CAREER: Towards Unbiased Long-Range Freight Planning Through Passive-Sensors and Workforce Diversity

这项教师早期职业发展（Career）资助将产生货运数据，以做出有关长期运输基础设施投资和政策的明智、数据驱动的决策所需的解决方案。这些决定影响到美国公民和货运行业的健康、安全和繁荣。自上世纪90年代开始进行全国货运调查以来，尽管货运业务和供应链日益复杂，但公共机构收集商品流量数据的方式几乎没有改变。随着2017年联邦政府对电子日志的要求和全球定位系统的广泛使用，重新构想货运数据的收集方式具有巨大的潜力。这项资助的研究目标是从被动收集的匿名货运数据（特别是卡车和水运货运）中获得无偏的空间和时间连续的商品和行业信息。这项工作将使研究人员和从业人员能够推进20-40年货运运动的预测模型，并制定解决关键行业问题的方案，如司机短缺、服务时间规定、缺乏安全和可用的停车位。此外，运输劳动力的多样性对于确保投资和基础设施决策反映不同旅客的独特需求至关重要。该项目纳入了一项创新的服务学习教育计划，以提高女性交通专业人员和学生的就业吸引力和保留率。这项研究将产生积极的社会影响，使运输机构能够利用越来越多的被动收集的货运数据样本，在基础设施投资、环境政策和经济发展方面做出及时、公正的决策。该研究将：1)确定从被动收集的移动传感器数据中获得的活动模式准确预测商品携带的程度；2)确定从道路交通传感器获得的车身特征对所载商品的预测程度；3)针对被动采集的货运数据，建立并验证偏差检测和质量措施。该项目将促进妇女的初始参与和持续的职业满意度，以帮助缩小性别差距，并确保在交通规划过程中定期纳入不同的观点。这个三层计划在年度专业会议期间实施培训师培训课程，由大学生（第一层）教授实习交通工程师（第二层）如何提供以交通传感器为主题的K-12（第三层）外展。该项目更广泛的教育影响通过促进：1)女性充分参与STEM， 2)发展更多样化，具有全球竞争力的STEM劳动力，以及3)增加学术界和专业组织之间的合作伙伴关系，支持国家科学基金会的社会成果。该项目由民用基础设施系统（CIS）计划和促进竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，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 Faculty Early Career Development (CAREER) grant will produce freight-goods movement data at a resolution needed to make informed, data-driven, decisions about long-range transportation infrastructure investments and policies. Such decisions affect the health, safety, and prosperity of US citizens and the freight transportation industry. Since the inception of nationwide shipment surveys in the 1990s, little has changed in how public agencies collect commodity flow data, despite the increasing complexity of freight operations and supply chains. With the 2017 federal mandates for electronic logbooks and widespread use of Global Positioning Systems, there is tremendous potential to reimagine how freight data is collected. The research objective of this grant is to derive unbiased spatial and temporally-continuous commodity and industry information from passively collected, anonymized freight movement data (specifically for truck and waterborne freight). The work will enable researchers and practitioners to advance 20-40 year forecast models of freight movement, as well as formulate solutions to critical industry issues such as driver shortages, Hours-of-Service regulations, and lack of safe and available parking. Additionally, diversity in the transportation workforce is critical for ensuring that investment and infrastructure decisions reflect the unique needs of diverse travelers. An innovative service-learning education plan is integrated into the project to improve job attraction and retention rates of female transportation professionals and students.This research will yield positive societal impacts by enabling transportation agencies to leverage increasingly available samples of passively collected freight movement data for timely, unbiased decision-making regarding infrastructure investment, environmental policy, and economic development. The research will: 1) determine the extent to which activity patterns derived from passively collected mobile sensor data accurately predict commodity carried; 2) identify the extent to which vehicle body characteristics derived from roadway traffic sensors predict commodity carried; 3) establish and validate bias detection and quality measures for passively collected freight movement data. The project will promote women’s initial engagement and ongoing career satisfaction to help close the gender gap and ensure that diverse perspectives are routinely included in transportation planning processes. The three-tiered plan implements train-the-trainer sessions during annual professional conferences where college students (tier 1) teach practicing transportation engineers (tier 2) how to deliver traffic sensor-themed K-12 (tier 3) outreach. The broader educational impacts of this project support NSF societal outcomes by promoting: 1) full participation of women in STEM, 2) development of a more diverse, globally competitive STEM workforce, and 3) increased partnerships between academia and professional organizations.The 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.