Updated: Nov 20, 2018
17 FEB 2017 (FRI) | 19:00-20:00
Wang Gungwu Theatre, Graduate House, The University of Hong Kong.
Prof. Chandra Bhat
Director of Center for Transportation Research, University Distinguished
Teaching Professor and Adnan Abou-Ayyash Centennial Professor in Transportation Engineering, Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin
Dr. Chandra R. Bhat is the Director of the Center for Transportation Research (CTR) and the Adnan Abou-Ayyash Centennial Professor in Transportation Engineering at The University of Texas at Austin, where he has a joint appointment between the Department of Civil, Architectural and Environmental Engineering (CAEE) and the Department of Economics. Bhat is a world-renowned expert in the area of transportation and urban policy design, with far reaching implications for public health, energy dependence, greenhouse gas emissions, and societal quality of life. Methodologically, he has been a pioneer in the formulation and use of statistical and econometric methods to analyze human choice behavior. His current research includes the social and environmental aspects of transportation, planning implications of connected and automated smart transportation systems (CASTS), and data science and predictive analytics. He is a recipient of many awards, including the 2017 Lifetime Achievement in Transportation Research and Education Award (Academic) from the Council of University Transportation Centers (CUTC). This award is to “identify individuals who have had a long history of significant and outstanding contribution to university transportation education and research resulting in a lasting contribution to transportation.” He also received the 2015 ASCE Frank Masters Award and the 2013 German Humboldt Award. In 2016, he was listed as the top ten transportation thought leaders in academia by CUTC and The Eno Foundation. He is a top-cited transportation engineering researcher.
This presentation will focus on a new data landscape in which a whole host of equipment can act as sensors — legacy roadway systems, smart phones and GPS systems, and smart cars themselves. The key issue is how to deal with such voluminous and diverse amounts of incoming data per unit of time, and translate them into usable information for near-real time operations purposes or for longer-term planning purposes. This is a challenge, given the low latency and data reliability required to translate data into actionable intelligence, especially for such safety applications as collision avoidance. In addition, predictive analytics to translate data into information requires the ability to deal with data that may be from multiple sources, highly noisy, heterogeneous, and high-dimensional with complex interdependencies. On the last of these, the joint