RESEARCH

Transportation Asset Management

Transportation assets are the most critical element in transportation systems, providing essential functional support for road users. Public agencies need to make optimized decisions to maintain the functionality of their transportation assets while minimizing costs. These optimized decisions can and will rely on a comprehensive synthesis using sensing-based systems. Therefore, AI Lab envisions its efforts in this direction to develop fundamental ML/AI algorithms for sensing to construct a system that can intelligently evaluate, monitor, and diagnose transportation asset conditions.

• Ai, C. (PI). “Sidewalk Condition Assessment Leveraging Machine Learning/AI and Mobile LiDAR.” The District Department of Transportation. 7/2023-1/2025.
• Ai, C. (PI), and Gerasimidis, S. “Development of a Visualization, Sharing, and Processing Platform for Large-Scale Highway Point Cloud Data.” Massachusetts Department of Transportation. 1/2023-1/2025.
• Ai, C. (PI). “A Method for Pavement Marking Inventory and Retroreflectivity Condition Assessment Using Mobile LiDAR.” Massachusetts Department of Transportation. (Phase 1: 2/2020-6/2022 [Webinar]; Phase 2: 1/2023-2/2025).
• Marshall, W., Janson, B., and Ai, C. (UMass PI). “MPC-678 – Pedestrian Infrastructure and ADA Compliance: Leveraging Advances in Spatial Technologies.” U.S. Department of Transportation through the Upper Great Plains Transportation Institute. 6/2022-7/2023.
• Ai, C. (PI). “Automated Guardrail Inventory and Condition Evaluation.” Massachusetts Department of Transportation. 2021-2022.
• Ai, C. (PI). “Improving Pedestrian Facility Inventory Data in Massachusetts using Mobile Light Detection and Ranging (LiDAR).” Massachusetts Department of Transportation. 2018-1019.

AI Lab sees great opportunities in research on transportation safety awareness using sensing technologies. Sensing technologies offer inherent advantages for addressing fundamental questions in this research area, e.g., how to accurately and objectively acquire and evaluate safety measures, and how to anticipate (“predict”) potential safety issues proactively. However, naturally, many research issues in transportation safety may not be instantly resolved by simply applying sensing technologies. For example, it may remain challenging to quantify and estimate human perception and psychological behavior. Therefore, AI Lab envisions research in this direction focusing on these challenging issues by integrating traditional safety analysis methods, e.g., driving simulation and surveys, with sensing-based methods, e.g., eye tracking and vehicle trajectory analysis.

• Ai, C. (PI) and Tessier, R. “Development of a Salt Spreader Controller Program that Correlates its Material Dispensation Rate to Machine-Sensed Roadway Weather Parameters.” Massachusetts Department of Transportation. (Phase 1: 4/2022-4/2024 [Webinar]; Phase 2: 2/2025-2/2027)
• Christofa, E. Ai, C. (co-PI), and Furth, P. “Accessible Bus Stop Design in the Presence of Bike Lanes.” Massachusetts Department of Transportation. 1/2023-7/2024.
• Oke, J., Xie, Y., Ai, C. (co-PI), and Tainter, F. “Artificial Intelligence Framework for Mid-block Crosswalk Detection across Massachusetts.” Massachusetts Department of Transportation. 6/2023-12/2023. [Webinar].
• Xie, Y. and Ai, C. (co-PI). “Railroad Grade Crossing Profile Data Collection and Modeling.” Massachusetts Department of Transportation. 10/2023-8/2025.
• Christofa, E. and Ai, C. (co-PI). “Effectiveness of Two-stage Turn Queue Boxes in Massachusetts: A Comparison with Bike Boxes.” Massachusetts Department of Transportation. 8/2022-2/2024. [Webinar].
• Christofa, E. (PI) and Ai, C. (co-PI). “Effectiveness of Bicycle Boxes in Massachusetts.” Massachusetts Department of Transportation. 3/2020-9/2021. [Webinar].
• Xie, Y., Ai, C. (co-PI), and Liu, B. “Uncovering the Root Causes of Truck Rollover Crashes on Ramps.” Massachusetts Department of Transportation. 4/2021-3/2023. [Webinar].
• Ai, C. (PI), with Knodler, M. “Collecting Model Inventory Road Element (MIRE) Fundamental Data Elements (FDEs) for Intersections in Massachusetts.” Massachusetts Department of Transportation. 2/2019-6/2023.
• Ai, C. (PI), with Fitzpatrick, C. “Deflection Angle Effect on Continuous Driver Performance along Horizontal Curves.” U.S. Department of Transportation through the Safety Research Using Simulation (SAFER-Sim) University Transportation Center. 12/2020-6/2023. [Webinar].
• Ai, C. (PI), with Knodler, M. “Quantifying the Impacts of Situational Visual Clutter on Driving Performance Using Video Analysis and Eye Tracking.” U.S. Department of Transportation through the Safety Research Using Simulation (SAFER-Sim) University Transportation Center. 12/2019-5/2021. [Webinar].

Infrastructure Preservation and Maintenance

To construct and maintain the most invested infrastructure in the entire transportation system, studies on pavement and bridge condition evaluation and preservation have been actively conducted over the past decades. Until recently, emerging 3-D scanning laser technology has revolutionized both industry practices and those of public transportation agencies, especially in data acquisition. However, while the overwhelming volume of data provides unprecedented detail on pavements and bridges, it has become increasingly challenging to extract useful information accurately and efficiently to support maintenance decisions. Therefore, AI Lab envisions research in this direction that bridges the gap between “raw data” and “derived knowledge” of infrastructure condition by harnessing exciting collaborations in image processing and signal processing, structural analysis, deterioration modeling, infrastructure management systems, and related areas. 

• Gerasimidis, S. and Ai, C. (co-PI). “Collaborative Research: ENG-EAM: Solid-State Cold Spray Additive Manufacturing Repair for Corroded Steel Structures.” National Science Foundation. 12/2025-12/2027.
• Tessier, R., Ai, C. (co-PI), and Gummeson, J. “The Laboratory Information Materials Management System (LIMMS) Development Planning.” Massachusetts Department of Transportation. 1/2023-7/2024.
• Gerasimidis, S., Breña, S., and Ai, C. (co-PI). “Development of Improved Inspection Techniques Using LiDAR for Deteriorated Steel Beam Ends.” Massachusetts Department of Transportation. 4/2022-4/2024. [Webinar].