Recent Advances in Traffic Prediction Using Deep Learning Techniques
Date and Time of the talk: April 22 2021, 7:00 PM EDT (April 23 9AM Melbourne time)
Information of the Speaker
Jianzhong Qi, University of Melbourne
Jianzhong Qi is a senior lecturer in the School of Computing and Information Systems at The University of Melbourne. He obtained his PhD degree from The University of Melbourne in 2014. He has been an intern at Microsoft Redmond in 2014 and a visiting scholar at Northwestern University in 2017. Jianzhong Qi publishes in leading venues in database management and machine learning such as TPAMI, TODS, VLDBJ, ICML, NeurIPS, and PVLDB. He has won the “Best Vision Paper” award in ACM SIGSPATIAL 2017. He is the PC Co-Chair for the Australasian Database Conference 2020. His research interests include machine learning and data management and analytics, with a focus on spatial, temporal, and textual data.
Traffic prediction plays an essential role in intelligent transportation systems. Accurate traffic prediction can assist traffic management, route planning and assignment, and congestion mitigation. This problem is challenging due to the complex and dynamic spatio-temporal dependencies between different regions of the road network. Extensive studies have been done in this area, proposing techniques ranging from classical statistical methods to modern machine learning especially deep learning models. In this guest lecture, we will discuss recent developments in this area, with a focus on deep learning models for traffic prediction.