Yue completed her Bachelor's degree in Economics and Statistics at Jinan University, where she developed a strong interest in quantitative analysis and its applications across various fields. Building upon this foundation, she achieved a Master's degree in Economics for Business Intelligence and Systems with distinction at the University of Bath, enhancing her technical expertise and academic skills.
Throughout her academic journey, Yue had the opportunity to engage in a summer project with the Office for National Statistics (ONS). Here, she focused on examining the transition of individuals with disabilities and their connection to various life outcomes. This experience let her developed her ability to approach data analysis methodically and logically, while also deepening her awareness of the importance of inclusivity in our society.
Yue's research interests primarily center around the economic and computational aspects of transportation systems. She hopes to apply her acquired skills and knowledge to the development of more effective traffic management systems, with the aim of reducing traffic congestion and vehicle emissions.
Traffic congestion in the main roads and junctions of our cities is a world-wide problem incurring huge financial losses to the global economy as well as other negative environmental and health effects. We propose to study an algorithmic and economic / game theoretic design of a smart traffic management system to dynamically schedule traffic based on real-time traffic conditions and value of time reports from drivers, aiming to minimize the economic damage of traffic congestion.
Many cities in the UK have various types of static congestion charges where ‘static’ refers to the fee being charge, to the hourly/daily limitations, and to the zone itself. Economically, this could be highly inefficient as various parts of the city could have different congestion patterns that a static fee for example could only partially resolve. A too-low fee will be ineffective while a too-high fee will significantly damage economic and social activities unnecessarily.
Yue will study the possibility of designing a system to dynamically set congestion fees and route traffic based on multiple factors. The proposed system will be composed of three parts: (1) an information module to disseminate knowledge about traffic across the network, enabling each road and junction to obtain a probabilistic forecast about the future arrival of cars; (2) a traffic light scheduling algorithm that, given this information and the information on the value of time of drivers arriving to junction, will decide on the green/red light schedule in order to minimize the aggregate cost of waiting in the junction; and (3) a payment scheme for charging payments from drivers, ensuring that drivers will not exaggerate (nor underestimate) their value of time reports. These payments will effectively create a dynamic toll system that will more efficiently ensure that drivers use roads in an economically efficient way, replacing today’s static toll systems.
Such a dynamic system is advantageous since the resulting tolls depend on the actual route being taken, and drivers’ payments increase when using more congested junctions. Yue's research is to design the algorithmic and economic theory behind such a system, and to evaluate the obtained solutions via computer simulations. The proposed research will rely on a variety of techniques: methods of planning and scheduling from artificial intelligence, algorithmic methods from theoretical computer science, and methods for the design of incentive-compatible (“truthful”) mechanisms from game theory. Such an interdisciplinary interaction will benefit all these disciplines, as it will introduce new questions, new answers, and new tools and techniques, to all of them.
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