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Big Data Innovation in Bike Share Schemes

Big data is changing how we experience cities and enabling us to live healthier, happier and more productive lives. As cities become smarter, big data is being used to reimagine transportation and how we get from A to B.

Every city is producing vast amounts of data every hour and every day. Increasingly this data is being captured and put to work creating new solutions, processes and experiences that improve how a city functions and is enjoyed by citizens.

Data can be used to improve, urban planning, health care, sustainability, transportation and just about every aspect of a city. The “smart” in Smart Cities is about taking this data and rapidly turning it into actionable insights.

According to IBM, a Smart City “makes optimal use of all the interconnected information available today to better understand and control its operations and optimise the use of limited resources”. It makes cities better places to live and enables the best use of what a city’s budgets, space, people and technologies.

By 2021, open and shared data has the potential to add $2.83 billion (10.4 Billion AED) to Dubai’s economy every year, according to a report produced by KPMG. That is a lasting and long-term impact on the city of Dubai and results from using data in a Smart City environment.

While Smart City deployments continue to grow, transportation is an area where we are already seeing the direct impact of data on how citizens live day-to-day. In modern cities, Bike Share Schemes have emerged as a healthy and efficient means of commuting and navigating a city.

These schemes are taking the Smart City concept and applying it to local challenges and succeeding in growing ridership and providing more citizens with healthy and efficient transportation.

It’s this citywide data that is at the heart of the three pillars of smarter public bike sharing system as set out in the Policy Framework for Smart Public-Use Bike Share by the Platform for European Bicycle Sharing & Systems (PEBSS). Data influences how rider priorities are met and how cities offer suitable conditions with sustainable technologies and innovation. Smart Cities support Bike Share Schemes by considering the people, infrastructure and technology elements.

To make data work for Bike Share Scheme operators, it needs to be collected, managed and analysed effectively. This is where Artificial Intelligence (AI) plays a crucial role. AI-based platform manages all available data to deliver valuable insights to operators. The illustration below highlights this.

 

 

 

 

 

 

 

 

 

 

 

 

 

Stage Intelligence’s Usability Data Reveals the Need for Better Bike Share Scheme Management

Insights from schemes in London, Paris, New York and Chicago show that data can be used to deliver optimised rider experiences and grow Bike Share Schemes

LONDON, 8 November 2017 – Stage Intelligence, the leading provider of Bike Share Scheme management solutions, has released its 2016 Q4 data on bicycle and docking station usability and availability across Bike Share Schemes in London, Paris, New York and Chicago. Usability is central to providing an exceptional rider experience and supporting the growth of Bike Share Schemes. It ensures bikes and docking stations are available when and where they are needed most.

The data reveals Chicago and London leading the group with an average usability figure for the quarter of 99.3% and 99.4% respectively. Chicago has been consistent in delivering a reliable Bike Share Scheme to its riders. London is also rated highly but this could indicate over servicing its market, which can create unnecessary costs and limit growth.

New York City ranks the lowest of the major cities with an average usability figure of 90.2% for the quarter. It means that on average 10% of riders at any given time cannot access the bikes or docking stations they want. Paris follows New York City with an average usability of 98.1% with usability dipping to lows of 76.7%. The inconsistency in the scheme means that it may be difficult for riders to depend on the scheme on a daily basis.

“Bike Share riders and cities benefit from Schemes that are easy and reliable to use,” said Tom Nutley, Head of Operations at Stage Intelligence. “Operators need to make sure that riders have access to bikes when and where they need them without over servicing the market. This is where the London scheme could be at most risk. The data is very positive for London but it could be using too much city resources to manage its operations especially when there is no need to.”

Stage’s usability measure compares all docking stations that are within easy walking/cycling distance in a published 500m radius from each other, which can be altered by the BICO platform. The platform takes into account each city’s SLAs when measuring the usability of a Bike Share Scheme. A docking station is usable if there are bikes and docking points available at the station itself or at one or more of its neighbours. The BICO platform can also set custom usability defined by a specific threshold or the SLAs of different cities.

“For Bike Share schemes to be seen as a real, public transport solution and a smart answer to urban mobility, they need to work as good or better than existing public transport services,” said Paul Stratta, Director, at Platform for European Bicycle Sharing & Systems (PEBSS), an initiative from the ECF. “Nowadays people go to bus and railway stations expecting the services to be there, and for it to operate on time. It should be the same with Bike Share schemes. Collecting and analysing data allows Bike Share operators, and their city clients, to get a big picture of operations and understand where bike share can improve and how exactly to do it.”

The neighbourhood approach goes beyond the usual cluster and geographic data collection method which may be a sub-optimal approach – especially in larger schemes. It can identify if Bike Share Schemes are managed well and if it is over or under servicing the cities and its riders. The BICO platform is dynamic to each city and considers each city’s user patterns and prioritisation as well as the SLAs they operate under to measure the usability of Bike Share Schemes and provide valuable data for operators.

