Creating Better Jobs with Artificial Intelligence

Artificial Intelligence (AI) is disrupting all kinds of industries and changing them for the better. While most discussions around AI are positive, there are still fears around AI’s impact on the job market.

In many cases, AI won’t replace workers but it will make them better at their jobs. It will enable workers to refocus their jobs around areas where a human touch can add value. The impact of AI is already being felt and we see it as not about phasing people out but finding smarter ways to work.

AI is enabling many types of jobs to become easier and more efficient. It permits extremely large quantities of data to be made accessible and useful for people to make faster and more precise decisions. While this can be seen as outsmarting human beings, it is actually enabling workers to manage and use more data with better results.

This means workers will be able to proactively manage situations based on extremely complex data patterns while businesses will be able to make larger and more targeted investments. It creates a new business environment that is different but isn’t simply about removing people from the workforce.

At the same time, AI is driving demand for new skills. We must be prepared to “up-skill and re-skill” according to the UK parliament.

At Stage Intelligence, we agree that education and training systems across the world should be developed and made more flexible. They should teach students about AI and the skills they need to accommodate to a life working with it. AI is not going away and as it becomes ever present within day-to-day life, specifically within the jobs market, it is important that we can all find a way to maximise the potential of AI in our businesses.

We believe that AI should be embraced as a technology that helps facilitate jobs and enhances our lives. At Stage Intelligence we use AI to create intelligent solutions to solve complex problems within logistics in Bike Share Schemes. Our AI is helping Bike Share Scheme operators to increase usability and ridership allowing its staff to focus on other core areas of the business. By embracing AI both individuals and technology can work together to make a better Bike Share Scheme.

It is important that we all find a way to embrace and work with AI. It should not be seen as a threat but rather as an opportunity. AI can improve the jobs market and evolve the ways of working to create greater efficiency, enhancing the lives of everyone.

What are your thoughts on the future role of AI within jobs? Share your ideas in a comment below.

Putting Cycling at the Centre of Active Transport in London

London is putting cycling front and centre of its campaign to deliver smarter, greener and more efficient transport.

In August, Mayor of London Sadiq Khan shared his vision for the transforming transport in London. He wants the capital city to be a place of ‘active transport’ and is working to make the city greener, smarter and easier to navigate.

Following this announcement, Transport for London (TfL) awarded a new £79.7 million Cycle Hire contract to Bike Share Scheme operator Serco, which includes the development of lighter, more comfortable and more maneuverable bikes. Serco will continue to maintain and distribute the fleet of Santander Cycles until 2022.

Cycling has already taken a major role in commuter life in London. There is a fleet of 11,500 Santander Cycles in London and TfL says that there are over 610,000 bike trips a day made in the city, well over double the figure of the early 2000s.

In 2000, central London’s roads during the morning rush hour were populated by seven times as many cars as bikes. This ratio has now dropped to nearly two to one, leading TfL to predict that bikes will outnumber cars within a few years.

Khan recognises the potential and popularity of cycling within London, and has created a campaign for enhancing the cycling experience in London. He has agreed to increase the TfL budget spend on cycling and has given the go ahead on continuing the build of the cycle super highway. He wants London to be a leader in supporting cycling and show the rest of the world that London understands how to deliver active and green transport.

Khan is dedicated to supporting cycling across London and is working on a number of ways to make this happen. One of the best ways to support cycling in London is to focus on driving usage in its Bike Share Scheme. More users mean less cars on the road, fewer traffic jams, lower emissions and greatly improves public health through physical activity.

London can grow its Bike Share Schemes by focusing on making it even more user friendly. The happier riders are the more uptake Bike Share Schemes will incur, and as a result the more active London will become.

At Stage Intelligence we use Artificial Intelligence to simplify the distribution of bicycles in Bike Share Schemes. When Bike Share Schemes are properly distributed and made easy-to-use, customer satisfaction increases. Bike Share Scheme optimisation can effectively help transform the London transport experience. We think that Khan’s support in bettering cycling in London is a great place to begin in bringing his vision to life.

