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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

 

Artificial Intelligence for Modern Transport Operators

With an AI-based management platform, transport operators benefit from utilising a variety of data sources. For Bike Share Schemes, the platform can give insights as to where bikes are required and instantly inform distribution trucks about where bikes need to be picked up and dropped off. When information is being processed instantly and communicated to drivers, there is no lag between new demand emerging and that demand being served.

The value of AI is its ability to process vasts amount of data across a Smart City and make it useful for operators. Citizens get the resources they need and that supports the long-term sustainable growth of public transport.

As a form of modern transport, AI platforms simplify the management of Bike Share Schemes and deliver unique benefits to operators:

 

User Satisfaction

Increased user satisfaction by ensuring bikes and docking points are available when and where required

 

Cost Reductions

Improved operational efficiency and reduced requirement of operational resources

 

Remove Unnecessary Processes

Move away from traditional schedule or dispatch-based approaches and eliminate wasted journeys

 

New Visibility

Real-time truck locations, colour coded station status and station clustering as well as access to advanced analytics and actionable reports via a single dashboard

 

Increased Autonomy

Drivers receive direct communications often via a mobile app, allowing them to work independently of each other and the back office with less wasted time

 

Greater Control

Autonomous operation of a Bike Share Scheme that reflects real time conditions, offers consistent delivery instruction and a detailed overview of the scheme

 

Scenario Simulation

The simulation engine in such management platforms offers the ability to see responses to “what if” scenarios, allowing improved and more efficient resource planning

 

Scale Up

Increase the size of a Bike Share Scheme without the need to simultaneously increase available resource to maintain operation levels

 

The demand for public transport is growing with more citizens turning to Bike Share Schemes as a viable mode of transport. In a growing and competitive Bike Share market, AI could be the key to success for many operators. It has already proven its value to some of the largest schemes in the world and will continue to be at the heart of modern transportation in the future.

 

To find out more about the advantages of utilising AI in transportation read our full whitepaper on ‘How to Grow a Smart City Bike Share Scheme’.

Solving Distribution Challenges in Bike Share Schemes

Effective distribution, in some of the best Bike Share Schemes, require immense amounts of citywide data to be captured, processed and used. Increasingly, schemes around the world are using city data to not only optimise its redistribution but to also show complete visibility to its users as to where the bikes are on its system map.

It’s how Bike Share Schemes use this data that drives value for operators, riders and cities. Bike Share Scheme operators are often familiar with rider statistics and patterns but the challenge is to use this data to accelerate growth within a scheme.

Tracking growth and stimulating growth are often two very different things. At the heart of new growth is rider experience. Bike Share Schemes are challenged to offer a consistent rider experience across a city while ensuring that using a Bike Share Scheme is easy, convenient and enjoyable for the rider. A positive and consistent Bike Share Scheme begins and ends with two questions:

 

  1. “Can I get a bike where I want one?”
  2. “Can I dock my bike at the end of my journey?”

 

If a Bike Share Scheme can guarantee these two things, it is likely that a rider will have a positive riding experience. When a rider can borrow a bike and dock it, they are more likely to use the scheme again and make it part of their routine.

That’s good for the Bike Share Scheme as it will help to grow overall ridership and new people will experience the city using shared bikes. A Bike Share Scheme with an active and growing ridership is able to invest and expand its schemes.

The data available in a city can be used to ensure that riders can access bikes and docks where and when they want them. Different days of the week, weather, events, seasons, local conditions and scenarios, and a whole range of criteria can shape how a Bike Share Scheme is used.

On a rare rainy day in Los Angeles, people may not cycle at all. In Amsterdam, there may only be a slight variance in usage patterns. At the same time, different events can be connected like a sunny day in a city, matched with a train drivers strike and major sporting event being held in one area of the city. All of these factors can influence how a scheme is functioning and where more or less bikes are needed.

Artificial Intelligence (AI) can be an excellent tool for simplifying Bike Share Scheme operations while using the power of data to drive decision making. AI can process a variety of data both historically and in real-time
to deliver actionable insights for Bike Share Scheme operators. Operators gain visibility into all of the criteria shaping a cityscape and benefit from useful insights to optimise bike distribution to match changing conditions.

