Collaborative analytics for predicting expressway-traffic congestion

Authors:
Chee Seng Chong;Bong Zoebir;Alan Yu Shyang Tan;William-Chandra Tjhi;Tianyou Zhang;Kee Khoon Lee;Reuben Mingguang Li;Whye Loon Tung;Francis Bu-Sung Lee
Affiliations:
Institute of High Performance Computing, Singapore;Nanyang Technological University, Singapore;HP Labs Singapore;Institute of High Performance Computing, Singapore;Institute of High Performance Computing, Singapore;Institute of High Performance Computing, Singapore;National University of Singapore;HP Labs Singapore;Nanyang Technological University, Singapore
Venue:
Proceedings of the 14th Annual International Conference on Electronic Commerce
Year:
2012

Citing 4
Cited 0

Optimizing search engines using clickthrough data

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Traffic flow forecasting by seasonal SVR with chaotic simulated annealing algorithm

Neurocomputing
Experiment databases

Machine Learning
A cloud-based workflow management solution for collaborative analytics

ICSOC'11 Proceedings of the 2011 international conference on Service-Oriented Computing

Quantified Score

Hi-index	0.00

Visualization

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.