonnes of CO2 emissions as a direct result of less redistribution trucks being on the road. This has enabled these Bike Share Schemes to take one step closer to their goal of delivering a carbon neutral operation.
It all adds up to a clean mode of transport being even cleaner with the use of AI.
Let us know if you’d like to learn more about how we support Bike Share Schemes with AI: firstname.lastname@example.org