President Biden will address world leaders at the United Nations on Tuesday. The best way to get around is by taking the subway or by walking. The traffic surrounding the presence of world leaders though, isn’t pretty for commuters. "We can analyze the congestion, traffic jams and accidents, or we can use it for simulations for city planning.NYC braces for gridlock: UN General Assembly fuels rush hour traffic surge "If we can get access to the traffic cameras, then we can apply the same system," Nguyen said. cities such as Los Angeles, Seattle or Atlanta. Once perfected, the technology could potentially be applied to any urban area with traffic issues, including U.S. It can help save on labor costs and frees them up to address issues." If you have AI, it can tell you, 'Hey, there’s a traffic jam over there' or 'There's an accident over here,' for example. "You have 10,000 cameras and at least 1,000 people looking at the cameras all the time," Nguyen said. In addition to creating simulations for city planners, the National Science Foundation wants the team to focus on real-time traffic analysis, which will allow workers monitoring city traffic cameras to quickly spot and resolve problems. "We anticipate that the IoT pluggable framework developed from this project can be used for future, similar crowd-sourcing applications for the common good." "We will adopt cutting-edge cloud technologies for handling large-scale and high-volume real-time traffic from our pluggable client-side framework," Phung said. He will help develop a framework that can stream data from IoT devices such as cameras and sensors for the AI component. Phung, co-principal investigator and also a Vietnam native, has expertise in cybersecurity and the Internet of Things (IoT), which allows interrelated, internet-connected objects to collect and transfer data over a wireless network without human intervention. After testing their model's accuracy for tracking vehicles and traffic flow, they will scale up the project to include data from all 10,000 cameras across the 796-square-mile city. Their challenges include training the state-of-the-art AI model to detect vehicles at night and during Vietnam’s rainy season, which creates camera noise and distortion. Tam Nguyen and Phung are using the data to train and evaluate their AI algorithms in University of Dayton computer labs. Nguyen and Phung's research collaborators in Ho Chi Minh City - including Minh-Triet Tran from the University of Science, and Duy-Dinh Le and Khang Nguyen from the University of Information Technology - provided one month's worth of data from about 100 traffic cameras in the city’s center. "Motorcycles weave through traffic like in the movies" "If you find an empty space, you just ride your vehicle there," said Nguyen, a Ho Chi Minh City native. Motorists tend to travel fast, cross at red lights and ride their motorbikes on sidewalks to circumvent traffic on city streets. "We analyze the data by using AI - artificial intelligence - and then we know exactly what traffic will do."įormerly known as Saigon, Ho Chi Minh City is well-known for its traffic congestion and high density of vehicles, with nearly as many motorbikes - 7.5 million - as residents. For this project, we can retrieve exact data," said Nguyen, the project's principal investigator, whose research focus is computer vision and machine learning. "Normally, for simulations we have to create some dummy data. The process, known as visual crowd-AI sensing, will provide city planners with actual data for simulating infrastructure changes, such as adding a new bridge or changing the direction of a traffic lane, to determine their potential impacts. Data from Ho Chi Minh City’s network of 10,000 traffic cameras allows for real-time analysis of traffic flow, congestion and accidents. With a $248,338 grant from the National Science Foundation, the pair is using artificial intelligence to help urban planners address traffic and infrastructure problems in Ho Chi Minh City, Vietnam - a city of 8.9 million people. Is a world without gridlock possible? It might be, thanks to research by Tam Nguyen and Phu Phung, assistant professors in the Department of Computer Science.
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