Sustainable solution for smooth rush-hour traffic using new simulation techniques

Dr Aparna Deshpande

How about foreseeing a day where your daily drive to work place is channelized in a way so that you don’t have to pull on a red light. In a recent study, researcher from MIT have developed a machine-learning approach which ensures a smooth flowing traffic of autonomous vehicles at signalized intersection. If any vehicle stops at red light it adds up to the fuel consumption and green-house gas emissions as well. One can time the trip so that you arrive on any intersection when the light is green.

Simulations were performed in order to control a cluster of autonomous vehicles in a way that keeps traffic flowing smoothly. Dealing with intersection algorithm is intricate as it gives rise various different scenarios like the number of lanes, how the signals operate, the number of vehicles and their speeds, the presence of pedestrians and cyclists, etc says the lead author of the study Vindula Jayawardana, a graduate student in Laboratory for Information and Decision Systems (LIDS) and the Department of Electrical Engineering and Computer Science.

The mundane approach of mathematical models for resolving traffic uncertainties doesn’t seem to be fruitful as the traffic patterns are messy worldwide. In a new study, MIT researchers demonstrate a machine-learning approach that can learn to control a fleet of autonomous vehicles as they approach and travel through a signalized intersection in a way that keeps traffic flowing smoothly. Using simulations, they found that their approach reduces fuel consumption and emissions while improving average vehicle speed. The technique gets the best results if all cars on the road are autonomous, but even if only 25 percent use their control algorithm, it still leads to substantial fuel and emissions benefits. Typical approaches for tackling intersection control problems use mathematical models to solve one simple, ideal intersection. That looks good on paper, but likely won’t hold up in the real world, where traffic patterns are often about as messy as they come. Jayawardana et al. have come up with a new technique “deep reinforcement learning “where the controlled alogorithms learns to make a sequence of decisions.

The key aspects of reinforcement learning is it leverages assumptions learned by a neural network to find shortcuts to good sequences, even if there are billions of possibilities. For solving a long horizon problem, the control algorithm accelerates around 500 instructions to an autonomous vehicle over a longer time duration. The get the sequence right it has performed dual operation of mitigating emissions and getting to the intersection at a good speed says a senior professor Cathy Wu who leads the study. The researcher team established an approach known as reward shaping which gives the system some domain knowledge which enables it to learn on its own. The system was penalized every time when it came to a complete stop, so that it avoids this action. The control algorithm connects with vehicles, which communicates with upcoming traffic lights to receive signal phase and timing information and observe the surrounding traffic scenario. The control algorithm tells each vehicle how to accelerate and decelerate. This technique will result in sustainable mobility. Recognizing facts of interference that are small changes to the system but have significant impact is something that needs to be addressed says the research team with a hope of providing a greener solution to the current traffic scenarios

Please see the following news Source(s) and original reference(s) therein:  https://www.sciencedaily.com/releases/2022/05/220518113851.htm

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Dr Smita Chaturvedi
Smita is an experimental condensed matter physicist. The quest for multifunctional materials motivates her. Smita finished her PhD from RDVV Jabalpur and BARC Mumbai in the year 2002 and worked as a research associate in IIT Mumbai and Oakland University, Michigan. Smita was awarded as a Fulbright Nehru Academic and Professional excellence fellow in 2018-19. Smita holds more than a decade of research and teaching experience. She possesses good knowledge about education system and research opportunities in India as well as abroad. Music and gardening are her mindful meditations.
Dr. Priyadarshini Karve
Dr Priyadarshini Karve has worked in the areas of household energy, decentralised waste-to-fuel technologies, climate change mitigation and adaptation, sustainable and climate-ready urbanisation, etc. She runs her own social green enterprise Samuchit Enviro Tech in Pune, focused on enabling access to sustainable products and services. She is also a co-founder of OrjaBox, a startup promoting solar thermal technologies. Dr Karve is a Founder Member of Clean Energy Access Network (CLEAN - a multi-state society of decentralised renewable energy practitioners and entrepreneurs) and Cleaner Cooking Coalition (CCC - an international organisation focused on promoting user-centric cooking energy technologies that are good for health as well as climate). She is also the National Convener of Indian Network on Ethics and Climate Change (INECC - focused on climate justice issues and working to bring people's voices in policy choices). Her work has been recognised by several national and international awards and honours.
Sanjay Khare
Sanjay Khare ,after his graduation in Electrical Engineering from Indian Institute of Technology, Kanpur, in 1986 has been associated with major Japanese & European Automotive OEMs in Indian Subcontinent for 35 years . Widely travelled across Europe & Asia , he has held positions across diverse functions of Automotive Corporations.
In his current role as Board Member and Vice President at Skoda Auto Volkswagen India , he is Chief Sustainability Catalyst to guide actions at Skoda Auto Volkswagen India along with the sister brands Audi, Porsche & Lamborghini.
He leads an active Climate Resilience program where the automotive major in India has already achieved Zero Waste to Landfill, Water Positive & Zero liquid Discharge Certification, Zero Accidents , targeting an 18.5 MW of installed Roof Top Solar plant at a single Automotive site in India in 2021 and fully Carbon Neutral Production by 2025.
Sanjay has done his Executive MBA from Management development Institute ,Gurgaon specialising in Strategy & Marketing . He has active interests in driving Cultural Change, Competency building , Human Motivation topics while adopting/ innovating the technological advancements and total quality revolution.He is also a Qualified Independent Director registered with Ministry of Corporate Affairs, Govt. of India. An active speaker on Automotive & Sustainability topics on National & International forums.
In personal life, he is an Endurance Cyclist having participated in many adventure ride expeditions . He also spends his time in developing mastery on Indian Classical Music instrument Sitar.