Sensing-Based Transportation Asset Management
Transportation assets are the most critical element in transportation systems to provide critical functional support for road users. Public agencies need to make optimized decisions to maintain the functionality of their transportation assets while minimizing cost. I strongly believe that these optimized decisions can be made through a comprehensive synthesis using a sensing-based system. Therefore, I envision my research in this direction takes a deeper root on developing fundamental computer vision and sensing signal processing algorithms to construct a system that can intelligently evaluate, monitor, and diagnose transportation asset conditions.
Sensing-Based Transportation Safety Awareness
I see great opportunities in the research areas of transportation safety awareness by employing sensing technologies. I feel that sensing technologies have inherent advantages to answer some of the fundamental questions in this research area, e.g. how to accurately and objectively acquire and evaluate safety measures, how to proactively aware (“predict”) the potential safety issues, etc. 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, I envision my research in this direction focuses on these challenging issues, by integrating the traditional safety analysis methods, e.g., driving simulation, survey, etc., and the sensing-based methods, e.g., eye tracking, vehicle trajectory analysis, etc., through collaborations with the current faculty members.
Sensing-Based Pavement Preservation and Maintenance
To construct and maintain the most invested infrastructure in the whole transportation infrastructure system, studies on pavement condition evaluation and preservation have been actively conducted during the past decades. Until recently, the emerging 3-D scanning laser technology has revolutionized both the industry’s and public transportation agencies’ practices, especially in the area of pavement data acquisition. However, while the overwhelmingly large amount of data provides unprecedented details for the pavement, it has become increasingly challenging to accurately and efficiently extract useful information supporting maintenance decisions. Therefore, I envision my research in this direction is to bridge the gap between the “raw data” acquired on the pavement and the “derived knowledge” of the pavement condition, through harnessing exciting collaborations in the areas of image processing and signal processing, structure analysis, pavement deterioration modeling, pavement management system, etc.