Collaborative Research: Bias Modeling and Estimation of Networked Transportation Data

该奖项将通过推动应用于交通系统的数据分析，为国家繁荣和经济福利做出贡献。虽然大数据越来越多地被用于交通运输以及科学和工程的其他学科，但收集的数据可能会出现错误和偏见，因此可能无法真实地代表整个人口。有偏见的数据可能会导致与交通基础设施相关的投资、规划和运营的政策无效和次优决策。随着交通领域向智能和自主系统的重大转变，数据质量对于确保这些投资产生公益效益至关重要。该奖项支持对数据偏差的来源、分类和建模方法的全面调查和基本了解，PI将开发新的解决方案来解决这些问题。项目团队将与运输从业者密切合作，测试和验证这项研究的结果，并将科学知识转化为规划和运营实践。该奖项还支持通过更新课程和虚拟研讨会扩大STEM对工程和数据科学的兴趣，并为代表性不足的社区提供机会。这项研究将开发基于网络的新型数据辅助运输分析(NetData)框架的理论、模型和算法，用于数据偏差建模和估计，该框架可以识别和利用数据中的底层网络结构和过程。NetData框架将明确捕获偏差，并将数据与适当的网络模型集成在一起，无论是在确定性和随机性环境中，还是在现实的网络考虑因素(如动态和多模式网络)下。这项研究填补了交通数据科学和实践中在建模和解决数据偏差方面的一个重要空白。该分析框架利用并扩展了交通网络科学、随机优化和数据科学的最新技术。它将产生算法来整合来自多个来源的数据，以帮助对旅行模式和决策进行更准确和可靠的分析。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award will contribute to national prosperity and economic welfare by advancing data analytics applied to transportation systems. While big data are increasingly used in transportation and other disciplines in science and engineering, the collected data may come with errors and biases, and therefore may not provide an authentic representation of the entire population. Biased data can lead to ineffectual policies and suboptimal decisions for transportation infrastructure related investment, planning, and operations. As the transportation field undergoes a major transformation towards smart and autonomous systems, data quality is critical in ensuring that public welfare results from these investments. This award supports a comprehensive investigation and fundamental understanding of the sources, taxonomy, and modeling approaches of data biases and the PIs will develop novel solutions to address the issues. The project team will work closely with transportation practitioners to test and validate the findings of this research, and transfer scientific knowledge to planning and operation practices. The award also supports efforts to broaden STEM interest in engineering and data sciences through updated curricula and virtual seminars, and to provide opportunities for underrepresented communities.This research will develop theories, models, and algorithms of a novel NETwork-based, Data-Assisted Transportation Analysis (NetData) framework for data bias modeling and estimation, which can recognize and utilize the underlying network structure and processes in the data. The NetData framework will explicitly capture bias and integrate data with proper network models, in both deterministic and stochastic settings and under realistic network considerations such as dynamic and multimodal networks. This research fills an important gap in transportation data sciences and practice in modeling and addressing data bias. The analytical framework leverages and extends state-of-the-art techniques from transportation network science, stochastic optimization, and data science. It will produce algorithms to integrate data from multiple sources to help conduct more accurate and reliable analysis of travel patterns and decisions.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.