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

合作研究：CISE-MSI：RCBP-RF：CPS：用于改善附近道路空气质量的社会知情交通信号控制

• 批准号: 2318697
• 负责人: Yu Yang
• 金额: $ 19.78万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318697&HistoricalAwards=false
• 关键词: Collaborative; Research; CISE; MSI; RCBP

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.

随着城市化的迅速速度，空气污染已成为全球城市的紧迫环境和社会关注，特别是由于直接从道路附近的车辆中散发出的污染物。这个问题对各种弱势群体的健康和福祉构成了重大威胁，包括儿童，老年人和患有疾病的个人，由于其靠近主要的道路和高流量地区的风险，他们面临着更高的风险。因此，至关重要的是监测空气质量并在靠近道路附近的流量以有效解决污染并促进这些受影响个人的福利。管理交通和减轻这些挑战的一种潜在解决方案是实施交通信号控制系统，该系统为减少交通拥堵，最大程度地减少车辆排放以及最终增强了附近居民和通勤者的整体生活质量，提供了有希望的前景。受这种实际情况的启发，该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的感应和控制技术与社会需求规范整合到网络物理系统和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集成的，社会知情的框架，该框架包括三个主要任务，包括（1）开发一种低成本的移动空气质量传感系统，以近乎道路的空气质量传感和一个空间图形扩散学习模型，用于交通传感，（2）基于强化学习模型的模型，并实现了基于增强的控制模型，并实现了交通型号，并（3）构成了交通型和交通设备，（3），（2）（2）（3）现实世界数据以及流量与空气质量之间的相关性。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并改善附近居民和旅行者的生活质量。此外，全面的能力建设，教育和外展活动将为代表性不足的少数群体提供支持和促进数据密集型领域的研究技能和未来职业的发展。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子和更广泛影响的评估来评估的支持，并被认为是值得的。