article by Tom Nutley
Bike Share Schemes are a growing part of European cityscapes. It has become a common way for people to get from A to B in a cost efficient, healthy and environmentally friendly way.
From the rider’s perspective, Bike Share Schemes look simple. You give a credit or debit card to unlock the bike and you ride it to wherever you need to go. At the end of your journey you dock the bike and carry on with your day. For the Bike Share Scheme operator, that seamless experience requires a lot of effort and increasingly relies on data-driven insights.
Every rider wants to access bikes where and whenever they want them and across cities in Europe that can be a challenge to deliver. Operators need to look at how they are maximizing their potential of their data in order to effectively distribute cycles and give riders the best possible experience. How they manage this data and turn it into actionable insights plays a key role in shaping the rider experience and the overall growth of ridership in Bike Share Schemes.
Optimizing Rider Experience with AI
How the schemes are managed depends on how well the data is collected, stored and analysed for operators. Often, the unstandardized and ‘dirty’ data can make it very overwhelming for operators to organize and understand critical intel related to their own Bike Share Schemes. Fortunately, we are at a stage where technology is advanced enough to not only collect, store and present data but also analyse and predict issues and challenges.
Advancements in Artificial Intelligence (AI) and machine learning help to sort and organize vast amounts of information into clean and standardized data, while using real-time intelligence to predict demand and optimize performance for operators. Clean data ensures all the information is accurate and relevant, while the standardized format presents it in a simplified way. With manageable data, operators gain real insights into their users, resources and the market.
In some of the largest and most competitive markets, we are seeing a lot of investment in physical resources such as bikes and docking station but the back-end management systems are simply not prepared enough to manage this effectively, particularly on a large scale. Bike Share Scheme users are still facing the recurring issue of not having access to the bikes when they are needed in these markets.
With actionable data and AI technology, companies can look closer into the underlying problem and analyse trends to predict demand. The two elements work together to help operators gain insights into peak times, popular areas and unused resources. This allows operators to find efficiencies in redistribution rather than being focused purely on supply in an overly crowded market.
With optimized redistribution, operators see significant time and money benefits as they are not unnecessarily purchasing and maintaining large numbers of bikes, docking stations and other resources linked to increased supply. The redistribution effort and the data behind it becomes the key in improving ridership and growing the Bike Share Scheme.
Driving Bike Share Growth Across Europe
We see the benefit of data and AI in Bike Share Schemes in accelerating the growth of cities across Europe but the challenge is how we make use of and interpret these huge amounts of data effectively. We need better solutions to collect, organize and present the overwhelming amount of data into useful insights that can benefit Bike Share scheme operators, users and wider city developments.
The General Bikeshare Feed Specification (GBFS), adopted by the North American Bikeshare Association (NABSA) in 2015 is an example of utilising real-time publicly available data in a standardised format. GBFS enables map and transportation based apps to easily incorporate intelligence into their systems. It would be great to see the growth of GBFS across Europe as it gives access to new data and a platform to build AI and grow Bike Share Schemes.
With huge amounts of data being collected, the focus needs to be on ensuring that it is accurate. As errors occur, the process needs to detect and correct mistakes to make data relevant and ‘clean’. Operators are able to rely on the actionable insights from the clean data in a standardised format to optimise their Bike Share Schemes and improve rider experiences.
The key part of the solution, is the one that AI plays. As human beings are limited in their processing capabilities, we require technology to make use of the vast data that is out there. AI helps to collect and sort this information into data that can be utilised across operations within Bike Share Schemes. Through analysing and predicting mobility needs of users, AI can fully support modern Smart City initiatives throughout Europe and around the world.
As data continues to be the foundation for Bike Share Schemes across Europe, how it is managed and made use of becomes key. Bike Share scheme operators need to combine accurate and clean data with reliable AI technology to collect, analyse and predict user behavior. With AI, operators gain real insights that helps enhance rider experiences and drive the adoption of Bike Share Schemes across Europe.
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