论文标题
基于多模式融合和表示映射的大规模交通拥堵预测
Large-Scale Traffic Congestion Prediction based on Multimodal Fusion and Representation Mapping
论文作者
论文摘要
随着城市化过程的进步，城市运输系统对于城市的发展和公民的生活质量至关重要。其中，它是通过分析拥塞因素来判断交通拥堵的最重要任务之一。最近，引入了各种传统和基于机器学习的模型，以预测交通拥堵。但是，这些模型要么在大规模的拥塞因素上汇总不当，要么无法对大规模空间中每个精确的位置做出准确的预测。为了减轻这些问题，本文提出了一个基于卷积神经网络的新型端到端框架。通过学习表示，该框架提出了一个新颖的多模式融合模块和一个新颖的表示映射模块，以在大规模地图上在任意查询位置上实现交通拥堵预测，并结合各种全局参考信息。所提出的框架对现实世界大规模数据集取得了重大结果和有效的推断。
With the progress of the urbanisation process, the urban transportation system is extremely critical to the development of cities and the quality of life of the citizens. Among them, it is one of the most important tasks to judge traffic congestion by analysing the congestion factors. Recently, various traditional and machine-learning-based models have been introduced for predicting traffic congestion. However, these models are either poorly aggregated for massive congestion factors or fail to make accurate predictions for every precise location in large-scale space. To alleviate these problems, a novel end-to-end framework based on convolutional neural networks is proposed in this paper. With learning representations, the framework proposes a novel multimodal fusion module and a novel representation mapping module to achieve traffic congestion predictions on arbitrary query locations on a large-scale map, combined with various global reference information. The proposed framework achieves significant results and efficient inference on real-world large-scale datasets.