AI accelerates how decisions are made by operators while taking the guess work out of bike distribution. The AI technology can predict peak times up to 12 hours in advance, enabling operators to manage supply and meet requirements in those areas. This ultimately leads to bikes and docks being available and riders getting a better Bike Share experience.

 

To find out more about the role data and AI has on a Bike Share Scheme, read our full whitepaper on ‘How to Grow a Smart City Bike Share Scheme’

 

7 Benefits of an AI-Optimised Bike Share Scheme for Smart Cities

Smart Cities that have active and growing Bike Share Schemes create urban environments that are healthier, with less congestion and better placed to manage growing populations.

In 2016, 1.7 billion people or 23% of the world’s population lived in a city with at least 1 million inhabitants, according to the United Nations. By 2030, that will grow to 27%. Urbanisation is continuing to grow and that puts strain on transportation networks.

Public Transport in its current state is already stretched and cities are often challenged to fund new projects. With optimised Bike Share Schemes, cities can encourage citizens to cycle and avoid crowded transport systems.

As more Smart City initiatives are deployed, cities become data-rich environments that can benefit Bike Share Schemes. The emergence of the Internet of Things (IoT) and a growing number of connected devices deployed across a city will only expand the potential of Artificial Intelligence (AI) in Bike Share Schemes and transportation overall.

Expanding data sets managed with AI can deliver results that directly benefit riders and influence how a city functions and grows.

All cities can benefit from an AI-driven Bike Share Scheme but as smart technologies are rolled out widely, the depth of data will grow. Operators benefit from new and increasingly precise insights while riders will see Bike Share Schemes optimised in new ways.

With AI, operators can ensure a well-run Bike Share Scheme that offers:

 

A Cleaner Transport Option:

For cities to help tackle climate change and deliver a better environment for citizens to live in

 

Healthier and Happier Riders:

Through daily exercise

 

Effective First & Last Mile Solution:

Since it can be significantly cheaper and faster than other public transport options for short distances

 

Reduced Strain on Infrastructure:

As less people are using public transport that requires continuous upkeep and maintenance

 

More Investment in Cities:

With less need for maintenance and new projects, Smart Cities can use funding on other much needed transport infrastructure such as cycling lanes and incentives

 

Manage Rising Transport Demand:

With increasing urban-dwellers, cities can offer more transport options with a Bike Share Scheme to accommodate this rise

 

City’s Brand Image:

Can be shaped by a cycling culture, supporting tourism and other thriving economic industries

 

Bike Share Schemes are like no other modes of transport. It offers a viable transportation option to many crowded cities that deliver a range of benefits to both cities and its citizens.

To find out more about the benefits of Bike Share Schemes to operators, cities and citizens, read our full white paper on ‘How to Grow a Smart City Bike Share Scheme’.

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’.

How Cities Can Support Bike Share Schemes

Cities and its citizens stand to gain a lot from the success of Bike Share Schemes. They provide a clean and healthy transportation option in increasingly congested urban areas. Cities now play a huge role in attracting new schemes and supporting the adoption with cyclists.

 

In an effort to grow and capture further market share, Bike Share Schemes are always looking to enter new markets. Chinese start-ups are a prime example of this with many expanding to nearby countries such as Singapore and even as far as the UK.

 

Operators are now looking at more than just market size. They need to be sure that cities can fully support the growth of their schemes with proper infrastructure, capital and in changing consumer perception if necessary.

 

We highlight what cities and city planners can do to help attract Bike Share Schemes and support the adoption and growth with its citizens:

 

  • Provide safe cycling infrastructure:

It is important that citizens have access to safe cycling infrastructure. By promoting safe cycling, more riders are likely to see Bike Share Schemes and cycling in general as a viable solution.

 

Suitable cycling lanes and places for docking stations will be critical to the adoption and growth of the schemes.

