Transport Data Science and Advanced Analytics
Project Member(s): Liu, W.
Funding or Partner Organisation: National ICT Australia
Start year: 2015
Summary: This collaborative research project proposes a Transport Data Science and Advanced Analytics initiative targeted to address traffic congestion problem from data science and data analytics perspectives. While traffic information is ubiquitously captured by various types of data, this project will utilize cutting-edge data mining and machine learning technologies to make advanced travel time and traffic volume prediction, assist in decision support, and provide effective strategies for road safety and handling natural disasters. Specifically, the project will pay major attention to the problem of incident detection from social network and social media.
Publications:
Do, Q, Pham, T, Liu, W & Ramamohanarao, K 1970, 'WTEN: An Advanced Coupled Tensor Factorization Strategy for Learning from Imbalanced Data', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Web Information Systems Engineering, Springer International Publishing, Shanghai, China, pp. 537-552.
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Quan Do & Liu, W 1970, 'ASTEN: An accurate and scalable approach to Coupled Tensor Factorization', 2016 International Joint Conference on Neural Networks (IJCNN), 2016 International Joint Conference on Neural Networks (IJCNN), IEEE, Vancouver, Canada, pp. 99-106.
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Shao, J, Yin, J, Liu, W & Cao, L 1970, 'Mining actionable combined patterns of high utility and frequency', 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), IEEE, Paris, pp. 1-10.
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Keywords: Transport Data Science, Advanced Analytics, Incident Detection.
FOR Codes: Pattern Recognition and Data Mining, Expanding Knowledge in the Information and Computing Sciences, Data engineering and data science