Novel algorithms for solar-powered in-road sensors to measure speed and classify vehicles to inform intelligent traffic management decisions
Lead Participant:
CLEARVIEW TRAFFIC GROUP LIMITED
Abstract
The primary objective of our consortium, which is comprised of a UK based technology SME and an English University, is to investigate the capabilities of solar-powered, in-road, autonomous sensor systems to collect accurate and reliable traffic data, including speed and vehicle classification. This data will inform intelligent decision-making by transport managers and road network operators in order to minimise congestion and the associated emissions, reduce accidents, and lower the costs of installation and maintenance traditionally linked to in-road traffic data collection systems. In order to measure the speed of vehicles passing with appropriate accuracy, two detectors must be placed some distance apart. Wireless communications negates the need for costly and problematic cables linking the sensors. However, it is impractical to send all the data collected from one sensor to another due to the size, power and capacity limitations. Thus, the key challenge is to develop a set of algorithms capable of compressing vital data to enable it to be sent wirelessly to another sensor in the network to create actionable information and inform intelligent traffic management decision-making.
is to investigate a trade-off between the detection and classification performance on one hand and power efficiency and communication constraints on the other handa
is to investigate a trade-off between the detection and classification performance on one hand and power efficiency and communication constraints on the other handa
Lead Participant | Project Cost | Grant Offer |
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CLEARVIEW TRAFFIC GROUP LIMITED | £86,627 | £ 64,970 |
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Participant |
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UNIVERSITY OF BEDFORDSHIRE | £30,000 | £ 30,000 |
UNIVERSITY OF YORK |
People |
ORCID iD |
Shona Wooding (Project Manager) |