Short-term traffic state prediction has been an integral part of intelligent transportation systems. Currently, many prediction methods that model traffic dynamics have been proposed. However, only a few of them utilize the relationship among traffic variables represented on the fundamental diagram of traffic flows for prediction. This study proposes traffic speed prediction models based on a threshold error correction model. The proposed model expresses long-run fundamental relationships between speed and density, which differ in non-congested and congested states separated by a speed or density threshold, and the short-run fluctuation of traffic variables that are cointegrated. Using traffic sensor data for more than 300 days on 96 links of arterial roads in Naha City, Japan, the feasibility of the proposed method is evaluated through the analysis of estimated parameters and prediction accuracy. The results confirmed that the proposed method estimates the fundamental speed–density relationship in non-congested and congested states and improves the prediction accuracy using simple threshold error correction models, feed-forward neural networks. The proposed method supports vector regression for most of the links.

中文翻译：

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基于非拥塞和拥塞状态下速度-密度的基本和协整关系的短期交通速度预测

短期交通状态预测已成为智能交通系统不可或缺的一部分。目前，已经提出了许多模拟交通动态的预测方法。然而，其中只有少数利用交通流基本图上表示的交通变量之间的关系进行预测。本研究提出了基于阈值误差校正模型的交通速度预测模型。所提出的模型表达了速度和密度之间的长期基本关系，其在由速度或密度阈值分隔的非拥塞和拥塞状态以及协整的交通变量的短期波动中有所不同。在日本那霸市的 96 条干线道路上使用了 300 多天的交通传感器数据，通过对估计参数和预测精度的分析，评估了该方法的可行性。结果证实，所提出的方法估计了非拥塞和拥塞状态下的基本速度-密度关系，并使用简单的阈值误差校正模型、前馈神经网络提高了预测精度。所提出的方法支持大多数链接的向量回归。