IIT-H launches M2Smart Testbed to develop low-carbon transport solutions

Our Bureau Hyderabad | Updated on May 30, 2019 Published on May 30, 2019

Indian Institute of Technology (IIT) Hyderabad has launched ‘M2Smart Project’ Testbed to develop a model to find low-carbon models for the transportation sector.

The Testbed will generate a practical ‘handbook’ on low carbon urban transportation for developing countries based on big data generated by vehicular traffic.

The M2Smart (Smart Cities for Emerging Countries based on Sensing, Network and Big Data Analysis of Multi-modal Regional Transport System) is an Indo-Japanese joint research project. Researchers from Nihon University and Nagoya Electric Works of Japan are also working on the project.

“We are developing carbon dioxide emission estimation methodologies to ascertain energy and carbon dioxide emission reduction by route and mode choices. It will be backed by IoT based traffic management system,” Bheemarjuna Reddy Tamma, Associate Professor (Department of Computer Science and Engineering) of IIT-H, said.

The field testbed system has been deployed at the campus and along a 30-km stretch of the National Highway-65.

The project generates data using cameras, Bluetooth and Wi-Fi sensors, speed detection safety system and conflict warning system using traffic signal lights.

“Electric rickshaws also run inside the campus and are used to understand the driving cycle of e-vehicles,” an IIT statement has said.

“The traffic monitoring is done not only in Hyderabad but also in Ahmedabad where the researchers established traffic monitoring cameras at a major junction. They are able to monitor and compare the real-time traffic data from the two cities,” it said.

“We will be able to have a better understanding of multi-modal transportation in India and be able to provide solutions to reduce the carbon footprint,” U B Desai, Director of IIT-H, said.

The Testbed will also be developing micro traffic simulation models based on the aggregated traffic data from several sensing devices using big data analysis.

Published on May 30, 2019

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