Decision-Embedded Deep Learning for Transit Systems

交通系统决策嵌入式深度学习

• 批准号: 2409847
• 负责人: Zhiwei Chen
• 金额: $ 43.26万
• 依托单位: Drexel University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2409847&HistoricalAwards=false
• 关键词: Decision; Embedded; Deep; Learning; Transit

This award support research that investigates the impacts of deep learning on decision-making and the development of a new type of learning paradigm where decision models are directly integrated into deep learning model design, with an explicit focus on transit system applications. It aims to help transit decision-makers understand the impacts of deep learning models on their decision-making outcomes such as transit timetable design and bus motion control. It also aims to offer transit researchers and practitioners a novel framework for developing deep learning models with verifiable decision quality in both normal and adversarial (e.g., malicious cyberattacks, sensor malfunctions, and extreme weather conditions) scenarios. The outcomes of this project will open new research areas in both fundamental methodologies and civil infrastructure applications. The project will offer interdisciplinary education and research training opportunities and new deep-learning-related courses to undergraduate and graduate students. It will also involve under-represented minority students at the secondary, undergraduate, and graduate levels through course projects, research assistantships and internship opportunities.This research project integrates deep learning theories with knowledge and methods from transportation engineering to generate new knowledge on the interplay between learning-based prediction and transit decision-making. Theoretical bounds identified through this research projct will assist transit agencies in designing deep learning models, such as choosing the right sample size and identifying the appropriate model architecture, to optimize transit decision-making quality. A new deep learning paradigm will be created to overcome the limitations of existing works, thereby assuring optimal decision-making outcomes in transit systems. This researched paradigm intends to leverage decision errors to update parameters in deep learning models so that they move toward optimal and reliable decisions directly. This paradigm could not only benefit transportation systems but also transform many other infrastructure systems such as power distribution and communications where parameter prediction and decision-making are currently largely siloed.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.

这项奖励研究研究研究了深度学习对决策的影响以及新型学习范式的开发，其中决策模型直接集成到深度学习模型设计中，并明确关注过境系统应用程序。它旨在帮助公交决策者了解深度学习模型对他们的决策结果的影响，例如公交时间表设计和公交车运动。它还旨在为运输研究人员和从业人员提供一个新颖的框架，用于开发具有正常和对抗性（例如恶意网络攻击，传感器故障和极端天气状况）的深度学习质量的深度学习模型。该项目的结果将在基本方法和民用基础设施应用中开放新的研究领域。该项目将为本科生和研究生提供跨学科的教育和研究培训机会以及与深度学习有关的新课程。它还将通过课程项目，研究助理职位和实习机会涉及二级，本科和研究生级别的代表性不足的少数族裔学生。该研究项目将深度学习理论与交通工程的知识和方法相结合，从运输工程中，以在学习基于学习的预测和交通决策之间建立有关相互作用的新知识。通过这项研究确定的理论界限将有助于运输机构设计深度学习模型，例如选择正确的样本量并确定适当的模型体系结构，以优化运输决策质量。将创建一个新的深度学习范式来克服现有作品的局限性，从而确保运输系统中的最佳决策结果。该研究的范式打算利用决策错误来更新深度学习模型中的参数，以便它们直接朝着最佳和可靠的决策迈进。该范式不仅可以使运输系统受益，而且可以改变许多其他基础设施系统，例如电源分配和参数预测和决策制定，目前在很大程度上被索洛。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查审查标准来通过评估来通过评估来获得支持的。