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.

该小企业创新研究 (SBIR) 第二阶段项目的更广泛影响侧重于自适应交通信号计时解决方案。全球各大城市每年花费超过 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。交通信号计时不合时宜的后果是：a) 由于拥堵而导致生产力损失增加，美国人平均每年在交通中花费 97 个小时；b) 交通事故增加，美国道路上每 15 分钟就有 1 人死亡；c ）温室气体排放量增加，占道路上车辆排放总量的三分之一。该项目将支持基于网络的技术的开发和商业化，以支持城市和直辖市的交通管理者利用人工智能（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，该系统还将显着减少有害排放，从而打造更加环保的城市街道。该技术在美国和欧洲的可用市场（这两个市场合计占全球信号定时市场的 60%）代表着 24 亿美元的机会。 该小型企业创新研究第二阶段项目旨在开发完全自适应交通信号重新定时解决方案的概念验证，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优势将是开发可以大规模运行并稳健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）使解决方案稳健； 3) 确保系统可供用户使用。实现这些目标可能有助于确保产品能够在经济地部署在云上的大数据架构上成功运行。 该解决方案还将提供对人机交互的更深入的理解。总体而言，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此类信号授时策略。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。