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Smart city article details

Title	Deeprtp: A Deep Spatio-Temporal Residual Network For Regional Traffic Prediction
ID_Doc	18144
Authors	Liu Z.; Huang M.; Ye Z.; Wu K.
Year	2019
Published	Proceedings - 2019 15th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2019
DOI	http://dx.doi.org/10.1109/MSN48538.2019.00062
Abstract	Accurate traffic prediction can benefit many smart city applications. Existing works mainly consider traffic prediction on each individual road segment, and heavily rely on some statistical or machine learning models, which suffer from either poor prediction accuracy or high computation overheads for predictions of the whole road network. In this paper, we instead consider the region-level traffic prediction that is still useful for many applications. To describe the regional traffic conditions and capture their spatio-temporal dependencies, we present a deep learning based model - DeepRTP. Specifically, we use a novel metric called Traffic State Index (TSI) to measure regional traffic conditions, and carefully classify traffic data into three categories that are used to capture hourly, daily, and weekly traffic patterns. Furthermore, we employ the convolutional and residual neural networks to model both spatial and temporal dependencies. Experimental results from real-world traffic data demonstrate that DeepRTP outperforms five baseline methods and can achieve higher prediction accuracy. © 2019 IEEE.
Author Keywords	Deep residual network; Spatiotemporal dependency; Traffic prediction

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