Abstract
Traffic flow forecasting is a fundamental problem in transportation modeling and management. Among various methods multi-task 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 K-means. The final results of traffic flow forecasting on real dataset show the metric-based multi-task neural network outperforms the Euclidean-based multi-task neural network.
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Hong, H., Huang, W., Song, G., Xie, K. (2014). Metric-Based Multi-Task Grouping Neural Network for Traffic Flow Forecasting. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science(), vol 8866. Springer, Cham. https://doi.org/10.1007/978-3-319-12436-0_55
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DOI: https://doi.org/10.1007/978-3-319-12436-0_55
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