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

随着城市化的快速发展，空气污染已成为世界各地城市迫切的环境和社会问题，特别是由于道路附近的车辆直接排放的污染物。这一问题对各种弱势群体的健康和福祉构成重大威胁，包括儿童、老年人和先前存在疾病的个人，他们由于靠近主要道路和交通繁忙地区而面临更大的风险。因此，至关重要的是要监测空气质量，并在道路附近管制交通，以有效地解决污染问题，并促进这些受影响的个人的福利。管理交通和缓解这些挑战的一个潜在解决方案是实施交通信号控制系统，该系统在减少拥堵，最大限度地减少车辆排放，并最终提高附近居民和通勤者的整体生活质量方面具有良好的前景。受此实际情况的启发，本项目的主要目标是开发一种创新的交通信号控制方法，可以有效地减轻道路附近空气污染的不利影响。 该项目旨在通过将创新的基于机器学习的传感和控制技术与社会需求规范相结合，弥合网络物理系统与社会意识之间的关键差距。主要重点是为交通信号控制系统开发一个综合的、社会知情的框架，该框架包括三个主要任务，包括（1）开发用于近路空气质量传感的低成本移动的空气质量传感系统和用于交通传感的时空图扩散学习模型，（2）设计一种融合社会需求规范和交通辅助监管的基于强化学习的控制模型，以及（3）实现基于真实世界数据以及交通与空气质量之间的相关性的模拟工具。该项目的成功具有重大的社会和技术意义，因为它有可能提高道路附近的空气质量，改善附近居民和旅行者的生活质量。此外，全面的能力建设，教育和推广活动将提供支持，以代表性不足的少数民族在计算和促进研究技能的发展和未来的职业生涯中的数据密集型field.This奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

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.