Characterizing and Mitigating Community-level Compound Algorithmic Biases for Data-driven Road Infrastructure Decision-making

Efficient road infrastructure maintenance, ranging from fixing cracks/potholes to repairing traffic signs, is critical to increasing ride comfort and traffic safety and reducing operation costs. In recent years, government agencies increasingly rely on the open e-government systems to acquire real-time data of road infrastructure conditions from community residents, due to their low-cost, timely, and transparent natures, for optimizing maintenance strategies. These data are often multi-modal and shared by people in active or passive manner: (1) in active e-government platforms, community residents can directly call in or send request via websites to report observed road infrastructure problems (e.g., 311 or City Connect programs), or (2) people can install mobile software on individual smart devices that automatically collect and share data in the back end while moving around the neighborhood. Such multi-modal human-centric crowdsensing data are further analyzed and utilized to train statistical or machine learning models for estimating road infrastructure conditions and commonly used to help the government agencies optimize allocations of maintenance resources. However, a major challenge is the sensing biases in the data that can cause inefficient maintenance decision-making. Previous studies indicate that community status would significantly impact the levels of civic engagement in multi-modal e-government systems. The disparities in community status may lead to distinct qualities of crowdsensed data and thus introduce sensing biases to the data (e.g., under-reporting and over-reporting). These sensing biases further activate and amplify the inherent algorithmic bias in data-driven infrastructure modeling, leading to under-estimation of the urgency and severity of maintenance demands. The biased estimations of road infrastructure conditions will finally lead to inefficient road maintenance service. Existing studies mainly focus on understanding the engagement in single-mode e-government system or addressing algorithmic biases regardless of sensing biases, but none of them have yet to quantitatively reason the generation and propagation of the coupled social-algorithmic biases in multi-modal e-government systems and mitigate them jointly. This project aims to reason and mitigate community-level compound social-algorithmic biases generation and propagation in human-centric e-government systems for promoting efficient road infrastructure maintenance decision-making.

Language

• English

Project

• Status: Active
• Funding: $66,932.00
• Contract Numbers:

  69A3552348321

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Florida A&M University, Tallahassee

  404 Foote/Hilyer
  Tallahassee, FL  United States  32307
• Project Managers:

  Moses, Ren

• Performing Organizations:

  Florida A&M University, Tallahassee

  404 Foote/Hilyer
  Tallahassee, FL  United States  32307

  Stony Brook University

  100 Nicolls Road
  Stony Brook, NY  United States  11794
• Principal Investigators:

  Yazici, Anil

  Xu, Susu

  Qin, Ruwen

• Start Date: 20240601
• Expected Completion Date: 20251231
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Bias; Crowdsourcing; Decision making; Highway maintenance; Infrastructure
• Subject Areas: Data and Information Technology; Highways; Maintenance and Preservation; Society;

Filing Info

• Accession Number: 01955523
• Record Type: Research project
• Source Agency: Rural Safe, Efficient, and Advanced Transportation Center
• Contract Numbers: 69A3552348321
• Files: UTC, RIP
• Created Date: May 21 2025 12:51PM