CAREER: Transportation Network Maintenance under Climate Change, Resource Uncertainties, and Connectivity

职业：气候变化、资源不确定性和连通性下的交通网络维护

• 批准号: 2238051
• 负责人: Kakan Dey
• 金额: $ 54万
• 依托单位: West Virginia University Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2028-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2238051&HistoricalAwards=false
• 关键词: CAREER; Transportation; Network; Maintenance; under

This Early Faculty Career Development (CAREER) project aims to improve transportation network maintenance with a focus on the growing severity and frequency of natural disasters due to climate change, resource uncertainties, and emerging data sources and implement an education program for next-generation transportation engineers, high school students, and the existing workforce with a focus on holistic thinking from a multidisciplinary perspective. Natural disasters substantially compromise regional transportation network’s mobility performance, reduce economic productivity, and create significant safety hazards to travelers. Transportation agencies in the United States spend more than $2.3 billion annually for winter roadway maintenance alone. Systematic considerations of the uncertainty of long-term regional weather patterns due to climate change, maintenance resources, and new data sources can significantly improve the resilience of a resource-intensive transportation network maintenance. This project will lay the groundwork for transforming traditional static transportation network maintenance operations into the next-generation data-driven and dynamic program that will reduce transportation infrastructure maintenance time and costs and minimize adverse societal impacts. The education program will train next-generation transportation professionals and the existing workforce and foster school students’ interest in science, technology, engineering, and mathematics.The goal of this project is to advance the scientific discovery in transportation network maintenance operations under growing climate change-induced adverse weather risks and uncertainties for the formulation of a robust, efficient, flexible, and reliable maintenance program. Advanced data-driven and machine learning-based emerging network modeling and analysis techniques will be developed to advance the understanding of the interrelationship between transportation networks and maintenance demand uncertainties in a regional roadway network. This project will formulate and solve scientific problems critical for (i) understanding the dependency of different uncertainties in optimal maintenance activity center configuration at a regional scale, (ii) modeling the roadway condition observation location to improve the forecast of the transportation network condition and traffic pattern for timely and effective roadway maintenance, (iii) formulating the maintenance operations considering weather and resource uncertainties in a data-intensive environment, and (iv) characterizing the all-electric maintenance operations and charging infrastructure. The education plan will apply the multidisciplinary principles of engineering problem-solving in traditional civil/transportation engineering courses; prepare next-generation engineers through multidisciplinary research and education activities; develop and execute educational activities for K-12 to increase awareness about science, technology, engineering, and mathematics disciplines and careers; and execute outreach activities for dissemination of research findings.This project is jointly funded by the Civil Infrastructure Systems (CIS) program and the Established Program to Stimulate Competitive Research (EPSCoR).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.

这一早期教师职业发展(CALEAR)项目旨在改善交通网络维护，重点关注气候变化、资源不确定性和新兴数据源造成的日益严重和频繁的自然灾害，并实施针对下一代交通工程师、高中生和现有劳动力的教育计划，从多学科的角度关注整体思维。自然灾害在很大程度上损害了区域交通网络的交通性能，降低了经济生产率，并给旅行者带来了重大的安全隐患。美国的交通机构每年仅在冬季道路维护上就花费超过23亿美元。系统地考虑气候变化、维护资源和新的数据源导致的长期区域天气模式的不确定性，可以显著提高资源密集型交通网络维护的弹性。该项目将为将传统的静态交通网络维护操作转变为下一代数据驱动的动态计划奠定基础，该计划将减少交通基础设施维护时间和成本，并将不利的社会影响降至最低。该教育计划将培养下一代交通运输专业人员和现有劳动力，培养在校学生对科学、技术、工程和数学的兴趣。该项目的目标是在气候变化引发的不利天气风险和不确定性日益增加的情况下，推进交通网络维护作业的科学发现，以制定稳健、高效、灵活和可靠的维护计划。将开发先进的数据驱动和基于机器学习的新兴网络建模和分析技术，以促进对区域公路网中运输网络和维护需求不确定性之间的相互关系的理解。该项目将制定和解决以下关键科学问题：(I)了解不同不确定性在区域范围内最优维护活动中心配置中的相关性；(Ii)对道路状况观测位置进行建模，以改进对交通网络状况和交通模式的预测，以便及时有效地进行道路维护；(Iii)在数据密集型环境中制定考虑天气和资源不确定性的维护操作；以及(Iv)描述全电动维护操作和充电基础设施。该教育计划将在传统的土木工程/运输工程课程中应用解决工程问题的多学科原则；通过多学科研究和教育活动培养下一代工程师；开发和执行K-12教育活动以提高对科学、技术、工程和数学学科和职业的认识；并开展推广活动以传播研究成果。该项目由民用基础设施系统(CIS)计划和已建立的刺激竞争研究计划(EPSCoR)共同资助。该奖项反映了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。