Ray Summit 2022
As Qatar is gearing up to host the FIFA World Cup 2022, the mobility of the many thousands of people who will attend is a major hurdle for transport authorities. This is an unprecedented challenge considering the fact that all eight stadiums hosting the event are within a 20-mile radius of the city center. We at Qatar Computing Research Institute (QCRI) are working with the local transport authorities to model the traffic demands and plan a traffic control policy targeted at the event. In particular, we have developed: (a) a parallel, congestion-optimized, traffic microsimulator to analyze various what-if scenarios such as closure of a road segment, (b) a prediction model based on Graph Convolutional Networks for traffic in-flow estimation at a traffic light junction, and (c) a multi-agent reinforcement learning framework based on RLlib to design a coordinated traffic light control policy. We will present a brief overview of our work and walk through a demonstration of how we incorporated RLlib to speed up multi-agent learning for coordinated traffic light control.
Dr. Mayuresh Kunjir is a postdoctoral researcher at Qatar Center for AI (QCAI), a research group within Qatar Computing Research Institute (QCRI), Doha, Qatar. His research includes developing ML-driven solutions to urban mobility problems. Before joining QCRI, Mayuresh received his doctorate degree in computer science from Duke University in 2019. His doctoral research involved understanding and improving memory management in large-scale data analytics clusters.
Ji Lucas is a software specialist at Qatar Center for AI (QCAI) who likes to translate research output into high-quality prototypes that can be consumed by end users across many domains, including traffic management and air cargo. Ji is passionate about ensuring that scientific discovery gets delivered into concrete ML/AI products.
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