讲座题目：Data-Driven Solutions to Arterial and Freeway Operations Issues
报告人： Prof. Yao-Jan Wu
Due to recent advances in Intelligent Transportation Systems (ITS) technologies, an increasing number of public and private transportation agencies have begun to invest in traffic data collection systems. The ITS traffic data collected provides these agencies with a range of different possibilities for measuring roadway performance and improving traffic flow. However, transportation data are ubiquitous and collected from multiple sources, so full utilization of the multi-source data remains challenging because of its complexity. Additionally, not all agencies have fully utilized the capacities of the advanced technologies because few problem-solving approaches have been developed based on these new datasets. This presentation will first introduce a variety of traffic data available on U.S. freeways and arterials. Next, several data-driven solutions to enhancing traffic data usability for performance measurement will be presented and discussed. Several case studies of real traffic problems will be used to demonstrate the feasibility of the proposed data-driven approaches.
Dr. Yao-Jan Wu is an associate professor of transportation engineering and the Director of Smart Transportation Lab in the Civil and Architectural Engineering and Mechanics at the University of Arizona (UA). Before joining the UA, Dr. Wu was an assistant professor at Saint Louis University (2011~2013) and postdoc at the University of Virginia (2011). He has served as the Principal Investigator (PI) or Co-PI of more than 20 national/international projects. Dr. Wu has more than 100 peer-reviewed publications, including more than 40 journal publications. He has presented his research findings more than 100 times at national and international conferences, and invited speaker events. Dr. Wu’s research interests highlight a strong connection between information technology (IT) and traditional transportation research. His research broadly covers four major fields: 1) intelligent transportation systems (e.g., advanced traffic detection technology, sensor data quality control, and computer vision applications), 2) data-driven large-scale transportation system analysis and optimization (e.g., freeway and arterial operations, system, traffic data management, mining, and analysis, and traveler behavior analysis) , 3) traffic safety (e.g., accident modeling and analysis), and 4) sustainable transportation planning (e.g., transit and climate change).