By Adrian Malone, Head of Digital Project Delivery, WSP in the UK
Last week, in our first post in our new series on digital innovation, Adrian Malone outlined how building a digital twin was the first step to enhancing how network providers deliver safe, efficient road schemes that customers want. Now he discusses what comes next.
With the digital twin up and running, it’s time to harness the power of algorithms. These machine-learning tools can examine huge volumes of data and spot patterns that would go unnoticed by people. In the consumer sector, firms like Uber, Amazon and Airbnb have built disruptive and very successful new business models on their ability to analyse vast amounts of data.
In infrastructure, there’s huge potential for artificial intelligence to tackle complex design challenges. However, the real advantage comes not only in the algorithms themselves, but how we use their output to enhance our understanding of networks. Algorithms alone are one thing, but when combined with the experience, judgement and professional insight of our transport professionals they could create something potentially game changing.
The digital twin can present patterns and the outputs of simulations which transport professionals validate and interpret. Ultimately, they make operational and investment decisions based on their conclusions. They will ask many of the same questions they ask today, but they will be able to access the answers faster, with better information, and on a bigger scale than ever before.
Self-aware infrastructure
Imagine a scenario where a digital twin is providing a data-rich picture of a road network. Software using artificial intelligence constantly seeks patterns across all this data – it becomes ‘self-aware’, not only spotting current problems but also finding potential solutions to emerging congestion or capacity issues in real time.
For example, artificial intelligence could identify where adjustments to timing of traffic signals is needed by spotting patterns, like if queues form on a particular arm of a roundabout at a certain time of day through analysis of traffic flow data. The ability to analyse vast amounts of real-time data in the light of current and forecast demand, provides huge opportunity to make real time, dynamic adjustments to the operation of our road networks.
As things stand today, a highways planner simply doesn’t have the full picture, or at least painting that picture could potentially take weeks. They don’t have access to the array of data that would feed into a digital twin, and the data they do have is often fragmented across too many systems, making it impossible to combine or generate meaningful insights. For instance, identifying a pattern of rear shunt accidents caused by motorists driving directly towards the sun. Algorithms would be able to spot a pattern that shifted with sunrise / sunset times and season, which may not be apparent from more traditional data analysis.
When it comes to modelling new schemes, the lack of relevant information often means surveys are needed to get data on existing infrastructure. The digital twin, updated with as-built information and survey information as work is completed, would reduce this need over time. This rich data would allow future improvement schemes to be identified with algorithms helping to prioritise these based on predicted cost and customer benefit, and then monitor real-world performance of implemented schemes to refine and improve the model over time.
In the case of the digital twin, algorithms, and transport professionals, the sum really is greater than the total of the parts. Alone, each completes the tasks and provides the benefits we expect and are used to, but together they can create a powerful and innovative force, which ultimately benefits those who matter most – our customers.
In the next post, we will look at how the digital twin can then enable planners and designers to quickly ‘draw’ a range of specified and costed options for a new scheme.
About Adrian Malone:
Adrian is Head of Digital Project Delivery and BIM for Transport & Infrastructure at WSP in the UK. With more than 20 years of experience in the construction sector, Adrian has spent the majority of his career engaged with innovation and research in BIM and digital training including EU-funded research on industrialised construction, BIM initiatives with professional institutions such as RICS and APM, and most recently, i3P. Adrian has a master’s degree in information systems, and combines his technical knowledge with a strong people and customer focus. He has experience in contacting and consulting organisations as well as both construction and facilities management. Adrian is a passionate advocate for innovation and digital transformation in the construction and engineering sector. Follow him on LinkedIn here.
