Intelligent transportation systems (ITS) are advanced and intelligent applications which aim to provide innovative services for improving safety and mobility related to different modes of transport and traffic management. Our team has performed cutting-edge research and development in several areas.
Some of our research work appears in major transportation journals (e.g., Transportation Research Part C,Transportation Research Record, and Journal of Transportation Engineering ASCE) and major conferences (e.g., TRB Annual Meeting and IEEE ITS Conference).
Our Co-Founder, Dr. Xie, is the lead inventor of Smart and Scalable URban TRAffic Control (SURTRAC), a real-time, adaptive traffic control system that improves dynamic traffic flows in urban road networks.
He has created the core control engine (the brain) of SURTRAC, including schedule-driven intersection control and decentralized coordination mechanisms. He has also worked on strengthening strategies of SURTRAC to enable its operations by coping with real-world traffic challenges in urban environment.
Our goal is to analyze the relative performance of alternative route choice models as different assumptions are made about the type of traffic control in use in the urban road network. To this end, we deﬁne a uniﬁed agent-based framework for formulating different route choice models, and integrate this framework with a microscopic trafﬁc simulation environment. Within this framework, each agent’s memory is updated repeatedly (daily) to reﬂect available prior individual and social experience, and then a route is chosen by a probabilistic sequential decision-making process that is a function of the agent’s updated current memory.
We have focused on improving the broader mobility of all modes of traffic that are central to sustainable urban living – especially pedestrians – in an integrated way, specifically on optimizing the delay tradeoffs between vehicles and pedestrians. Three basic extensions are introduced and evaluated on a real-world road network. Tests are performed in the field at selected intersections of the target road network, to demonstrate the effectiveness of the approach in operation.
We present a study combining participatory and physical sensing data, based on location-based checkins collected in the Pittsburgh metropolitan area, and vehicle records collected in a sub-area controlled by an adaptive urban traffic control system. Our aim is to disclose how we could utilize the combined data for a better understanding on urban mobility networks and activity patterns in urban environments, and how we may take advantage of such combined data to improve urban mobility applications such as anomaly traffic detection and reasoning, topic-based nontrivial traffic information extraction, and traffic demand analysis.