高速公路流预测的多相时间序列模型

M. Davarynejad, Yubin Wang, J. Vrancken, J. Berg
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引用次数: 9

摘要

本文提出了一种多阶段时间序列预测方法来解决高速公路流量预测问题。这里提出的方案是基于对交通模式的广泛研究,这些模式是从荷兰阿姆斯特丹一条密集使用的环路上收集的。本文提出的预测方法基于多相信息提取,其最终目标是预测网络边界点的交通状态。该方法结构简单,具有实际应用价值,与现有模型相比有很大改进。在其一般形式中,所提出的方法可以处理维数诅咒,这是与输入空间的维数相关的常见问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-phase time series models for motorway flow forecasting
In this study, a multi-phase time series prediction approaches is proposed for solving the motorway flow forecasting problem. The schemes presented here is based on an extensive study of flow patterns that were collected from a densely used ring road of Amsterdam, The Netherlands. The new prediction approach proposed here is based on a multiphase information extraction whose ultimate goal is to forecast traffic states at the boundary points of a network. With its simple architecture that makes the proposed approach of interest of practical application, a significant improvement is achieved in comparison with existing models. In its general form, the proposed approach could handle the curse of dimensionality, a common problem associated with the number of dimensions of input space.
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