ITSC 2024 Paper Abstract

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Paper ThBT7.6

Zhao, Yaxin (Chongqing University), Xiang, Pengcheng (Chongqing University), Luan, Xinyu (Chongqing University), Liu, Shengjie (Chongqing university)

A Spatio-Temporal Embedding and Attention Mechanism for Traffic Prediction in Large-Scale Road Networks

Scheduled for presentation during the Regular Session "Traffic prediction and estimation IV" (ThBT7), Thursday, September 26, 2024, 16:10−16:30, Salon 15

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 3, 2024

Keywords Data Mining and Data Analysis, Simulation and Modeling, Traffic Theory for ITS

Abstract

Various fields have recognized the effectiveness of network embedding for handling large-scale graphs. However, accurate traffic conditions prediction using network embedding on large-scale road networks remains challenging due to the intricate correlations in traffic data. To tackle this challenge, we propose a novel framework called Spatio-Temporal Embedding and Attention Mechanism (STEAM). In the embedding process, we design a novel spatial embedding method to consider both the local structure and global structural role of each node. Concurrently, long-term and short-term temporal dependencies are embedded in the temporal part. During the inference process, an attention mechanism is applied to adaptively capture the nonlinear spatio-temporal correlations. Our model was evaluated by predicting traffic speed on two traffic datasets, demonstrating significantly improved prediction performance and superior generalization capabilities.

 

 

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