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Title: Collaborative analytics for predicting expressway-traffic congestion
Authors: Chong, Chee Seng
Zoebir, Bon
Tan, Alan Yu Shyang
Tjhi, William-Chandra
Zhang, Tianyou
Lee, Kee Khoon
Li, Reuben Mingguang
Tung, Whye Loon
Lee, Francis Bu-Sung
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Source: Chong, C. S., Zoebir, B., Tan, A. Y. S., Tjhi, W.-C., Zhang, T., Lee, K. K., et al. (2012). Collaborative analytics for predicting expressway-traffic congestion. Proceedings of the 14th Annual International Conference on Electronic Commerce.
Abstract: There are many ways to build a predictive model from data. Besides the numerous classification or regression algorithms to choose from, there are countless possibilities of useful data transformation prior to modeling. To assist in discovering good predictive analytics workflows, we introduced recently a collaborative analytics system that allows workflow sharing and reuse. We designed a recommendation engine for the system to enable matching of analytics needs with relevant workflows stored in repository. The engine relies on meta-predictive modeling of traffic-analysis workflow-characteristics. In this paper, we present a feasibility study of applying this collaborative analytics system to predict traffic congestion. Different ways to build predictive models from traffic dataset are pooled as shared workflows. We demonstrate that through dynamic recommendation of workflows that are suitable for the real-time varying traffic data, a reliable congestion prediction can be achieved. The promising results showcase that systematic collaboration among data scientists made possible by our system can be a powerful tool to produce very accurate prediction from data.
DOI: 10.1145/2346536.2346542
Rights: © 2012 ACM.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

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