I2SAFE Lab

Smart Work Zone

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Project Overview

Rationale

Work zones remain one of the most hazardous environments for both roadway workers and motorists due to limited visibility, changing traffic patterns, and unpredictable driver behavior. This project develops an integrated smart work zone system that combines IoT sensing, artificial intelligence, digital twin technology, and connected vehicle communication to detect risks in real time and provide timely warnings to improve safety and situational awareness.

Impact

  • Improve worker safety through real-time detection of vehicle intrusions and worker exposure to live traffic.
  • Enhance driver awareness by delivering timely roadside, wearable, and in-vehicle warnings for hazardous work zone conditions.
  • Support data-driven decision-making through a digital twin platform that provides real-time visualization and monitoring of work zone operations.
  • Advance connected and automated transportation by enabling V2X communication between infrastructure, workers, and vehicles.
  • Establish a scalable smart work zone framework that can be deployed in both stationary and mobile work zones to reduce crashes, improve operational efficiency, and support future autonomous vehicle integration.

Publication List

Conference Proceedings

  1. Riaz, A., Chang, W. C., Zhu, T., Esmaeili, B., Feng, Y., Su, L., & Hasanzadeh, S. (2026). “AI-Driven Digital Twin for Smart Work Zone Safety: Sensing, Risk Prediction, and Adaptive Alerting”. The 23rd Smart and Sustainable Built Environment (SASBE) 2026 FUTURE ROADS Conference. Cambridge, UK
  2. Chang, W. C., Weston, N., & Hasanzadeh, S. (2026). “AI-Driven Trajectory Prediction to Support Worker Monitoring in Work Zone Digital Twins”. Construction Research Congress 2026. San Antonio, Texas.
  3. Chang, W. C., Wang, X., Zhu, T., Adetooto, J. D., Su, L.,Feng, Y., Esmaeili, B., & Hasanzadeh, S. (2026). “Development of an Integrated Smart Work Zone for Proactive Intrusion Detection and Warning”. Transportation Research Board Annual Meeting. Washington D.C.
  4. Chang, W. C., Wang, X., Zhu, T., Adetooto, J. D., Su, L.,Feng, Y., Esmaeili, B., & Hasanzadeh, S. (2025). “Digital Twin for Detecting Worker Violations in Road Construction Work Zones”. International Conference on Computing in Civil Engineering. New Orleans, Louisiana.
  5. Ghadiri, A, Chang, W. C., & Hasanzadeh, S. (2025). “Leveraging Large Language Models and Deep Learning for Construction Work Zone Intrusion Risk Assessment”. International Conference on Computing in Civil Engineering. New Orleans, Louisiana.

Researcher

Woei-Chyi Chang

Almass Riaz

Jionghao Wei

Nathan Weston

Collaborators

  • Yiheng Feng, PhD, Lyles School of Civil and Construction Engineering, Purdue University
  • Lu Su, PhD, Elmore Family School of Electrical and Computer Engineering, Purdue University 
  • Behzad Esmaeili, PhD, Lyles School of Civil and Construction Engineering, Purdue University

Funding Source