Rate My Professor Lillian Wu

Wu's research focuses on AI-driven urban mobility systems, integrating data-driven analytics, learning-based control, and spatial modelling to improve transport efficiency, safety, and sustainability. She employs spatial analysis and urban big data modelling to examine interactions between the built environment, transport infrastructure, and human mobility. Key publications include 'The 15-minute community life circle for older people: Walkability measurement based on service accessibility and street-level built environment – A case study of Suzhou, China' (Cities, 2025, with Z. Jiang and H. Chung), 'Deep Reinforcement Learning Based Traffic Signal Control: A Comparative Analysis' (Procedia Computer Science, 2023, with I. Kim and Z. Ma), 'The effects of built environment spatial variation on bike-sharing usage: A case study of Suzhou, China' (Cities, 2021, with I. Kim and H. Chung), 'Analyzing the structural properties of bike-sharing networks: Evidence from the United States, Canada, and China' (Transportation Research Part A: Policy and Practice, 2020, with I. Kim), and 'Multi-Agent Reinforcement Learning for Traffic Signal Control: Algorithms and Robustness Analysis' (IEEE 23rd International Conference on Intelligent Transportation Systems, 2020, with Z. Ma and I. Kim). She has earned the UWA School of Engineering Commendation for Teaching Excellence Award for Early Career (2024), Best Presentation Award (2025), UWA Data Institute Travel Grant (2025), and recognition as an outstanding reviewer for Multimodal Transportation (2024). Wu serves as Associate Editor for the IEEE Intelligent Transportation Systems Conference, reviews for journals, chairs conference sessions, and contributes to projects like 'Preparing WA to deploy automated on-road freight vehicles'.