Successful smart city projects need to balance technology, governance, economic and social needs. Chordant’s Jim Nolan looks at how to find a way ahead.
While there has been a lot of media hype around smart cities for the past two or three years now, the concept of a smart city isn’t a new one – in fact, the concept has existed for nearly ten.
However, new technologies such as big data analytics, cloud architectures, multi-access edge computing and virtualisation are pushing the boundaries of urban planning further than ever before. And these disruptive and far-reaching innovations are helping us get a better understanding of how to build cities that are able to grow and remain liveable and sustainable.
Most, though, are busy investigating how to build smart cities without considering what’s already out there. Most cities, just like any industrial enterprise, will have a large number of sensor-based systems that generate a great deal of siloed data. This data is extremely valuable – and can transform the way in which cities operate.
Smart city development often follows one of two schools of thought. The first uses technology as the basis to “smarten” existing cities and their services. The second takes a much broader approach and comes from the top-down, defining a vision and strategy first. But what approach is the best? And how is data to be used within these approaches to help build smart cities?
The technology-driven approach
The first, and often the most common, approach to building smart cities is driven by a single city department building a single technology use case, such as smart lighting or smart garbage bins.
These deployments are proof of concepts in a limited geographic area. While they attract huge media hype and a great deal of fanfare, marketing the city as a great place to live and work, a single use case can’t make a city smart.
This is because these deployments don’t provide a path or roadmap forward for the city as a whole. Once the technology has been implemented, there is no ‘next step’– they simply replace the existing service – which makes it difficult to measure their return on investment. To give a real-world example, does a smart garbage service truly contribute to the city’s overall performance, or is it just a ‘nice to have’ service?
Although the implementation of such technology will undoubtedly provide some improvements to the city, the problem with having a single technology is that it doesn’t drive enough insights —which are crucial for a city to truly be smart.
Instead, these single use cases need to form and be a part of a broader smart city ecosystem, integrated into the IT and operational infrastructure that the city already has in place. Without incorporating data from multiple use cases, cities simply won’t be able to scale the value.
The top-down approach
The second approach, driven from the top down, looks at the “digitalised” city from a wider, more mature perspective. A number of larger cities have announced ambitions to become fully integrated smart platforms and have recently issued RFIs (request for information) or RFPs (request for proposals) designed to help cities drive smart city innovation.
These requests have typically been issued by city officials to form business relationships or partnerships to assist the city in planning and developing smart city projects.
These larger cities understand that in order to demonstrate significant benefits, such as better governance within businesses and governments, better utilisation of infrastructure and other assets like sustainability, increased safety, and affordability, a more comprehensive approach is required.
But just like with the first approach, in order to achieve this goal, data needs to be integrated between each new smart deployment, as well as with any other sources of data the city already has in place.
However, this approach does not come without its challenges – for example, conclusions sourced from collated data might require consulting support to action them. This requires big investments and usually a need to realign city departments and organisations to meet the level of integration required — an undertaking which can be daunting or too expensive.
So, how do cities successfully implement new services, scale technologies and integrate data in order to make the smart city dream a reality?
Finding the middle ground
Successful smart city projects need to balance technology, governance, economic and social needs. On the one hand, building and applying unique smart solutions one at a time is not very strategic and won’t be as beneficial in the long-term. Mostly because, with no equivalent benchmark, it’s hard to measure the success or scale.
On the other hand, cities that put a lot of pressure on applying new technologies or new sensor systems at scale and from the ground up are exposed to huge costs and time-consuming negotiations within many governmental bodies. That’s a big job, especially for a city that’s just starting its journey to becoming smart.
The answer? Finding the middle ground. Before constructing the entire model from scratch, it’s worth looking at integrating existing data from sensors that are currently siloed. From the beginning, the focus should be on the use of an open standards-based platform that can scale to support multiple use cases without locking the city into a single proprietary approach.
The oneTRANSPORTTM Data Marketplace has tried and tested this method, supporting several use cases in the transportation space, including the management of traffic around Watford Football Club’s stadium on match days. During the football season, matches at Watford attract over 20,000 attendees and, as the car is the preferred choice of transport for most of the fans, this puts significant pressure on the town’s roads and car parks. Typically, this would not only cause major traffic on the roads surrounding the stadium, but also in the town centre.
To help manage the traffic flows around Watford, real-time transport data from various source systems and suppliers was analysed and displayed, contributing to reduced pre-match queues and much smoother journeys for travellers, post-match.
Through pre-match monitoring of car park fill rates and occupancy, variable message signs could be placed around the town centre, advertising alternative car parks for visitors to use. This reduced the queues around popular car parks and as a result, congestion dropped from 50% to 3%.
The oneTRANSPORT Data Marketplace has also been used for complex traffic management at Silverstone Formula 1 Race Weekend, as well as for Oxford’s Park and Ride service. The platform was used by Oxford Council, a public bus operator, as well as two analytics providers and two app developers, to exchange data and suggest the most convenient transport options for users. By applying prediction models created by the analytics partners, such as projected available parking, and combining this with real time bus information and car journey times, oneTRANSPORT Data Marketplace was able to enable congestion reduction and increase the use of the Park and Ride bus service. This and similar use cases present a forward-thinking and results-driven strategy that is both realistic and scalable.
Smart vision, strategy and execution plan
If cities are to successfully harness the opportunity presented by the IoT and smart city technologies, city officials and technology developers need to understand the importance of creating a non-fragmented network, unlocking hidden data about the operations of towns and even regions that currently resides in countless closed siloed systems.
While traditionally there have been two different approaches cities have taken to achieve the smart city dream, finding the middle ground between the two presents the most value. By gathering insights from different technologies and using these together, city officials will be able to collaborate with specific organisations and manage cities better without the huge costs associated with it.