Before joining the faculty at Georgia Tech, Dr. Tsai worked as a senior research scientist in the GIS center at Georgia Tech for 10 years. Since 1997, he has led a research team and worked with GDOT pavement engineers, developing and successfully implementing a large-scale Oracle GIS-based pavement preservation and management system for GDOT to effectively preserve and manage its 18,000-centerline miles of highway for the past 20 years. A series of models and programs developed by Dr. Tsai, including field pavement condition data acquisition, the annual pavement preservation project prioritization and program development, treatment determination model, cost model, and performance forecasting model, and long-term system performance simulation and optimization, have been successfully implemented by the Office of Maintenance of the GDOT. They have resulted very positive impact on GDOT's operations. He has received the innovation award on GIS-enabled Ship Recognition - Innovation Research Grants for Transportation Security in 2007. He was named Chinese Chang Jiang Scholar in 2009 for his international recognition on pavement preservation and management using sensing and information technologies. The Chang Jiang Scholar is one of the most prestigious scholar honors in China. His research focuses on 1) “Smart Cities: Intelligent Transportation Asset Health and Safety Assessment, Monitoring, and Managing Using Emerging Sensing Technologies and Artificial Intelligence (AI)”, 2) “New Transportation Ecosystem with Telematics, Connected and Connected Vehicles”, 3) Big Data Analytics. The sensing technologies include 2D imaging, 3D laser, Lidar, UAV, Inertial Measurement Unit (IMU), Smart Phones, and GPS/GIS Technologies. His research includes spatial optimization of sensor and information technology and development of intelligent decision-support systems for improving roadway asset management with a special focus on pavements, roadside assets, like signs and guardrails, roadway safety, and logistics. He has actively developed ITS applications. Dr. Tsai had also led more than $3.5M of competitively selected research projects on “Remote Sensing and GIS-enabled Asset Management (RS-GAMS)” and “RS-GAMS Phase 2”, sponsored by the USDOT Office of the Assistant Secretary for Research and Technology (USDOT/OST-R), from 2010 to 2014. These projects were designed to intelligently assess roadway asset health conditions by using emerging sensor technologies installed in the intelligent Georgia Tech Sensing Vehicle (GTSV), along with artificial intelligence and machine learning that Dr. Tsai and his research team has integrated and developed (http://www.news.gatech.edu/2013/09/05/road-warriors-gt-researchers-redefine-infrastructure-maintenance). Dr. Tsai’s GDOT research project of “Implementation of Automatic Sign Inventory and Pavement Condition Evaluation on Georgia’s Interstate Highway” has been competitively selected as the 2017 AASHTO High Value Research (HVR) Award because of its innovation and broad impact (https://ce.gatech.edu/news/national-group-honors-research-using-lasers-a...). He is also the PIs for three research projects competitively selected and sponsored by the National Academy of Science (NAS) NCHRP Innovation Deserving Exploratory Analysis (IDEA) program on “Using Image Pattern Recognition Algorithms for Processing Video Log Images to Enhance Roadway Infrastructure Data Collection”, on “Development of Sensing Methodology for Intelligent and Reliable Work Zone Hazard Awareness”, and “Development of an Asphalt Pavement Raveling Detection Algorithm Using Emerging 3D Laser Technology and Macrotexture Analysis”. His research outcomes were featured in the National Academy of Sciences’ Ignition Magazine (http://www.trb.org/Publications/Blurbs/163652.aspx) because of their significant positive impact on asset management and roadway safety. One of the most noteworthy accomplishment is his development of a methodology to automatically detect sign condition changes using roadway images that are widely available. Although the developed methodology is applied for routine traffic sign asset inventory, it has promising applications for expedited infrastructure condition evaluation along with unmanned aerial vehicle (UAV) and smart phones, following natural disasters because of its automatic and non-contact nature. He is also the PI of License Plate Recognition, sponsored by the State Road and Tollway Authority (STRA), and the PIs for several research projects sponsored by GDOT, including Using Image Processing for Pavement Crack Growth Measurement-A Feasibility Study, and Development of Optimization Modules for GDOT Pavement Management System. He is also developing vehicle detection and tracking technologies for intelligent transportation system (ITS) and real-time traffic flow monitoring to improve road safety and mobility. He also works on logistics routing and scheduling for the United Parcel Service (UPS) and maximizing Port and Transportation System and logistics Productivity by Exploring Alternative Port Operation Strategies. Dr. Tsai is also working on a large-scale project on “Crowdsourcing Transportation Asset Management Using Low-Cost Mobile Devices” to create a synergy between logistics companies and transportation agencies. Dr. Tsai was also invited to give a talk on "Dynamic Mapping of Infrastructure & Road Conditions" in the Connected Fleets USA 2017 in September 26, 2017. He is currently part of the technical committee of the United States National Cooperative Highway Research Program (NCHRP 20-102 (06) Road Markings for Machine Vision, one of 14 connected and automated vehicle research projects sponsored by the USDOT. Dr. Tsai served on the Expert Task Group (ETG) of the US National Strategic Highway Research Program II (SHRP II) for the Naturalistic Driving Study (NDS) to provide guidance on research focuses, including the use of computer vision or processing and analyzing big NDS data, from 2008 to 2015. He is also on the technical committee of the AFD 10 Pavement Management Systems of the Transportation Research Board in the National Academies. Since 2010, he has served as the Associate Editor of ASCE Journal of Computing in Civil Engineering. Dr. Tsai holds a professional engineering (PE) license.
Smart Cities: Intelligent Transportation Asset Health and Safety Condition Assessment, Monitoring, and Management Using Emerging Sensing Technologies and AI, Big Data Analytics, Automated roadway health and safety condition assessment using image processing, 3D laser, and AI., ITS, GPS/GIS, Decision-support systems for pavement/infrastructure/asset management (inventory and condition assessment, preservation, and management), Roadway safety, Logistics