“Our BICO platform allows us to take a deep dive into individual Bike Share Schemes in different cities and neighbourhoods around the world and find ways to improve usability within them,” said Toni Kendall-Troughton, CEO at Stage Intelligence. “We were particularly impressed with Chicago’s Bike Share scheme which was performing well without over servicing its neighbourhoods or the market. It was consistent throughout the quarter with high usability figures and over-performed on busy summer weekends to meet the rise in demand.”

 

About Stage Intelligence

Stage Intelligence specialises in developing Artificial Intelligence solutions for the transport and logistics industry. Its flagship solution, the BICO recommendation engine, delivers real-time intelligence for the management of bikeshare schemes. BICO enables precise and optimal decision making and has been purpose-built to remove the complexity from managing resources within a bikeshare scheme. Customers choose Stage Intelligence because our solutions increase their agility, adaptability and enable them to move beyond traditional manual processes. We collaborate with customers to solve complex problems and deliver solutions that have a lasting impact on their operations.

www.stageintelligence.co.uk

 

How to Grow a Smart City Bike Share Scheme

Smart Cities offer an entire ecosystem of valuable and relevant data that Bike Share Scheme operators can use. Smart City data can be used to identify trends and provide actionable insights that can drive the growth of Bike Share Schemes.

These four questions about data hold key information that Bike Share Scheme operators can use to reshape their approach:

  • Who are they?
  • What is happening in the City?
  • Where are they going?
  • What are they saying?

Bike Share Scheme operators need to know not only who their riders are but also the potential of the market. Citywide census and records collect data on population and demographics as well as human behaviour that can be used to predict the future of such schemes for operators. Trends in demographics can be identifiers for areas of growth in specific markets.

Cities also offer the potential to track a range of real-time data from traffic to weather and major events. Understanding how areas are being used at different times of day, by different types of people, and in response to different events through real-time data, can be highly beneficial to operators. A dynamic scheme is the first step in providing mobility options that work for all.

How people move in urban cities is just as important as identifying who they are. Fortunately, cities have a way of capturing this data too. Mobile phones, parking sensors, congestion zones all yield data about how and when people are moving around the city. Transport for London (TFL), a body responsible for the cities transport system, can track passenger movements through the Oyster card. For Bike Share Scheme operators, this data allows them to provide resources that are better attuned to the rider’s needs.

In a more connected and social world, it is also much easier to find out what people are thinking.
As an example, sentiment analysis can be used to track attitudes and opinions on social media. Operators can use this data to see how people react, what they like and dislike as well identify any opportunities for improvement. Ridership is the key to success for Bike Share Schemes and insights on this data can go a long way in ensuring the satisfaction of riders.

The challenge for operators is in how this data is collected and managed. Smart Artificial Intelligence (AI) systems will make use of public data feeds and encrypt user information to ensure the security of data.

For Bike Share Schemes and other transportation networks, it is imperative that they comply with existing and soon-to-be implemented regulations on data collection, privacy and usage such as the General Data Protection Regulation (GDPR). The EU GDPR replaces the Data Protection Directive 95/46/EC and was designed to harmonise data privacy laws across Europe, to protect and empower citizens and to reshape the way organisations approach data privacy.

To find out more about what data is available in Smart Cities, read our full white paper on ‘How to Grow a Smart City Bike Share Scheme’.

Bike Share Schemes Are Starting to Realise the Potential of AI

As Bike Share Schemes around the world become more popular, how we manage the resources such as bikes and docking stations defines the success and growth of such programs.

For Bike Share Schemes to truly be a solution to last mile problems, riders need bikes and docking stations to be available when and where they need them. It is up to the operators to ensure this happens every time.

But many operators fail to provide this basic level of service as they lack the actionable data and operations to manage the schemes effectively.

For a long time, the solution to ridership problems in Bike Share Schemes has been to supply the market with more bikes. In reality this does little to increase efficiency and often adds to the problem.

Now, Bike Share Scheme operators are seeing the value of data and AI in predicting demand and managing supply. Mobike, one of the start-ups in China, is beginning to use AI to manage how its Bike Share Schemes are run.

Mobike’s ‘Magic Cube’, uses data and AI to forecast supply and demand for its bike-rentals. In a fierce competition for market share, Mobike is seeing the value of using AI to simplify scheduling and operations of its scheme.

Mobike has also released its whitepaper outlining what Bike Share Schemes can do with citywide data. The report goes a long way in highlighting the potential for operators in collecting and using data.

The importance of data and AI is clear. For operators, the key is in not only collecting the data but also having a process that works with its systems and resources to drive growth and increase ridership.

In the future, we are going to see more operators turn to data and AI, especially since cities have the potential to collect and store vast amounts of valuable data. With actionable data, operators save money, cities aren’t cluttered with bikes and citizens can rely on a reliable Bike Share Scheme that they can use in their day-to-day lives.

At Stage Intelligence, we have been using Artificial Intelligence (AI) and self-organising algorithms to solve complex problems in Bike Share Schemes from the beginning. Our BICO solution is easily incorporated into existing platforms to simplify logistics and increase ridership.

To find out more about how Stage Intelligence can drive growth within your Bike Share Scheme, please contact: tom.nutley@stageintelligence.co.uk