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

职业生涯：通过无源传感器和劳动力多元化实现公正的远程货运规划

• 批准号: 2042870
• 负责人: Sarah Hernandez
• 金额: $ 51.46万
• 依托单位: University of Arkansas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2042870&HistoricalAwards=false
• 关键词: CAREER; Towards; Unbiased; Long; Range

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.

这项学院早期职业发展(Career)补助金将以所需的分辨率生成货运流动数据，以做出有关长期交通基础设施投资和政策的知情、数据驱动的决策。这些决定影响到美国公民和货运业的健康、安全和繁荣。自20世纪90年代开始在全国范围内进行发货调查以来，尽管货运业务和供应链日益复杂，但公共机构收集商品流动数据的方式几乎没有改变。随着2017年联邦政府对电子航海日志的要求以及全球定位系统的广泛使用，重新想象货运数据的收集方式具有巨大的潜力。这笔赠款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物)中得出无偏见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者能够推进20-40年的货运预测模型，并为关键的行业问题制定解决方案，如司机短缺、服务小时数法规以及缺乏安全和可用的停车。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行者的独特需求至关重要。创新的服务学习教育计划被整合到该项目中，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，使交通机构能够利用越来越多的被动收集的货运数据样本，及时、公正地做出关于基础设施投资、环境政策和经济发展的决策。这项研究将：1)确定从被动收集的移动传感器数据得出的活动模式在多大程度上准确地预测商品运输；2)确定从道路交通传感器得出的车身特征在多大程度上预测商品运输；3)建立和验证被动收集的货物移动数据的偏差检测和质量测量。该项目将促进妇女最初的参与和持续的职业满意度，以帮助缩小性别差距，并确保在运输规划过程中经常纳入不同的观点。这项三层计划在年度专业会议期间实施培训员培训课程，在这些会议上，大学生(第1级)教授实习交通工程师(第2级)如何提供以交通传感器为主题的K-12(第3级)外展。该项目的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、更具全球竞争力的STEM劳动力，以及3)加强学术界和专业组织之间的伙伴关系，支持NSF的社会成果。该项目由民用基础设施系统(CIS)计划和既定的激励竞争研究计划(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。

项目成果

Representative truck activity patterns from anonymous mobile sensor data

• DOI: 10.1016/j.ijtst.2022.05.002
• 发表时间: 2022-05
• 期刊: International Journal of Transportation Science and Technology
• 作者: T. Akter;S. Hernandez

Freight Operational Characteristics Mined from Anonymous Mobile Sensor Data

从匿名移动传感器数据中挖掘的货运运营特征

• DOI: 10.1177/03611981231158639
• 发表时间: 2023
• 期刊: Transportation Research Record: Journal of the Transportation Research Board
• 作者: Akter, Taslima;Hernandez, Sarah;Camargo, Pedro V.
• 通讯作者: Camargo, Pedro V.