Indra to develop detection system for sustainable mobility programs

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Spanish consulting and transportation technology company Indra is leading the European BeCamGreen R&D&i project, which aims to develop a system based on computer vision and big data that will help reduce traffic, particularly single occupant vehicles, while aiding new sustainable mobility policies.

The project is being financed and overseen by EIT Digital, a leading European digital innovation and entrepreneurial education organization, and Indra will work alongside the Polytechnic University of Milan in Italy.

The project will perfect and test, in a real traffic scenario, a product that is unique and market-ready, which can automatically identify vehicle types in real time and recognize its number of occupants, in both front and back seats. The system will allow local authorities and transport agencies to understand existing mobility patterns, and then define strategies and policies to reduce traffic congestion, prioritize and promote the use of public transport, and high-occupancy or low-emission vehicles, resulting in improved traffic, air quality and noise levels.

BeCamGreen will develop an automated, non-intrusive system using state-of-the-art big data, computer vision, deep learning and multispectral analysis technologies. Indra will work on improving the image processing algorithms for face and body detection that it started to develop in previous R&D&i projects. To achieve the highest precision, the company will include better vision equipment and will combine existing algorithms with new ones to improve accuracy.

The solution will also include multispectral analysis for detecting human skin, in order to avoid false or erroneous detections, helping to differentiate between a dummy and a person, for example. Cutting-edge technology, in both hardware and software, will be incorporated to increase the system’s precision and cut investment and operating costs for potential clients.

The Polytechnic University of Milan will focus on developing a big data engine to detect and predict traffic situations by using and integrating data in real time from IoT sensors, social networks, different types of open data, and the project’s vision subsystem. The real-time macro big data engine will contribute valuable information to help managers in their decision making and in validating and improving their mobility management strategies.

Currently, this type of solution is being demanded in the USA, where the number of HOV/HOT (High Occupancy Vehicles / High Occupancy Toll) lanes is increasing. In Europe, the platform is intended to be a key element for the demand management and access strategies that are being deployed in many cities, based on occupancy, vehicle type, license plate or peak hours.

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Tom has edited Traffic Technology International magazine and the Traffic Technology Today website since he joined the company in May 2014. Prior to this he worked on some of the UK's leading consumer magazine titles including Men's Health and Glamour, beginning his career in journalism in 1997 after graduating with a law degree from the London School of Economics (LSE).

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