Artificial Intelligence has a huge potential to streamline the Asset Management process currently used on our highways. By automating our systems and using technology to perform laborious tasks, time can be better-spent by contractors and surveyors whilst human error is greatly reduced. However, as ever with new technology there are always considerations which need to be accounted for, in this case, that data quality and collection is consistent across the country to ensure that the information we receive is fully harnessed.
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Transportation Asset Management (TAM) is a process focused on creating the most efficient road infrastructure planning and management systems possible. The process allows for road assets (drainage, carriageways, footways, cycleways, bridges, retaining walls, lighting columns, traffic management systems, street furniture etc.) to be evaluated, maintained, and improved. Through this, local highway authorities can ensure that roadways are safe, resilient, and well-designed for all types of road users, including pedestrians. This constant assessment also means that transparency on funding, spending and accountability is available to both the government and the public, which when implemented well should lead to better value for money and improved customer service.
Historically, road evaluation, an essential part of TAM, has been an arduous and sometimes dangerous task for those involved. Contractors or surveyors would have to spend long hours slowly driving along highways whilst visually analysing road assets such as traffic signs and road surface conditions. This task would also require these people to leave their vehicles to measure or inspect the assets by hand, an undertaking that would often raise safety concerns, as any job that involves standing close to moving traffic would. Understandably, these methods also suffer from a degree of human error, where even experienced eyes may not spot a defect in the road.
Artificial Intelligence (AI) software can greatly improve TAM processes making them more time-efficient, accurate and safer for those involved. Vehicle-mounted cameras equipped with laser technology can be used to capture and automatically analyse road assets, whilst the vehicle drives at the speed of traffic. Combined with GPS, distance measurement and AI annotation, a whole range of assessments can be made without requiring the surveyor to leave their car.
This technology can provide new services as well as beneficial updates to existing process such as:
Real-time assessments can be made as data is compared with live databases to provide dilapidation reports, maintenance actions, etc. This has the potential to save a huge amount of time and allow evaluations to be more accurate which will create a better understanding of assets – a key recommendation in CIHT’s Improving Local Highways Report.
AI can be used to perform high-level analysis that consults historic weather conditions, traffic conditions, and common data trends to predict future conditions and foresee various scenarios. This can be used to predict future road conditions or upcoming faults with road assets, as well as to evaluate the effectiveness of repairs or new, replacement assets. All of this can then feed into effective planning and maintenance strategies.
As we move towards a more connected and autonomous future, we could see public and private vehicles equipped with AI becoming essential tools to monitor the conditions of our road assets. This technology could provide constant, daily data, allowing local highways to gather an accurate and extensive picture of their roads network.
Whilst AI can be extremely useful for identifying potholes on our highways, there are some metaphorical potholes to this new technology that we need to be aware of as it becomes a larger part of the way we operate.
At a time when many local authorities are struggling with funding and budgeting, spending money on the latest AI technology for asset management may not be a top priority. As with all new technologies, ‘state of the art’ performance is often associated with a high price tag that many won’t be able to afford. As the government moves to encourage local highway authorities to have flexibility to choose whichever surveying technology best supports their asset management strategy, this can lead to a disparity in data quality between areas that can afford more advanced technology, and those that can’t.
In the words of Dr. Ian Malcolm (Jeff Goldblum’s character in Jurassic Park) ‘Your scientists were so preoccupied with whether they could, they didn’t stop to think if they should’. Being able to capture every detail of the road network sounds impressive, but the result is an incredibly large set of data that will need processing, formatting and manipulating to be able to extract useful information from. Whilst some of this can be automated, an element of manual data handling will still be required. This is a job that can be far more effective when data collection methods focus on collecting information for specific use cases, thus making it easier to track trends.
As technology improves and we build a more detailed picture of our road assets this information will need to be centralised and regulated to make sure that the benefits of AI are fully utilised. There is no point in spending money on developing and procuring this technology if the information gleamed from it is not harnessed to it’s full potential. When data is shared and updated regularly it leads to improved algorithms that will be able to provide an accurate and holistic picture of our highway network which can be used to correctly predict the future performance of our road assets.
In the next installment of the CIHT Monthly Masterclass Dr Janvi Shah (Head of Asset Management Strategy) and Lila Tachtsi (Asset Management Director) both from National Highways will be discussing the direction and future of National Highways’ Asset Management Policy and Approach. We will also hear from Rob Gillespie (Director, Hounslow Highways and CEO, VINCI Highways) who will provide an insight into Asset Management in the local, urban environment.
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