To find out more about Stage Intelligence’s technology and how it can assist in making the distribution in Bike Share Schemes as efficient as possible please visit our website:

Stage Intelligence adds support for the GBFS Open Data Standard into its BICO Distribution Solution


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BICO uses Artificial Intelligence to enable Bike Share Schemes around the globe to maximise their potential and deliver an optimal user experience.   

LONDON, 7 September 2016 – Stage Intelligence, the leading provider of Bike Share distribution solutions, has added support for the General Bikeshare Feed Specification (GBFS) open data standard into its BICO Distribution Solution. The GBFS integration enables BICO to access published data feeds from all participating bike share schemes. Bike share schemes that use GBFS are able to rapidly deploy BICO and use Artificial Intelligence to not only automate, but also optimise, real-time bike distribution decisions with end-to-end workflow management to Field Operatives.

BICO has been designed to simplify how scheme operators manage day-to-day bike distribution in a consistent and scalable way, allowing schemes to grow and adapt as demand patterns change. BICO can evaluate the enormous amount of possible distribution options available in its decision-making, enabling operators to leverage new levels of real-time intelligence improving end-user satisfaction.

“The new GBFS support accelerates access to real-time intelligence for participating bike share schemes. Schemes that use BICO see more than a 15% reduction in operational cost while ensuring that bike share users can have access to the bikes or docks they need,” said Toni Kendall-Troughton, CEO at Stage Intelligence. “We are the only provider of Artificial Intelligence-based solutions for Bike Share schemes with live deployments in major cities. The new BICO GBFS support will advance our growth around the world.”

The GBFS open data standard was adopted by the North American Bikeshare Association (NABSA) in November 2015. It makes real-time data feeds publicly available online in a uniform format so that map and transportation based apps can easily incorporate this data into their platforms.

“We are in a pivotal moment for bike share – one where technological advancements make bike share a more accessible form of transportation and easier to use than ever before.” said President of NABSA, Nicole Freedman. “The bike share industry has the opportunity to more seamlessly integrate into other forms of public transportation and trip-planning apps with the data GBFS offers.”

BICO uses real-time intelligence to understand and predict demand while communicating where bicycles need to be redistributed to drivers via a mobile App. It removes the need for manual processes and decision making, eradicating the need for operators to try and predict the constantly changing demand across the scheme. This means better service for customers, greater efficiency in operations, and the ability to scale and grow bike share schemes in a consistent way.

“Data drives innovation. The GBFS integration gives access to data around new scenarios and behavioural patterns within Bike Share schemes. That helps us to develop new solutions and gives our machine algorithms vital data for optimising bike share schemes in participating cities but also around the world,” said Francois McDonald, Solutions Director at Stage Intelligence.

About Stage Intelligence

Stage Intelligence specialises in developing Artificial Intelligence solutions for the transport and logistics industry. Its flagship solution, BICO, delivers real-time intelligence for bike distribution. BICO enables automated and optimal decision-making and has been purpose-built to remove the complexity from managing resources, delivering distribution tasks directly to Field Operatives on demand. 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.

Gaining New Insights by Capturing Global Data


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Solving City Transport with Artificial Intelligence 

While there is currently no one international body that runs bike share schemes, members of the North American Bikeshare Association (NABSA) have developed an open standard for schemes across North America, and made its data available for public use.

The open data standard, GBFS (General Bikeshare Feed Specification), means that anyone can access the data, and makes life much easier for app and software developers such as ourselves to create new innovative ideas for bike share schemes globally. A unified data feed now enables us to access data without coding too much.

 We have integrated the GBFS with our BICO system to capture new intelligence and make the BICO recommender smarter, ultimately improving customer experience. As the data drives the decision making process in real-time, it is important to know the status of the network at any given moment and be able to make informed decisions. The unified data source enables bike share schemes to be more proactive and innovative in certain cities by reviewing real-time data.

Now that there is a standardised way of seeing the data across North America, we can easily discover what makes certain bike share schemes work, why people struggle in others, if there are size or distribution issues, and how they can be overcome. It enables us to look at real-time traffic data and provide recommendations easily for schemes looking to set up or grow and become more efficient.

