A spatio‐temporal ensemble method for large‐scale traffic state prediction. (23rd June 2019)

• Record Type: Journal Article
• Authors: Liu, Yang; Liu, Zhiyuan; Vu, Hai L.; Lyu, Cheng
• Other Names: Qu Xiaobo guestEditor.; van Arem Bart guestEditor.; Ukkusuri Satish V. guestEditor.
• Is Part Of: Computer-aided civil and infrastructure engineering. Volume 35:Number 1(2020:Jan.)
• Page Start: 26
• Page End: 44
• Publication Date: 2019-06-23
• Subjects: Civil engineering -- Data processing -- Periodicals; Computer-aided engineering -- Periodicals; 624.0285
• DOI: 10.1111/mice.12459
• Languages: English
• ISSNs: 1093-9687

Abstract

Abstract: How to effectively ensemble multiple models while leveraging the spatio‐temporal information is a challenging but practical problem. However, there is no existing ensemble method explicitly designed for spatio‐temporal data. In this paper, a fully convolutional model based on semantic segmentation technology is proposed, termed as spatio‐temporal ensemble net. The proposed method is suitable for grid‐based spatio‐temporal prediction in dense urban areas. Experiments demonstrate that through spatio‐temporal ensemble net, multiple traffic state prediction base models can be combined to improve the prediction accuracy.