Collaborative analytics for predicting expressway-traffic congestion
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 recen...
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sg-ntu-dr.10356-980452020-05-28T07:17:22Z Collaborative analytics for predicting expressway-traffic congestion 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 School of Computer Engineering Annual International Conference on Electronic Commerce (14th : 2012) DRNTU::Engineering::Computer science and engineering 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. 2013-07-25T07:29:02Z 2019-12-06T19:49:59Z 2013-07-25T07:29:02Z 2019-12-06T19:49:59Z 2012 2012 Conference Paper 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. https://hdl.handle.net/10356/98045 http://hdl.handle.net/10220/12269 10.1145/2346536.2346542 en © 2012 ACM. |
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DRNTU::Engineering::Computer science and engineering 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 Collaborative analytics for predicting expressway-traffic congestion |
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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. |
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School of Computer Engineering |
author_facet |
School of Computer Engineering 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 |
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Conference or Workshop Item |
author |
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 |
author_sort |
Chong, Chee Seng |
title |
Collaborative analytics for predicting expressway-traffic congestion |
title_short |
Collaborative analytics for predicting expressway-traffic congestion |
title_full |
Collaborative analytics for predicting expressway-traffic congestion |
title_fullStr |
Collaborative analytics for predicting expressway-traffic congestion |
title_full_unstemmed |
Collaborative analytics for predicting expressway-traffic congestion |
title_sort |
collaborative analytics for predicting expressway-traffic congestion |
publishDate |
2013 |
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https://hdl.handle.net/10356/98045 http://hdl.handle.net/10220/12269 |
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1681057956212244480 |