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1. Clustering Partner Agencies for a Local Food Bank

   Ivuawuogu, H.; Jiang, S.; Davis, L. (May 2023, Proceedings of 2023 Industrial and Systems Engineering Annual Conference)

   Babski-Reeves, K.; Eksioglu, B.; Hampton, D. (Ed.)

   Food insecurity is a serious problem in America and the pandemic makes the problem even worse. Feeding America has more than 200 food banks. that These food banks and their partner agencies are the key players in the battle against food insecurity. Partner agencies may vary in size and location depending on the service area and the variety of the partner agencies and the complexities of their operations make equitable food distribution very challenging. There is a need for a meaningful to group those partner agencies to assist food bank operations managers to make informed decisions. This study uses data from a local food bank and its partner agencies. Each agency is unique in terms of its behavior. Therefore, k-means clustering was used to categorize agencies into groups based on the number of persons served and the amount of food received. The results of the study will provide evidence-based information to assist the food bank in making informed decisions. 

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2. Predictive Model for Partner Agencies Dependency on Food Banks

   https://doi.org/10.54941/ahfe1005573  

   Ivuawuogu, Henry; Jiang, Steven; Davis, Lauren; Hamilton, Mikaya (December 2024, AHFE International)

   Ahram, Tareq; Karwowski, Waldemar (Ed.)

   In the quest for equitable resource distribution within food banks and their partner agencies, understanding the dependencies of these agencies on food banks emerges as a critical factor. This study investigates the intricate dynamics influencing agency dependency ratios, exploring the complex factors that shape the demand for food resources. Leveraging historical self-reported dependency ratio data, this preliminary study employs predictive modeling using Multiple Linear Regression to forecast agency dependencies on food banks. The primary objective is to discern the underlying factors that significantly impact agency dependency ratios. Employing Least Absolute Shrinkage and Selection Operator (LASSO) as a feature selection technique, the study identifies the key variables that capture the essence of the dataset. Identifying the variables that contribute the most to the model paves the way for robust predictive modeling. This study offers a comprehensive approach to understanding and predicting agency dependencies on food banks. The findings hold significant implications for non-profit hunger relief organizations, aiding in strategic decision- making for equitable resource distribution. 

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3. Improving Data Quality of Automated Pavement Condition Data Collection: Summary of State of the Practices of Transportation Agencies and Views of Professionals

   https://doi.org/10.1061/JPEODX.0000392  

   Luo, Xiaohua; Gong, Haitao; Tao, Jueqiang; Wang, Feng; Minifie, Jana; Qiu, Xin (September 2022, Journal of Transportation Engineering, Part B: Pavements)
4. Improving Data Quality of Automated Pavement Condition Data Collection: Summary of State of the Practices of Transportation Agencies and Views of Professionals.

   Luo, X; Gong, H; Tao, J; Wang, F; Minifie, J (July 2025, Journal of transportation engineering Part B Pavements)
5. Improving Data Quality of Automated Pavement Condition Data Collection: Summary of State of the Practices of Transportation Agencies and Views of Professionals

   Luo, X; Gong, H; Tao, J; Wang, F; Minifie, J (January 2022, Journal of transportation engineering)

   Automated or semi-automated pavement condition data collection is replacing manual data collection in many state and local highway agencies due to its advantages of reducing labor, time, and cost. However, the practical experience of highway agencies indicates that there are still data quality issues with the pavement condition data collected using existing image and sensor-based data collection technologies. This study aims to investigate the implementation experiences and issues of automated or semi-automated pavement condition surveys. An online questionnaire survey was conducted, along with scheduled virtual/phone interviews to gather information from government, industry, and academia about the state of the practice and state of the art. Open questions about the data quality and quality control & quality assurance (QC/QA) were used to receive first-hand inputs from highway agencies and pavement experts. The study has compiled the following observations: (1) Highway agencies urgently need a uniform data collection protocol for automated data collection; (2) the current QA requires too much human intervention; (3) cost ($100–$200 per mile) is a significant burden for state and local agencies; (4) the main issues regarding data quality are data inconsistencies and discrepancies; (5) agencies expect a greater accuracy once the image processing algorithms are improved using artificial intelligence technologies; and (6) existing automated data collection methods are not available for project-level data collection. 

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