Research Topics
ProjectsV2X Network-Enabled Traffic Analysis and Smart Traffic Signal Control for Large Traffic Networks (November 2019 - October 2022)Abstract: Mobility, literally speaking, refers to the ability to move from one location to another. It is key to the functioning of a livable and sustainable community. An efficient people-mover system is the backbone of a smart city that has been drawing worldwide attention. In anticipation of population growth and demographic changes, it is vital to develop an integrated and sustainable transport system that meets the diverse needs of the burgeoning population. The key lies in our ability to harness the capabilities of information communication technologies, factor resilience into infrastructure planning and management, retrofit existing infrastructure to promote greener commuting modes and develop innovative technologies in a timely manner to respond to people of all age groups. With all advanced transport related technologies achieved so far, fundamentally we are still facing the main challenge of how to ensure safety, comfort and affordability in terms of travel (time or money) cost, environmental footprint, and social impact. In this project we shall address two key problems from a systems and control perspective, i.e., how to discover and understand people’s travel needs and commute patterns at a societal level via traffic analysis and prediction, and how to use traffic signal control, which is essentially a group control mechanism, to enhance safety, comfort and affordability of daily travels in a large complex traffic network. Distributed Adaptive Urban Traffic Signal Control based on V2X Information Infrastructure (April 2015 - March 2019)Abstract: Traffic congestion has become one of the major challenges for metropolitan growth. In addition, the waste exhaustion from massive vehicles trapped on the road also imposes environmental issues, which, for example, have caused significant social and health problems in China. In this project we will investigate how to improve traffic situations in a densely populated region by using intelligent traffic control with Vehicle-to-infrastructure (V2X) information technologies. In the first stage of research, we aim to develop a distributed traffic-responsive scheduling architecture for urban traffic signal control, which consists of a set of local processing centres running in parallel that communicate with each other for neighboring traffic information to refine their own local traffic signals. It takes real-time traffic measurements via road sensors and/or V2X communication infrastructure, estimates vehicle driving patterns (such as speeds, turning ratios and link densities), and generates the corresponding green time schedule for each junction aiming for minimizing the network-wise total waiting time. To deal with traffic uncertainties, a model-predictive strategy will be adopted. In the second stage, synthesized traffic-responsive traffic light schedules and the corresponding predicted link speeds are sent back to individual vehicles via V2X, which affect vehicles future route plans. Such updated route plans will be fed back to the traffic light control centers via V2X to enhance existing traffic light schedules. Our ultimate goal is to form a closed loop between traffic light schedulers and traffic signal end users (i.e., vehicles), whose joint efforts will eventually lead to an intelligent and highly adaptive road traffic management system. |