SBIR Phase II: Information fusion-driven adaptive corridor-wide traffic signal re-timing

该小型企业创新研究（SBIR）第二阶段项目的更广泛影响集中在自适应交通信号配时解决方案上。世界各地的城市和市政当局每年花费超过40亿美元来重新调整交通信号，但往往无法充分减少道路拥堵。交通信号配时不当的后果是：a）由于拥堵而增加的生产力损失，美国人平均每年在交通中花费97小时，B）由于交通而增加的事故，在美国道路上每15分钟就有一人死亡，以及c）增加温室气体排放，道路上的车辆造成的所有排放量的三分之一。该项目将支持基于网络的技术的开发和商业化，以支持城市和市政当局的交通管理人员使用人工智能（AI）和大数据分析更好地管理交通。除了改善交通流量和减少拥堵外，该系统还将大大减少有害排放，从而使城市街道更加环保。美国和欧洲的这项技术的可服务市场占全球信号定时市场的60%，代表了24亿美元的机会。 该小型企业创新研究第二阶段项目旨在为完全自适应的交通信号重新定时解决方案开发概念验证，该解决方案可以稳健地处理多个信号走廊以实现商业化。这项工作的关键智力价值将是开发可以大规模运行并鲁棒地处理传感器噪声的深度学习模型。强化学习过程将帮助系统适应不同规模的不断变化的交通场景，而无需人工干预。第二阶段必须克服的研究目标集中在：1）扩展解决方案; 2）使解决方案健壮; 3）确保系统为用户准备好。实现这些目标可能有助于确保产品能够成功地在云上经济地部署的大数据架构上运行。 该解决方案还将提供对人机交互的更深入理解。总的来说，所提出的系统可以减少信号定时系统的实施时间以及资本和维护支出。这些优势将鼓励美国和国际上的城市采用这种信号配时策略。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact of this Small Business Innovation Research (SBIR) Phase II project focuses on an adaptive traffic signal timing solution. Cities and municipalities worldwide spend over $4 billion annually to retime traffic signals and yet often fail to adequately reduce congestion on roadways. The consequences of mistimed traffic signal timing are: a) increasing productivity losses due to congestion with the average American spending 97 hours stuck in traffic every year, b) increasing accidents due to traffic, with one fatality every 15 minutes on US roads, and c) increasing greenhouse emissions with a third of all emissions caused by vehicles on the roads. This project will support the development and commercialization of a web-based technology to support traffic managers in cities and municipalities to better manage traffic using artificial intelligence (AI) and big data analytics. In addition to improving traffic flow and reducing congestion, the system will also significantly reduce harmful emissions, leading to more environmentally friendly city streets. The serviceable markets for this technology in the US and Europe, which together constitute 60% of the global signal-timing market, represent a $2.4 billion opportunity. This Small Business Innovation Research Phase II project seeks to develop a proof-of-concept for a fully adaptive traffic signal retiming solution that can robustly handle multiple signal corridors for commercialization. The key intellectual merit of this effort will be developing deep learning models that can run at scale and handle sensor noise robustly. The reinforcement learning process will help the system to adapt to changing traffic scenarios at different scales without the need for manual interventions. Research objectives that must be overcome in Phase II are focused on: 1) scaling the solution; 2) making the solution robust; and 3) ensuring that the system is user ready. Achieving these objectives may help ensure the product can successfully run on big-data architecture economically deployed on the cloud. The solution will also provide a deeper understanding of human-machine interaction. Overall, the proposed system may reduce implementation time as well as capital and maintenance expenditures for signal timing systems. These advantages will encourage cities around the US and internationally to adopt such signal timing strategies.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.