Transforming Englandʼs streetlight management with AI
by Univrses04 February 20263 min read
Univrses has completed the first AI-powered survey of a national road system, delivering an accurate streetlight inventory across Englandʼs Strategic Road Network. In partnership with WSP Global Inc., Univrses used its AI-powered system, 3DAI™, to provide National Highways—the operator of Englandʼs major roads—with precise asset data, directly supporting their operational efficiency and Net Zero objectives.
National Highways manages 7,200 km of roads, ensuring safe and efficient travel for millions of users every day. A crucial part of this infrastructure is its vast network of approximately 117,000 streetlights. Street lighting costs National Highways several tens of millions of pounds annually in electricity and accounts for 58% of their corporate carbon footprint, contributing more than 15,000 tonnes of CO₂ annually. National Highways has been working to upgrade a significant portion of its streetlights from traditional luminaires to energy-saving LEDs. This transition is a key part of National
Highwaysʼ strategy to reduce electricity costs, cut carbon emissions, and support its long-term goal of achieving Net Zero corporate carbon emissions by 2030.
To plan this transition, measure progress and to forecast future energy savings, National Highways relied on an existing inventory of the street lights. However, it soon became apparent that this data was incomplete and outdated. This created major uncertainties, directly impacting National Highwaysʼ financial and operational planning:
Unclear planning for future upgrades – Missing or outdated records made it difficult to plan and execute the LED conversion program as well as optimize general maintenance of streetlights.
Unverified LED upgrades & billing discrepancies – a lack of up-to-date records complicated efforts to verify current energy consumption and made accurate assessment of electricity costs impossible.
Inaccurate cost projections – Without precise data on streetlight types and locations, National Highways was unable to estimate energy savings and the long-term financial impact of the transition to LEDs.
As such, there was a clear need to create an up-to-date inventory of National Highways streetlight assets. Traditionally, carrying out an inventory of such an extensive streetlight network requires labor-intensive manual surveys. These surveys pose significant safety risks as personnel must carry out their surveying tasks near live traffic. Reducing this risk was a key driver in National Highwaysʼ decision to explore an alternative approach.
Beyond safety concerns, the financial and logistical strain of manual inspections is significant. A full manual inspection of the road network would have required approximately 2,000 night shifts. With each night shift costing £5,000 per km surveyed, surveying costs alone (not accounting for traffic management or road closures) would have amounted to millions of pounds—a cost that was prohibitively large.
The Solution: Accurate streetlight data, powered by AI
In considering how to address the challenge, several automated approaches were explored—including the adoption of technologies such as LIDAR, optical imaging, satellite analysis and machine learning.
Univrses' AI-powered system, 3DAI™, was selected as the most effective solution. It is based on optical image capture and machine learning, and offers the best balance of speed, cost-efficiency, and precision.
Univrses deployed the system across the network, enabling detection, classification, and positioning of more than 100,000 streetlights in less than 3 months. The output was a structured dataset that National Highways could integrate into its asset management systems to inform decision-making and track the progress of LED upgrade and maintenance programmes. WSP reviewed and validated the results, ensuring the data met strict quality standards before being delivered to National Highways.
Read more: https://univrses.com/newsroom/case-studies/transforming-england%CA%BCs-streetlight-management-with-ai
Highwaysʼ strategy to reduce electricity costs, cut carbon emissions, and support its long-term goal of achieving Net Zero corporate carbon emissions by 2030.
To plan this transition, measure progress and to forecast future energy savings, National Highways relied on an existing inventory of the street lights. However, it soon became apparent that this data was incomplete and outdated. This created major uncertainties, directly impacting National Highwaysʼ financial and operational planning:
Unclear planning for future upgrades – Missing or outdated records made it difficult to plan and execute the LED conversion program as well as optimize general maintenance of streetlights.
Unverified LED upgrades & billing discrepancies – a lack of up-to-date records complicated efforts to verify current energy consumption and made accurate assessment of electricity costs impossible.
Inaccurate cost projections – Without precise data on streetlight types and locations, National Highways was unable to estimate energy savings and the long-term financial impact of the transition to LEDs.
As such, there was a clear need to create an up-to-date inventory of National Highways streetlight assets. Traditionally, carrying out an inventory of such an extensive streetlight network requires labor-intensive manual surveys. These surveys pose significant safety risks as personnel must carry out their surveying tasks near live traffic. Reducing this risk was a key driver in National Highwaysʼ decision to explore an alternative approach.
Beyond safety concerns, the financial and logistical strain of manual inspections is significant. A full manual inspection of the road network would have required approximately 2,000 night shifts. With each night shift costing £5,000 per km surveyed, surveying costs alone (not accounting for traffic management or road closures) would have amounted to millions of pounds—a cost that was prohibitively large.
The Solution: Accurate streetlight data, powered by AI
In considering how to address the challenge, several automated approaches were explored—including the adoption of technologies such as LIDAR, optical imaging, satellite analysis and machine learning.
Univrses' AI-powered system, 3DAI™, was selected as the most effective solution. It is based on optical image capture and machine learning, and offers the best balance of speed, cost-efficiency, and precision.
Univrses deployed the system across the network, enabling detection, classification, and positioning of more than 100,000 streetlights in less than 3 months. The output was a structured dataset that National Highways could integrate into its asset management systems to inform decision-making and track the progress of LED upgrade and maintenance programmes. WSP reviewed and validated the results, ensuring the data met strict quality standards before being delivered to National Highways.
Read more: https://univrses.com/newsroom/case-studies/transforming-england%CA%BCs-streetlight-management-with-ai
