Artificial Intelligence (AI) is changing how we commute and experience urban living. As bike-share schemes grow, AI is playing an increasing role in successful schemes and enabling operators to grow efficiently in new markets.
While some bike-share schemes are struggling to maximise profitability and maintain market share due to increasing headcount and throwing expensive resources into their schemes as they grow, others are using new technologies to accelerate ridership growth and optimise their operations. To succeed in the long-term, they have had to rethink their approach to bike-share scheme management and find ways to use the data in their schemes and turn it into actionable insights.
Many operators have global ambitions and want to expand into new markets but the first step is to ensure they have the best possible operating model and can replicate it in new cities.
Still, they face strong competition, strict regulations and sustained market conditions, which have forced some operators to pull out of global markets or drastically change the way they operate day-to-day.
Mobike, ofo, oBike, Reddy Go and Gobee have all recently shut down operations around the globe. In 2018 alone, ofo has scaled back its deployments in the US and UK and has pulled out of Germany, Australia, India, Israel and a number of cities in Spain.
Bike-share schemes looking to grow in new markets abroad need to fully understand their local operations. Supplying bikes and resources are simply not enough. They need to take advantage of enormous amounts of data in the cityscape to identify where demand will be and accurately serve that demand by using AI technology. AI goes beyond any traditional schedule-based management with a much more dynamic approach.
Accelerating micro-mobility transportation with data and AI
While bike-share schemes are starting to consolidate and pull out of international markets, we are seeing the rapid growth of micro-mobility modes of transportation including e-bikes, e-scooters and other small electric-assisted vehicles.
Its growth is driving Mobility as a Service (MaaS) models globally. Recently, Uber purchased and integrated Jump Bikes, an electric bike-sharing operator, to its app and Lyft acquired Motivate, the largest bike-share operator in the US. It’s transforming the urban landscape with cleaner, sustainable and more accessible modes of transport.
For operators, it also brings a whole new level of operational challenges. It requires them to efficiently maintain resources and ensure each electric vehicle is fully charged for its riders. That requires smarter intelligence and better visibility into the operations.
Micro-mobility operators need to fully understand how its e-bikes and e-scooters are being used. An e-bike or e-scooter that is broken or out of charge negatively impacts rider experience. Operators will struggle to grow in a new market if they are consistently facing poor ridership.
With data from the local city environments and AI technology, operators can track their fleet of e-vehicles, its usage and predicted demand. It enables operators to optimise internal processes to efficiently rebalance, recharge and manage its bikes and scooters.
Whether it’s docked, dockless, bike-share scheme or micro-mobility, growing in new markets requires an enormous amount of internal planning. Operators are faced with demanding local conditions and new operational concerns. They need to optimise their operations and manage their bikes and resources more efficiently. Management is at the heart of a bike-share scheme that caters to the city, its citizens and the operator.
Maximising profitability for capex and opex
While docked and dockless bike-share schemes are very different from one another, data and AI are critical to both. It can be used to proactively predict local demand to optimise redistribution, administration and other operational activities. For operators, AI can be an enabler for growth in new markets.
Traditional docked-based bike-share schemes that tend to be very capex-heavy, require city permission for space, investments for building infrastructure, estimated between £1,500 and £2,000 per bike according to CoMo UK, and opex costs of managing each bike. It can be costly for cities and operators to manage. Cities will need docked operators to better integrate their schemes with the local community and deliver sustainable ridership and rider experience. Data and AI will be at the heart of this.
Meanwhile, dockless schemes are more opex-based. There is no need for operators to build physical infrastructure and instead only need to pay for the number of bikes they put out.
In the past, the ability to deploy quickly and cheaply allowed dockless operators to expand across the globe and drop bikes in cities internationally. Today, cities are implementing stricter rules that limit how quickly operators can enter new markets.
Dockless operators are constantly challenged to use data to track their bikes, understand exactly how it’s being used and efficiently rebalance from all ends of the city. This adds significant management implications to dockless providers. In a free-floating scheme, riders can pick up and drop off a bike virtually anywhere, at any time. It means that in essence, each bike becomes a docking station on its own.
To succeed, dockless operators will need complete visibility into its operations and valuable insights into how the local market behaves. Dockless operators competing with a docked programme in a city will be asked to match the operational efficiency of its counterpart.
Addressing the operational challenges
Docked or dockless, cities need providers to deliver a well-managed programme that caters to demand and better serves the local community. Poorly managed bike-share schemes fail to be sustainable and profitable in the long-term.
Fortunately, operators have a wealth of local city data and access to innovative technologies such as AI that can help optimise their processes.
Data can be used to track all different aspects of the city from how people move to what they like to do. This information can be fed into a bike-share scheme to offer viable transport options to its local residents. But data in itself holds little value. It’s how operators make sense of it and what insights they gain from it that adds value.
AI can be an invaluable tool for operators of all sizes. It takes huge amounts of data to delivers real insights, reducing the need for large teams to analyse and manage this data. AI can use data sets on local city conditions from weather, population, major event or any transport disruptions to accurately predict demand ahead of time. A sunny summer’s weekend in London will have a much different bike-share usage to a raining winter’s weekday in the same region, and operators will need to take that into account.
Data and AI help operators build a big picture of their local market and their existing operations. It can be used to continually track user behaviour and deliver a bike-share scheme that is as dynamic as the city itself. It allows operators to plan ahead while having an operational model that can change with local demand.
The riders get the bikes and resources they want, every time, and can rely on the bike-share scheme for their day-to-day use. For operators, they see a huge increase in ridership and can integrate smarter intelligence into their operational processes to reduce wasted trips and streamline their entire back-end processes.
As cities push towards cleaner, healthier and more sustainable modes of transport, bike-sharing will enter its next phase of growth. One that will be strictly governed by rules and regulations. Optimising internal processes and simplifying management will be a necessity and at the heart of bike-sharing in the future.
Operators will rely on local data and smart AI platforms that can deliver valuable insights. When they can accurately predict demand ahead of time, it makes it simple to manage supply and grow in a local market. Management will be the difference between growing internationally or losing out to smarter competitors.
Globally, bike-share schemes are at a tipping point and operators need to take action to maximise and maintain profitability day-to-day. Operators of all sizes simply can’t afford
to neglect their back-end operational side. With bike-share rides costing very little to rent, operators need to focus on reducing their internal complexities to maximise revenues.
As micro-mobility transportation continues to grow rapidly around the world, cities and operators will be asked tough questions around its management. Operators will be required to find new ways to optimise how they rebalance and maintain their resources in the future with the use of data and AI technology. They will need to optimise operations with a rebalancer that can swap out or recharge batteries for e-bikes and e-scooters.