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Short-term Road Traffic Prediction based on Deep Cluster at Large-scale Networks by Han, Lingyi; Zheng, Kan; Zhao, Long; Wang, Xianbin; Shen, Xuemin is a scholarly article available to read on EtoBox.

What is Short-term Road Traffic Prediction based on Deep Cluster at Large-scale Networks about?

Short-term road traffic prediction (STTP) is one of the most important modules in Intelligent Transportation Systems (ITS). However, network-level STTP still remains challenging due to the difficulties both in modeling the diverse traffic patterns and tacking high-dimensional time series with low latency. Therefore, a framework combining with a deep clustering (DeepCluster) module is developed for STTP at largescale networks in this paper. The DeepCluster module is proposed to supervise the representation learning in a visualized way from the large unlabeled dataset. More specifically, to fully exploit the traffic periodicity, the raw series is first split into a number of sub-series for triplets generation. The convolutional neural networks (CNNs) with triplet loss are utilized to extract the features of shape by transferring the series into visual images. The shape-based representations are then used for road segments clustering. Thereafter, motivated by the fact that the road segments in a group have similar patterns, a model sharing strategy is further proposed to build recurrent NNs (RNNs)-based predictions through a group-based model (GM), instead of individual-based model (I

Author

Han, Lingyi; Zheng, Kan; Zhao, Long; Wang, Xianbin; Shen, Xuemin

Published

2019

Language

EN