Hierarchical Graph Pooling is an Effective Citywide Traffic Condition Prediction Model

• URL: http://arxiv.org/abs/2209.03629v1
• Date: Thu, 8 Sep 2022 08:12:35 GMT
• Title: Hierarchical Graph Pooling is an Effective Citywide Traffic Condition Prediction Model
• Authors: Shilin Pu, Liang Chu, Zhuoran Hou, Jincheng Hu, Yanjun Huang, Yuanjian Zhang
• Abstract summary: This paper applies two hierarchical graph pooling approaches to the traffic prediction task to reduce graph information redundancy. The hierarchical graph pooling methods are contrasted with the other baselines on predictive performance. For the mentioned graph neural networks, this paper compares the predictive effects of different graph network inputs on traffic prediction accuracy.
• Score: 1.321203201549798
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate traffic conditions prediction provides a solid foundation for vehicle-environment coordination and traffic control tasks. Because of the complexity of road network data in spatial distribution and the diversity of deep learning methods, it becomes challenging to effectively define traffic data and adequately capture the complex spatial nonlinear features in the data. This paper applies two hierarchical graph pooling approaches to the traffic prediction task to reduce graph information redundancy. First, this paper verifies the effectiveness of hierarchical graph pooling methods in traffic prediction tasks. The hierarchical graph pooling methods are contrasted with the other baselines on predictive performance. Second, two mainstream hierarchical graph pooling methods, node clustering pooling and node drop pooling, are applied to analyze advantages and weaknesses in traffic prediction. Finally, for the mentioned graph neural networks, this paper compares the predictive effects of different graph network inputs on traffic prediction accuracy. The efficient ways of defining graph networks are analyzed and summarized.

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