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On prediction of traffic flows in smart cities: a multitask deep learning based approach
World Wide Web ( IF 3.7 ) Pub Date : 2021-04-01 , DOI: 10.1007/s11280-021-00877-4
Fucheng Wang , Jiajie Xu , Chengfei Liu , Rui Zhou , Pengpeng Zhao

With the rapid development of transportation systems, traffic data have been largely produced in daily lives. Finding the insights of all these complex data is of great significance to vehicle dispatching and public safety. In this work, we propose a multitask deep learning model called Multitask Recurrent Graph Convolutional Network (MRGCN) for accurately predicting traffic flows in the city. Specifically, we design a multitask framework consisting of four components: a region-flow encoder for modeling region-flow dynamics, a transition-flow encoder for exploring transition-flow correlations, a context modeling component for contextualized fusion of two types of traffic flows and a task-specific decoder for predicting traffic flows. Particularly, we introduce Dual-attention Graph Convolutional Gated Recurrent Units (DGCGRU) to simultaneously capture spatial and temporal dependencies, which integrate graph convolution and recurrent model as a whole. Extensive experiments are carried out on two real-world datasets and the results demonstrate that our proposed method outperforms several existing approaches.



中文翻译:

关于智慧城市交通流量的预测:一种基于多任务深度学习的方法

随着运输系统的迅速发展,交通数据已经在日常生活中大量产生。找到所有这些复杂数据的见解对车辆调度和公共安全具有重要意义。在这项工作中,我们提出了一种称为多任务递归图卷积网络(MRGCN)的多任务深度学习模型,用于准确预测城市中的交通流量。具体来说,我们设计了一个包含四个组件的多任务框架:用于对区域流动力学进行建模的区域流编码器,用于研究过渡流相关性的过渡流编码器,用于两种交通流的上下文融合的上下文建模组件以及特定任务的解码器,用于预测流量。特别介绍双注意图卷积门控循环单元(DGCGRU)可以同时捕获空间和时间相关性,将图卷积和递归模型集成为一个整体。在两个真实的数据集上进行了广泛的实验,结果表明我们提出的方法优于几种现有方法。

更新日期:2021-04-01
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