 

  • Promote a cycling culture

The citizens are one of the biggest assets for cities. By changing perceptions and encouraging people to cycle, Bike Share Scheme operators will see more value and potential in entering the market.

 

Aside from getting operators into the market, a cycling culture will also greatly benefit the city. As more people cycle, the image of cities itself can be reshaped while seeing environmental and cost benefits.

 

  • Work with existing transportation hubs 

A huge amount of Bike Share Scheme riders see it as a last-mile solution. Such schemes often help them get to their final destinations quicker, easier and more cost efficiently.

 

Through working with existing hubs by strategically placing bikes and docking stations, operators will have access to a large portion of the market. Cities can also offer its citizens an integrated transportation option.

 

  • Partner with operators

By working with operators, cities can ensure that Bike Share Schemes are set up to meet both the needs of the cities and citizens as well as the operators themselves. Showing a willing partnership is going to be more encouraging to new operators looking to enter and grow in a specific market.

 

Cities also have immense potential in capturing data. Operators need to make use of the data available in cities to increase ridership. Data within cities can be used to identify key areas that will be crucial to new schemes and is also highly helpful in predicting demand.

 

At Stage Intelligence, we use real-time data available in cities and Artificial Intelligence technology to simplify Bike Share Scheme logistics. By understanding the data, Bike Share Schemes can remove complexity and give bikes to riders when and where they want them.

 

To find out more about how Stage Intelligence can manage operations and increase ridership within your Bike Share Scheme, please contact

tom.nutley@stageintelligence.co.uk

 

AI Technology and Big Data are Building Smarter Cities

As technology continues to advance, cities don’t want to be left behind. Cities around the world are turning to Artificial Intelligence (AI) to facilitate and support the drive towards ‘Smart Cities’.

Organisations are realising the potential of cities in collecting and using valuable data to benefit its citizens. Big Data and AI are helping to drive new innovations and disruptions, especially within the transport sector.

We give a round-up of key advancements in AI technology with city environments. These articles show a future where AI could be at the heart of how we get from A to B.

 

  • AI Traffic Lights

AI Traffic lights, set to be implemented in Milton Keynes, UK for 2018, will aim to offer a more reactive solution to managing rush hour. Traffic lights at present run in sequence. The AI fitted lights will cover a 50 square mile area around busy zones to monitor traffic and lower congestion, making it safer for cyclists, buses and other vehicles.

The AI traffic lights are the first step to improve traffic by integrating with existing road signs and management systems. In the future, the traffic lights will be able to communicate with driverless cars to ensure they work effectively.

 

  • NVIDIA’s AI Security Camera

According to NVIDIA, there are hundreds of millions of surveillance cameras around the globe, expected to reach approximately 1 billion by 2020. The amount of data that this creates is difficult to manage by human beings alone.

This is where AI technology can play a huge part. AI can be used to analyse vast amounts of data and accurately drive insights. Connected to the Cloud, AI-powered systems can track and monitor behaviour as well be a solution to a lot of city problems.

 

  • Connecting the Car Industry with AI

 AI has transformed the transportation industry with new business models and ways of operating. The car industry is not any different. AI has bought new features and increased connectivity options while promising a future of driverless cars.

The advancements in AI are making vehicles safer, smarter and cost efficient for people. From major automotive brands to start-ups, AI is proving its value in improving operations and bringing innovations.

 

  • Launching a ‘Pop-up’ Bus

Citymapper’s CMX1, dubbed the ‘pop-up bus’, aims to offer bus routes that change dynamically according to traffic and demand. It analyses vast amounts of data to find where demand is and offer a bus route to meet that demand.

Public transport has remained unchanged over the years. Citymapper’s pop-up bus is a good example of how optimising your resources can find new efficiencies and benefits to operators, even in old traditional models.

 

At Stage, we use Artificial Intelligence to remove complexity in Bike Share Schemes. Through using real-time data, we can predict demand and manage supply to increase ridership and grow Bike Share schemes.

To find out more about how Stage Intelligence’s AI solution can simplify logistics within your Bike Share Scheme, please contact tom.nutley@stageintelligence.co.uk