How a data-led model for city transport will end the gridlock

Data science is increasingly influential in shaping the world we live in, the choices we make and the quality of goods and services we access. As consumers, we’re used to tailored communications from retailers and service providers.   

We understand that we share information about ourselves through many of our interactions online. And, in the most part, we are accepting that this exchange of data results in more individually-tailored and beneficial experiences. 

In the context of local government, data science has a  pivotal role to play in delivering personalisation, efficacy and efficiency. Local policy makers can harness and analyse both internal administrative data and data from third parties to make government more responsive to citizens and more targeted in its use of resources.   

Data science is potentially particularly revolutionary for small and medium sized cities, as it offers an alternative way of realising the ‘smart city’ vision, which does not rely on the installation of expensive sensor grids. 

So, what do the benefits of collecting, analysing, and acting upon data look like in the vital area of transportation? 

Well, first off, there’s no doubt that there is room for improvement. The ideal of reliably fast, smooth and effortless travel, whether by road or rail, has not yet been achieved. 

Instead, many of us who drive in urban areas will agree that this has become a joyless task. The powerlessness of sitting in gridlocks, or circling city blocks repeatedly in search of non-existent parking spaces; Traffic jams, road rage, commuter hell. There isn’t much positivity associated with getting from A to B in and around our cities.   

The time-wasting scenario of being unable to move around freely is painful for individuals, causing high levels of stress; and damaging to the environment, increasing air and noise pollution. So, there are big challenges that need to be faced. 

Defined as the smarter, greener and more efficient movement of people and goods around the world, intelligent mobility has the power to revolutionise transportation, providing solutions to a wide range of problems, including road congestion.    

The technology is already here. We’re seeing data-led transportation models working well in some UK cities. For example, in London, Oyster card data is being analysed to help optimise the London tube network system; and smart parking meters have been adopted in many cities to make parking a more seamless experience. 

At Purple, we’re playing a part in this exciting movement. Earlier this year, we won a contract, along with Cisco and other partners, to design and implement a data-led smart transportation platform for the Greater Manchester city region. With the city among the most congested in Europe, the scheme will use location-based data from Bluetooth and WiFi network operators to inform a responsive transport solution.   

The ultimate data-led model is found in ‘smart cities’, defined by The UK Department for Business, Innovation and Skills (BIS) as a process rather than a static outcome, in which increased citizen engagement, hard infrastructure, social capital and digital technologies make cities more livable, resilient and better able to respond to challenges.   

In these urban ‘intelligent spaces’ a network of sensors is combined with Internet of Things technologies and multiple data sources. This will include data about how individuals navigate around urban areas, which is of great value in helping to identify traffic bottlenecks and find solutions to congestion and a range of other problems. 

In ‘smart cities’, it’s possible for traffic controllers to see a digital map of the transportation network at a glance. They can rapidly spot and drill down into a detailed visualisation of areas of the network that are experiencing delay. This approach accelerates the decision-making process to clear congestion more swiftly. 

With advanced data analytics, it’s possible to route emergency vehicles intelligently in real-time; give drivers warnings about road hazards; identify whether the bus route start times are correct; or establish the best location to add additional bus lanes. There are huge possibilities for traffic flow optimisation and urban planning. 

As ever, the future is even more exciting. As the Internet of Things continues to expand with great momentum, the volume and variety of data related to transport and mobility is increasing rapidly. Relevant datasets will include: maps; weather; personal location; network disruptions; planned events; real-time network capacity; public transport schedules; vehicle location; CCTV; and service user sentiment. 

The increased range and scope of the datasets becoming available will make their coordination more challenging. As a result, all transport companies will be expected to be data companies essentially, exploiting the deep insights of their analyses. 

In the long term, we will see autonomous vehicles navigating the streets. The UK has invested millions in research, carrying out trials of driverless pods, looking at how the technology interacts with the environment and other road users.   

Experts suggest that in around 10 years from now, a car will be able to drive itself from door to door without a driver touching the wheel. This will include driving in city environments that feature traffic lights, junctions and roundabouts. Cars will be connected wirelessly to each other and communicate with the road infrastructure to make decisions on traffic and journey times.   

Intelligent mobility, enabled by real-time data-sharing and interpretation, is an exploding mega trend. We will see better integration of existing transport systems, optimisation across multiple transport networks, and new forms of on-demand mobility for people and things. Data science will become the beating heart of transport systems – to me, this is incredibly exciting.   

Gavin Wheeldon, CEO, Purple 

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