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首页> 外文期刊>Applied Intelligence: The International Journal of Artificial Intelligence, Neural Networks, and Complex Problem-Solving Technologies >A hybrid prediction approach for road tunnel traffic based on spatial-temporary data fusion
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A hybrid prediction approach for road tunnel traffic based on spatial-temporary data fusion

机译：基于空间临时数据融合的道路隧道流量混合预测方法

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摘要

In this paper, we propose a hybrid prediction model based on spatial-temporal data fusion to predict future tunnel traffic. Our approach consists of a local predictor, a global predictor, an outlier predictor, and a prediction integrator. Firstly, the local predictor forecast tunnel traffic based on the collected local data. It is more concerned with the historical and future traffic conditions, that is, the temporal correlation. Then, the global predictor uses data collected from peripheral road segments to predict tunnel volume, which models the spatial correlation based on a deep learning network. Thirdly, the prediction integrator dynamically integrates the prediction results of local and global predictors on the basis of the current weather conditions. In addition, we detect the abnormal traffic volume for training an individual outlier predictor. Finally, we integrate it with the output of the prediction integrator and accumulate the current tunnel traffic volume to calculate final prediction results. In our experiments, we collected the multisource urban-awareness data from Shanghai to evaluate the proposed hybrid prediction model. Our approach is obviously superior to the baseline when dealing with the general condition. The MREs of 30min and 60min tunnel traffic volume prediction are less than 6.5%. In addition, the outlier predictor of the proposed model significantly enhances the ability to predict the abnormal tunnel traffic under extreme weather conditions or unexpected traffic accidents.
机译：在本文中，我们提出了一种基于空间数据融合的混合预测模型来预测未来的隧道流量。我们的方法包括本地预测因子，全局预测器，异常值预测器和预测集成器。首先，基于所收集的本地数据的本地预测器预测隧道流量。它更关注历史和未来的交通状况，即时间相关性。然后，全局预测器使用从外围道路段收集的数据来预测隧道体积，其基于深度学习网络模拟空间相关性。第三，预测积分器在当前天气条件的基础上动态集成了本地和全球预测器的预测结果。此外，我们检测到培训个人异常预测器的异常交通量。最后，我们将其与预测集成器的输出集成并累计当前隧道流量以计算最终预测结果。在我们的实验中，我们从上海收集了多源城市认识数据，以评估提出的混合预测模型。在处理一般情况时，我们的方法显然优于基线。 30分钟和60分钟隧道交通量预测的先生小于6.5％。此外，所提出的模型的异常预测器显着提高了预测极端天气条件下的异常隧道交通的能力或意外的交通事故。

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