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

SBIR第一阶段：信息融合驱动的自适应走廊交通信号重新定时

• 批准号: 1914219
• 负责人: Vishal Mahulkar
• 金额: $ 22.47万
• 依托单位: ETALYC, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1914219&HistoricalAwards=false
• 关键词: SBIR; Phase; Information; fusion; driven

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result from a significant reduction in traffic delays, crashes, and fatalities by implementing a fully adaptive traffic signal re-timing solution. The most recent National Traffic Signal Report Card gave a failing grade of D+ to traffic signal operations in the United States. These failing grades are despite the fact that agencies spend approximately $2 billion every year on signal operation, maintenance, and capital improvements. If the US supports its signals at an "A" level, the public would see: (i) a 15-40% reduction in traffic delay, travel time savings up to 25%, and a 10-40 % reductions in stops; (ii) a 10 % or more reduction in fuel consumption resulting in nationwide savings of almost 170 billion gallons of motor fuels per year; and (iii) up to 22% reduction in harmful emissions. From a science and technology perspective, this effort will be an impactful success story for artificial intelligence and machine learning. As the small business is a product of Iowa State University start-up factory, the project is expected to involve students looking for industry experiences in the project leading to a more comprehensive education for them. This Small Business Innovation Research (SBIR) Phase I project will develop and demonstrate proof-of-concept of a fully adaptive traffic signal re-timing solution. The key intellectual merit of this effort will be developing deep learning models to extract abstract features from a range of heterogeneous information sources to perform feature-level fusion. Upon feature extraction, the proposed solution will use scalable deep reinforcement learning models to obtain re-timing decisions. The reinforcement learning process will help the system adapt to changing traffic scenarios at different time-scales without the need for significant manual interventions. The solution will be flexible for both onboard and cloud-based computing, depending on the availability of such platforms. Overall, the proposed system will reduce implementation time and capital and maintenance expenditures. These advantages will encourage cities around the US and internationally to adopt such a re-timing strategy and will dramatically transform the current landscape of this market.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) 第一阶段项目的更广泛影响/商业潜力将通过实施完全自适应交通信号重新定时解决方案来显着减少交通延误、碰撞和死亡人数。最新的国家交通信号报告卡将美国的交通信号运营评为 D+ 级。尽管各机构每年在信号运营、维护和资本改善方面花费约 20 亿美元，但这些成绩仍然不及格。如果美国支持其“A”级信号，公众将看到：(i) 交通延误减少 15-40%，出行时间节省高达 25%，停靠站减少 10-40%； (ii) 燃料消耗减少 10% 或更多，从而在全国范围内每年节省近 1700 亿加仑的汽车燃料； (iii) 有害排放量减少高达 22%。从科学技术的角度来看，这一努力将成为人工智能和机器学习领域一个有影响力的成功故事。由于该小型企业是爱荷华州立大学初创工厂的产物，因此该项目预计会让学生在该项目中寻找行业经验，从而为他们提供更全面的教育。该小型企业创新研究 (SBIR) 第一阶段项目将开发并演示完全自适应交通信号重新定时解决方案的概念验证。这项工作的关键智力优势将是开发深度学习模型，从一系列异构信息源中提取抽象特征，以执行特征级融合。在提取特征后，所提出的解决方案将使用可扩展的深度强化学习模型来获得重新定时决策。强化学习过程将帮助系统适应不同时间尺度不断变化的交通场景，而无需大量的人工干预。该解决方案对于机载计算和基于云的计算都很灵活，具体取决于此类平台的可用性。总体而言，拟议的系统将减少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取这种重新计时策略，并将极大地改变该市场的当前格局。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。