Collaborative Research: CISE-MSI: RCBP-RF: CPS: Socially Informed Traffic Signal Control for Improving Near Roadway Air Quality

点击翻译按钮获取中文摘要

With the rapid pace of urbanization, air pollution has emerged as a pressing environmental and societal concern in cities worldwide, particularly due to pollutants directly emitted from vehicles near roadways. This issue poses a significant threat to the health and well-being of various vulnerable groups, including children, the elderly, and individuals with pre-existing conditions, who face heightened risks due to their proximity to major roadways and high-traffic areas. Therefore, it is crucial to monitor air quality and regulate traffic in close proximity to roadways to effectively address pollution and promote the welfare of these affected individuals. One potential solution to manage traffic and alleviate these challenges is the implementation of traffic signal control systems, which offer promising prospects in reducing congestion, minimizing vehicle emissions, and ultimately enhancing the overall quality of life for residents and commuters in the vicinity. Inspired by this practical scenario, the primary objective of this project is to develop an innovative traffic signal control approach that can effectively mitigate the adverse impacts of air pollution near roadways. This project aims to bridge the critical gap between cyber-physical systems and social awareness by integrating innovative machine-learning-based sensing and control techniques with social requirement specifications. The primary focus is on developing an integrated, socially informed framework for traffic signal control systems, which encompasses three main tasks, including (1) developing a low-cost mobile air quality sensing system for near-road air quality sensing and a spatial-temporal graph diffusion learning model for traffic sensing, (2) designing a reinforcement learning-based control model incorporating social requirement specification and traffic-aided supervision, and (3) implementing a simulation tool based on real-world data and the correlation between traffic and air quality. The success of this project holds significant social and technological implications, as it has the potential to enhance air quality near roadways and improve the quality of life for nearby residents and travelers. Furthermore, comprehensive capacity-building, education, and outreach activities will provide support to underrepresented minorities in computing and foster the development of research skills and future careers in data intensive fields.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.