Passing Traffic Generates Power in Innovative Roads
Researchers from Lancaster University in the United Kingdom are working on the development of materials that would enable passing traffic to generate electricity as it uses the road. One of these so-called smart materials is called ‘piezoelectric’ ceramics.
Researchers from Lancaster University in the United Kingdom are working on the development of materials that would enable passing traffic to generate electricity as it uses the road. One of these so-called smart materials is called ‘piezoelectric’ ceramics. These are embedded into the road and can harvest energy via the vibrations of the passing vehicle.
The predicted energy recovery of this innovative road is thought to be around one to two Megawatts per kilometer under normal conditions (when around 2,000 to 3,000 cars pass an hour). When stored, this around the same amount of energy needed to power between 2,000 to 4,000 street lamps. This way of harvesting energy is a fantastic idea that would not only help save the environment but would help save taxpayer’s money too.
In the United Kingdom, to power between 2,000 to 4,000 street lamps cost between £1,800 and £3,600 per day. If this idea of road energy harvesting technology was installed instead it would cost around 20 percent of that. Professor Mohamed Saafi, the leader of the research project, said: “This research is about helping to produce the next generation of smart road surfaces. We will be developing new materials to take advantage of the piezoelectric effect where passing vehicles cause stress on the road surface, producing voltage.”
“The system we develop will then convert this mechanical energy into electric energy to power things such as street lamps, traffic lights, and electric car charging points,” added Saafi. “It could also be used to provide other smart street benefits, such as real-time traffic volume monitoring.”
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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