Have you ever wondered what would happen if bikes could think for themselves? Well, this week MIT researchers have a paper exploring the possible effects of an autonomous bicycle fleet on urban mobility.
The MIT Autonomous Bicycle – a multi-year project – is exploring bike sharing on demand. The idea is that users request a bike through an app and autonomously ride it to their location, where they can ride it normally.
Once the ride is complete, it will return to autonomous mode to pick up the next user or drive to a charging station.
Wait, how does an autonomous bike work?
The researchers have designed a wheel mechanism that offers two different configurations.
When in use, the bike mode ensures that the experience is the same as riding a regular two-wheeler. It then transforms into a tricycle for autonomous driving that has the necessary stability to drive independently.
Solving industry pain points
Autonomous bicycles could solve the industrial problem of fleet rebalancing, with bicycles concentrating at certain times in some parts of the city, while other areas remain bicycle-less.
Autonomous bicycles would also remove the need for operators to redistribute bicycles in vans or trucks, a practice that is not good for costs or the environment.
The researchers also believe that with automation, fleets could be smaller to meet the same demand.
It found that an autonomous group of bicycles could be three and a half times smaller than a station-based system and eight times smaller than a system without a dock.
The standalone model can provide overall improved performance and user experience even without rebalancing. The increased efficiency would cover the additional costs of the technology.
However, the disadvantage of automation in micro-mobility fleets is that it can take away some of the spontaneity. It could work well for those who initially locate an escooter through an app. But will spontaneous riders be willing to wait for their bike to come to them?
I think there is a more substantial use case in escooters.
The case for autonomous e-scooters
In many cities, micromobility operators are: develop technology to stop illegal parking. For example, some places in Germany want to limit e-scooters to: parking lots to keep them from blocking the sidewalk.
Automation can allow e-scooters to move from passenger to passenger. In addition, vehicles could drive autonomously to charging points. Imagine an e-scooter (or e-bike) that can diagnose a problem and remove itself for repair?
The simulation framework developed by MIT is admittedly a work in progress. But it can be a great starting point for urban mobility planning in general.
It currently contains variables such as geospatial data, user behavior, cycling functions, charging and rebalancing strategies, but is highly configurable.
Urban mobility is undergoing a radical transformation, balancing a growing population, preparing to roll out new modes of transport, such as hyperloops and eVTOLS. The more tools needed to effectively anticipate these changes, the more accurate the planning can be. This helps to make cities liveable, equitable, sustainable and resilient.