Traffic Congestion Prediction: A Spatial-Temporal Context Embedding and Metric Learning Approach

Hongsheng Hao, Liang Wang, Zenggang Xia, Zhiwen Yu, Jianhua Gu, Ning Fu
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Abstract

In urban informatics, traffic congestion prediction is of great importance for travel route planning and traffic management, and has received extensive attention from academia and industry. However, most previous works fail to implement a citywide traffic congestion prediction on fine-grained road segment, and without comprehensively considering strong spatial-temporal correlations. To overcome these concerns, in this paper, we propose a spatial-temporal context embedding and metric learning approach (STE-ML) to predict the traffic congestion level. In particular, our STE-ML consists of a traffic spatial-temporal context embedding component, and a metric learning component. From local and global perspectives, the context embedding component can simultaneously integrate local spatial-temporal correlation features and global traffic statistics information, and compress into an unified and abstract embedding representation. Meanwhile, metric learning component benefits from learning a more suitable distance function tuned to specific task. The combination of these models together could enhance traffic congestion prediction performance. We conduct extensive experiments on real traffic data set to evaluate the performance of our proposed STE-ML approach, and make comparison with other existing techniques. The experimental results demonstrate that the proposed STE-ML outperforms the existing methods.
交通拥堵预测:一种时空上下文嵌入和度量学习方法
在城市信息学中,交通拥堵预测对出行路线规划和交通管理具有重要意义,已受到学术界和业界的广泛关注。然而,以往的研究大多未能在细粒度路段上实现全市范围内的交通拥堵预测,且没有综合考虑强时空相关性。为了克服这些问题,本文提出了一种时空上下文嵌入和度量学习方法(STE-ML)来预测交通拥堵水平。特别地,我们的STE-ML由交通时空上下文嵌入组件和度量学习组件组成。从局部和全局的角度来看,上下文嵌入组件可以同时整合局部时空相关特征和全局交通统计信息,并压缩成统一抽象的嵌入表示。同时,度量学习组件受益于学习更适合特定任务的距离函数。这些模型的结合可以提高交通拥堵预测的性能。我们在真实的交通数据集上进行了大量的实验,以评估我们提出的STE-ML方法的性能,并与其他现有技术进行比较。实验结果表明,本文提出的STE-ML方法优于现有的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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