It allows systems like the BICO recommender to identify where the bikes in each scheme go, where they come from, and any seasonal trends that develop such as in summertime near parks and major landmarks, and decline in winter.

It will also impact users as we are also looking to improve customer experience and change the perception of transport. We can start to reduce the traffic on the roads and think about our future as a community. We can also promote the health benefits of bike share schemes by reducing pollution, as well as improving the fitness of those using the bike share schemes. 

Despite its niche market placement, bike share schemes generate and share large amounts of data for free, spurring innovative solutions such as TransitApp as developers have access to the data they need to build these apps.

However, other modes of transport don’t currently share this open approach and therefore chances of development for more efficient systems are stunted. Where data isn’t openly available, developers can’t advance technologies and create solutions for users getting around cities. With open integration in other modes of transport, we can look to create faster, integrated transport solutions and evolve our smart cities.

Version 6 of BICO is released.


BICO v6 Supports usability measures for cycle scheme performance

Version 6 provides additional map views in the BICO operator console to help operators to make optimal logistics decisions.

These new map views helps operators visualise the logistics task in more dimensions than the standard map view and helps to support more user oriented service level agreements.

EXISTING VIEW – Station Status View

The BICO console has always had the Empty Full Station Status map view that highlights the empty and full status of stations on a map.

Standard View

This view shows how BICO supports and optimises scheme performance for traditional service level agreements such as docking station Empty / Full measures and penalties.


Two new map views have been added to Version 6; Usability & Unpreparedness these views support additional scheme performance measures of Usability.

Usability view

Usability is a measure of scheme performance that Stage Intelligence has developed as a method of efficiently servicing zones within a cycle hire scheme.

Useability View

The usability measure focuses on the user experience, so that with a defined walking distance there is access to a docking station to hire or park.

Usability stops the over servicing or under servicing of areas/zones.

Unpreparedness View

This view is similar to the standard view except that the unpreparedness view shows more detail behind the level of empty full.


This view is particularly useful to help identify potential problems in zones before they become a problem and helps the operator take pre-emptive action.

This view also demonstrates the predictive nature of BICO.



Cycle Logistics: It’s a numbers game… Big numbers!

The calculations involved in providing cycle logistics for the Barclays Cycle Hire Scheme is huge, the number of tasks and options that need to be calculated at any one point in time is beyond belief; 7.0724 x10^56 or close to the number of atoms in the visible universe!

The scheme comes in for criticism mainly when users cannot find a bike and most importantly cannot find a space on their way home to catch a train. The operator gets criticised for its inability to move bikes around the scheme. But the numbers go some way to proving the logistics problem.

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Service Level Agreements need to meet user demand

Stage Intelligence has analysed cycle usage information from Cycle Hire Schemes and developed some unique solutions based on Artificial Intelligence to help predict and manage cycle usage across large city wide schemes. This ability can also be used to create and manage Service Level Agreements that meet the needs of cycle users.

Stage Intelligence has created a White Paper that highlights some of the issues and usage patterns. We present our thoughts and finding as a discussion framework that Cycle Scheme Owners and Operators can use to help them create a workable SLA to meet the demands of cycle users.

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Keep your members – Cycle Hire Schemes

Improve Cycle Hire Scheme Performance:

For membership numbers to increase, cycle hire scheme users need to depend on the scheme like any form of transport. Not being able to find a bike or a parking space is like going to a bus stop and finding out there are no buses today… but if you walk 1/2 a mile you might be able to get one! How many times would you do this before you gave up using buses?

Cycle Scheme owners can improve the scheme performance by optomising the network to improve cycle and space availability. Stage Intelligence are specialists in complex Artificial Intelligence Solutions for Logistics Problems. Our Logistics Recommender BICO – can calculate the optimum instruction to give to truck drivers in real time. Instructions includes “go to a specific Cycle Docking Station” and  “pick-up or drop-off X bikes”.

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