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IIT Hyderabad introduces M2Smart Project Testbed that will help them to generate models that will help to reduce the carbon footprint caused by transportation. IIT-H has set up the testbed at their campus as well as on a stretch of NH65 where with the help of CCTV, Bluetooth and WiFi Sensors.
In order to improve the traffic conditions across India, the Indian Institute of Technology Hyderabad (IIT-H) has initiated the “M2Smart Project” Testbed. The testbed will be used to examine low-carbon models for transportation in India. Using the data that will be collected, the test bed will produce a handbook on the low carbon transportation for urban settlements as well as create traffic flow models.
A field testbed system has already been set up at the campus of IIT Hyderabad as well as on a 30km stretch on NH 65. Some of the major elements of the testbed include technology and units for assessing the real-time traffic flow such as Cameras, Wi-Fi and Bluetooth Sensors, speed detection as well as conflict warning systems with the help of traffic lights along with remote environment gas sensing.
The city of Hyderabad is one of the cities along with Ahmedabad that has installed traffic monitoring systems. The researchers from IIT Hyderabad have also set up systems for monitoring traffic at one of the major junctions in Ahmedabad as a means to compare the traffic conditions between the two cities.
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The Director of IIT Hyderabad stated that installing such traffic monitoring systems are a necessity as India home to various types of transportation modes. With the help of the project, IIT-H aspires to understand the diverse modes of transportation available in India. Through the Testbed, he added, they will be able to understand the multi-modal transportation in India as well as figure out solutions to scale down the carbon footprint in India.
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One of the goals that IIT Hyderabad is aiming at via the research, is to establish a state of the art composite technologies for vehicle-sensing systems. These state of the art technologies will be used to identify all types of vehicles using different sensors such as LIDAR and CCTV.
