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Short-Term Traffic Flow Forecasting of Road Network Based on Spatial-Temporal Characteristics of Traffic Flow

机译：基于交通流时空特征的路网短期交通流预测

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摘要

This paper has presented a novel approach designed to realize multi-section short-term traffic flow synchronization forecasting in terms of road network. First, the road network is split into sub networks in accordance with traffic flow spatial-temporal characteristics. Second, chaos analysis method is proposed to forecast short-term traffic flow. Short-term traffic flow characteristics have been figured out by the phase space reconstruction technology and G-P algorithm. By analyzing the traffic flow database, the chaos characteristics of short-term traffic flow time series are correspondingly obtained. Furthermore, Elman neural network in which the input is reconstructing time series has been employed to achieve multi-section forecasting. In addition, an empirical study has been carried out to illustrate this approach. Consequently, this approach has been verified by using traffic flow field data on the road network. The results which support the use of this approach is indicating higher accuracy in short-term traffic flow forecasting.
机译：本文提出了一种新颖的方法，旨在实现基于路网的多路段短期交通流同步预测。首先，根据交通流的时空特性将道路网络划分为子网络。其次，提出了混沌分析方法来预测短期交通流量。相空间重构技术和G-P算法已经确定了短期交通流的特征。通过对交通流数据库的分析，可以得到短期交通流时间序列的混沌特征。此外，已采用输入重构时间序列的Elman神经网络来实现多部分预测。此外，进行了一项实证研究来说明这种方法。因此，该方法已通过使用道路网络上的交通流现场数据进行了验证。支持使用此方法的结果表明短期交通流量预测的准确性更高。

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