In order to improve the accuracy of short-term travel time prediction in an urban road network, a hybrid model for spatio-temporal feature extraction and prediction of urban road network travel time is proposed in this research, which combines empirical dynamic modeling (EDM) and complex networks (CN) with an XGBoost prediction model. Due to the highly nonlinear and dynamic nature of travel time series, it is necessary to consider time dependence and the spatial reliance of travel time series for predicting the travel time of road networks. The dynamic feature of the travel time series can be revealed by the EDM method, a nonlinear approach based on Chaos theory. Further, the spatial characteristic of urban traffic topology can be reflected from the perspective of complex networks. To fully guarantee the reasonability and validity of spatio-temporal features, which are dug by empirical dynamic modeling and complex networks (EDMCN), for urban traffic travel time prediction, an XGBoost prediction model is established for those characteristics. Through the in-depth exploration of the travel time and topology of a particular road network in Guiyang, the EDMCN-XGBoost prediction model’s performance is verified. The results show that, compared with the single XGBoost, autoregressive moving average, artificial neural network, support vector machine, and other models, the proposed EDMCN-XGBoost prediction model presents a better performance in forecasting.

中文翻译：

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基于时空特征提取的城市交通出行时间短期预测模型

为了提高城市路网中短期出行时间预测的准确性，提出了一种结合经验动态建模（EDM）的时空特征提取与城市路网出行时间预测的混合模型。以及具有XGBoost预测模型的复杂网络（CN）。由于行驶时间序列的高度非线性和动态性质，有必要考虑行驶时间序列的时间依赖性和空间依赖性，以预测道路网络的行驶时间。可以通过EDM方法（基于混沌理论的非线性方法）来揭示旅行时间序列的动态特征。此外，可以从复杂网络的角度反映城市​​交通拓扑的空间特征。为了充分保证时空特征的合理性和有效性，这些经验时空特征是通过经验动态模型和复杂网络（EDMCN）挖掘出来的，用于城市交通出行时间预测，为此建立了XGBoost预测模型。通过深入探索贵阳某路网的行驶时间和拓扑，验证了EDMCN-XGBoost预测模型的性能。结果表明，与单个XGBoost，自回归移动平均线，人工神经网络，支持向量机等模型相比，所提出的EDMCN-XGBoost预测模型在预测方面表现出更好的性能。针对这些特征建立了XGBoost预测模型。通过深入探索贵阳某路网的行驶时间和拓扑，验证了EDMCN-XGBoost预测模型的性能。结果表明，与单个XGBoost，自回归移动平均线，人工神经网络，支持向量机等模型相比，所提出的EDMCN-XGBoost预测模型在预测方面表现出更好的性能。针对这些特征建立了XGBoost预测模型。通过深入探索贵阳某路网的行驶时间和拓扑，验证了EDMCN-XGBoost预测模型的性能。结果表明，与单个XGBoost，自回归移动平均线，人工神经网络，支持向量机等模型相比，所提出的EDMCN-XGBoost预测模型在预测方面表现出更好的性能。