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

SBIR第二阶段：信息融合驱动的自适应走廊交通信号重新定时

• 批准号: 2052257
• 负责人: Tingting Huang
• 金额: $ 96.38万
• 依托单位: ETALYC, Inc.
• 依托单位国家: 美国
• 项目类别: Cooperative Agreement
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2052257&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Information; fusion

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.

这项小型企业创新研究（SBIR）II期项目的更广泛影响着重于自适应交通信号正时解决方案。全世界的城市和市政当局每年花费超过40亿美元来保持交通信号，但通常无法充分减少道路上的拥塞。流量信号时机误差的后果是：a）由于拥堵的损失增加，因为美国平均每年停留97个小时，b）由于交通量增加了事故，每15分钟死亡，在美国道路上每15分钟死亡，c）增加了温室排放，而在道路上，车辆造成的所有排放量的三分之一。该项目将支持基于Web的技术的开发和商业化，以支持城市和市政当局的交通经理，以使用人工智能（AI）和大数据分析更好地管理流量。除了改善交通流量和减少交通拥堵外，该系统还将大大减少有害排放，从而导致更环保的城市街道。这项技术在美国和欧洲的可维修市场占全球信号定时市场的60％，代表了24亿美元的机会。 这个小型企业创新研究阶段II阶段项目旨在为完全自适应的交通信号重述解决方案开发概念验证，该解决方案可以坚固地处理多个信号走廊以进行商业化。这项工作的关键知识优点将是开发可以大规模运行并稳健处理传感器噪声的深度学习模型。强化学习过程将有助于系统适应不同规模的不断变化的交通情况，而无需手动干预。在第二阶段必须克服的研究目标集中在：1）扩展解决方案； 2）使解决方案稳健； 3）确保系统准备就绪。实现这些目标可能有助于确保产品能够成功地在经济地部署在云上的大数据架构上。 该解决方案还将提供对人机相互作用的更深入的理解。总体而言，拟议的系统可能会减少实施时间以及信号正时系统的资本和维护支出。这些优势将鼓励美国和国际上的城市采用这种信号定时策略。该奖项反映了NSF的法定任务，并被认为是使用基金会的知识分子优点和更广泛的影响评估标准的评估值得支持的。