How AI Directly Influences The Bike-Sharing Program In Smart Cities?
Bike-Sharing system or bicycle-sharing system has been around since 1965 when a group called Provo introduced it in the bicycle-loving Amsterdam. But the idea of bike-sharing created a buzz only after the entry of smart city concept. It began to be recognised as an effective tool to reduce air pollution, traffic congestion, travel costs, and fossil fuel-dependency while improving public health. Besides, making cities look much more vibrant, cool and cosmopolitan.
However, launching a bike-sharing system in cities and over time making it an appealing mode of transportation is no easy feat. It requires a significant private and public investment and alterations to the built environment while understanding the needs of citizens. Many bike-sharing programs have been initiated amidst much hype yet their popularity has soon declined. They have ended up being used mainly on weekends and for recreation purpose.
There are a number of factors that lead to the failure of a bike-sharing system. But according to reports, the most impactful factors that discourage people from opting for it are dockless systems or systems that do not have adequate docks in a city. Dockless systems increase the risk of theft and vandalism while insufficient docking points make it difficult for riders to park the bike at a given dock and hence reducing the proper redistribution of bikes. If this single issue is resolved, bike-sharing programs can easily meet success and become an irresistible transport option in smart cities.
Stage Intelligence, a leading Artificial Intelligence (AI) platform provider for mobility and logistics has introduced an AI-based program called ‘BICO AI Platform’ to meet some of the challenges related to bike-sharing in smart cities. The company is a global player in developing, training and deploying AI technology in order to optimise the management and operations of bike-share programs. It has deployed its expertise in some of the major smart cities around the world including Paris, Helsinki, Chicago, and Guadalajara, Mexico.
Until now, AI has been a solution limited to the big market players, requiring a substantial amount of investment for development and deployment. However, the BICO AI platform is aimed at improving the ridership in small-to-medium bike-sharing programs.
As per Stage Intelligence, the operators with up to 2000 bikes can benefit from the AI technology with self-organising algorithm capabilities and real-time data. It helps them simplify the management of resources within the bike-sharing scheme, increase ridership and enhance rider experience.
Furthermore, deployment of BICO AI platform stimulates decision making. It helps provide information on weather conditions, seasonality, city event and other multiple factors that help operators meet the wavering demands before time. More specifically, it helps bike-sharing program operators to adopt an intelligent approach in redistributing the bike across the city – when and where there are needed. This way, they are moving ahead with an innovative approach against the dispatch-based one, significantly reducing the wasted journeys.
Today, the BICO AI platform is proactively managing one of the biggest challenges in bike-sharing – docking stations and the availability of bikes.
After deploying its BICO AI platform in a number of smart cities, Stage Intelligence published data showing how AI directly influences the bike-sharing program growth, sustainability, and efficiency. It reveals that City Bikes in Helsinki, Divvy Bikes in Chicago and MIBICI program in Guadalajara, Mexico have been able to deliver positive results for riders, program operators and the cities.
