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.
Yue's project aims to improve traffic management by studying how economic incentives, such as tolls and subsidies, can reduce congestion and make traffic systems more efficient. Traffic management can be viewed as a resource allocation problem, where limited road space should be used strategically to reduce congestion and help all drivers reach their destinations efficiently. Typically, drivers will act in their own interests choosing the route they believe will minimise their travel time, which often leads to system inefficiencies. In this project, congestion game models will be used to better understand how individual route choices affect the entire system and how incentives can encourage choices that enhance overall traffic flow and reduce total travel time.
In real-world settings, drivers enter a network at different times; however, current studies have only focused on static congestion games, where drivers enter the network simultaneously. The project will first explore the link between static and dynamic congestion games to identify how their dynamics differ. Based on these insights, we will further examine the effectiveness of tolls and subsidies in dynamic settings. For instance, while marginal cost toll pricing has shown promise in reducing inefficiencies in static scenarios, it remains unknown whether it will perform similarly in time-varying conditions. By developing strategies for applying these incentives in more realistic settings, this research aims to lay the groundwork for future, practical applications. The findings could ultimately help guide policies that improve traffic flow and reduce congestion without requiring extensive infrastructure changes.
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