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	Metric-based multi-task grouping neural network for traffic flow forecasting
	Hong, Haikun ; Huang, Wenhao ; Song, Guojie ; Xie, Kunqing
	2014
英文摘要	Traffic flow forecasting is a fundamental problem in transportation modeling and management. Among various methods multitask neural network has been demonstrated to be a promising and effective model for traffic flow forecasting, while there are still two issues unconsidered: 1) learning unrelated tasks together tends to reduce the model??s performance; 2) how to define or learn the distance metric for distinguishing related tasks and unrelated tasks. In this paper, a metric learning based K-means method is proposed to group related tasks together which effectively reduces the semantic gap between domain knowledge and handcrafted feature engineering. Then for each group of tasks, a deep neural network is built for traffic flow forecasting. Experimental results show the metric-based grouping method clusters tasks more reasonably with a better metric than classic Euclidean-based Kmeans. The final results of traffic flow forecasting on real dataset show the metric-based multi-task neural network outperforms the Euclideanbased multi-task neural network.; EI; CPCI-S(ISTP); 0
语种	英语
DOI标识	10.1007/978-3-319-12436-0_55
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/294766]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Hong, Haikun,Huang, Wenhao,Song, Guojie,et al. Metric-based multi-task grouping neural network for traffic flow forecasting. 2014-01-01.

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