Reinforcement Learning-Based Optimal Control of Wearable Alarms for Consistent Roadway Workers’ Reactions to Traffic Hazards

Details

• Creators:
  Lu, Daniel ; Ergan, Semiha ; Ozbay, Kaan
• Corporate Creators:
  Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC) ; New York University. Department of Civil and Urban Engineering
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  Calibration Driving Simulators Human Factors Microsimulation Road Crews Virtual Reality Warning Devices Work Zone Safety
• DOI:
  https://doi.org/10.1080/19439962.2024.2449119
• Resource Type:
  Manuscript
• Geographical Coverage:
  United States
• Corporate Publisher:
  Connected Communities for Smart Mobility Toward Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER) Tier-1 University Transportation Center (UTC)
• Abstract:
  Recent innovations in roadway construction include work zone intrusion alert (WZIA) systems that detect traffic hazards (e.g., speeding or intruding vehicles) and raise alarms (e.g., sounds, lights) using preset attributes (e.g., volume, duration) to warn human workers-on-foot. Designing alarms raised by wearable warning devices (e.g., smartwatch) for roadway workers remains an emerging area of transportation safety research. As roadway work zones begin to adopt these novel technologies, issues relating to the alarms may persist. Different individuals’ alarm preferences and alarm fatigue towards repeated exposure to constant alarm attributes can lead to decreases in worker vigilance and responsiveness to traffic hazard.
• Content Notes:
  This is the author’s version of a work that has been accepted for publication in the Journal of Transportation Safety & Security. The authors would like to acknowledge the funding agencies for this project: C2SMARTER funding under grant number (69A3551747119) and a 50% cost-share by New York University. Please cite as: Lu, D., Ergan, S., & Ozbay, K. (2025). Reinforcement learning-based optimal control of wearable alarms for consistent roadway workers’ reactions to traffic hazards. Journal of Transportation Safety & Security, 17(7), 757–781. https://doi.org/10.1080/19439962.2024.2449119
• Format:
  PDF
• Funding:
  69A3551747119
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:689870a62585b3d00ff290a370eeb604d7e109520127a1e64ae82b2e5e8b8c6dd9340e46243f388e94819e5a23ce46232c62312e50d8138ca6658e11b17d3e8e
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/86568/dot_86568_DS1.pdf
• File Type:
  [PDF - 3.30 MB ]