Liang Zhang, PhD
Zhang is an expert in traffic operations modeling, including public transit systems, passenger movement and vehicle traffic patterns. His research areas include intelligent transportation systems and transportation network analysis, with a focus on traffic data analysis, machine learning in transportation systems, and transportation sustainability. His recent research examines the integration of machine learning to help transit systems predict and accommodate usage patterns and passenger flow.
His doctoral research focused on intelligent transportation systems and using vehicle data to optimize traffic signals and supporting public policy efforts to reduce traffic. Zhang has published research about how smart traffic signal control systems could help to conserve energy and reduce traffic congestion that results in higher levels of vehicle emissions. He has participated in or led several research projects funded by NSF, PennDOT and industry. He is an active member of the American Society for Engineering Management (ASEM), the International Council on Systems Engineering (INCOSE) and the Transportation Research Board (TRB).
Zhang’s most recent research looks at passenger flow behavior on subway systems in urban areas. Using machine learning tools to develop forecasting techniques, the research looks at ways transit systems can reduce wait times and avoid dangerous levels of passenger congestion in subway cars and on platforms.