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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, D & Liu, W 2016, 'ASTEN: an Accurate and Scalable Approach to Coupled Tensor Factorization', the International Joint Conference in Neural Networks, The International Joint Conference in Neural Networks, IEEE, Vancouver, Canada, pp. 99-106.
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Do, Q, Pham, T, Liu, W & Ramamohanarao, K 2016, 'WTEN: An Advanced Coupled Tensor Factorization Strategy for Learning from Imbalanced Data', Web Information Systems Engineering – WISE 2016, International Conference on Web Information Systems Engineering, Springer International Publishing, Shanghai, China, pp. 537-552.
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Shao, J, Yin, J, Liu, W & Cao, L 2015, 'Mining Actionable Combined Patterns of High Utility and Frequency', Proceedings of the IEEE International Conference on Data Science and Advanced Analytics, IEEE International Conference on Data Science and Advanced Analytics, 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