Decision-Embedded Deep Learning for Transit Systems

该奖项支持研究深度学习对决策的影响，以及开发一种新型的学习范式，将决策模型直接集成到深度学习模型设计中，并明确关注交通系统应用。它旨在帮助公交决策者了解深度学习模型对其决策结果的影响，例如公交时刻表设计和公交车运动控制。它还旨在为交通研究人员和从业者提供一个新的框架，用于开发在正常和对抗中具有可验证决策质量的深度学习模型（例如，恶意网络攻击、传感器故障和极端天气条件）场景。该项目的成果将在基础方法和民用基础设施应用方面开辟新的研究领域。该项目将为本科生和研究生提供跨学科教育和研究培训机会以及新的深度学习相关课程。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，以产生关于基于学习的预测与交通决策之间相互作用的新知识。通过本研究项目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大小和确定适当的模型架构，以优化运输决策质量。将创建一个新的深度学习范式，以克服现有工作的局限性，从而确保交通系统的最佳决策结果。这种研究范式旨在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方向发展。这一范例不仅可以使交通系统受益，还可以改变许多其他基础设施系统，如配电和通信，这些系统目前在很大程度上是孤立的参数预测和决策。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。

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.