基于S型函数预处理的RBF神经网络交通流量预测

根据交通流复杂性的特点,提出了一种基于S型函数标准化数据预处理的交通流量RBF网络预测方法,缩短了RBF网络训练时间;同时采用OLS算法有效降低RBF网络训练的随机性。实验仿真结果表明,该算法可用于实时交通流量及参数预测,并具有可靠的精度和较好的收敛速度。     

基于改进OLS算法的RBF神经网络 高速公路事件探测

提出了一种基于模糊聚类技术和RBF神经网络的混合智能高速公路事件自动探测算法,同时改进了用于RBF神经网络训练的Oils(正交最小二乘)选择算法.仿真实验证明,改进的OLS选择算法大大提高了RBF神经网络的训练速度同时具有无须事先确定RBF中心的优点,将之运用于公路事件探测可以获得满意的性能.     

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为了定量反映民机"座级"、"设计航程"与直接运行成本之间的内在关联性,利用NASA 97方法为各种样本机型在不同的长短结合的"飞行航距"下生成直接运行成本样本点,确定"型别等级"民机市场直接运行成本与"飞行航距"的函数形式并计算回归系数的估计量,以此分析"座级"、"设计航程"与各个回归系数估计量之间的函数关系,给出"型别等级"民机市场DOC的计算模型函数,通过OLS方法估算模型的回归系数.通过与NASA 97方法对4种样本机型产生的10个样本点的比较,发现模型计算的最大相对误差为0.086,因此模型可行.     

Truck trip generation in small- and medium-sized urban areas

This paper describes procedures to develop truck trip generation (TTG) rates for small- and medium-sized urban areas and its implications. Ordinary least squares models are used to develop separate truck production and attraction equations with the number of employees as the independent variable for three industrial groups – retail, transportation and warehousing, and manufacturing. Results from this research indicate that number of employees is a statistically significant predictor, and has significant explanatory power in predicting the number of truck trips produced and attracted. The rates developed in this study are also found to be significantly different from rates developed in other studies with the implication that caution needs to be taken when transferring TTG rates. The rates are applied in a travel demand model as the initial step of incorporating truck traffic into the modeling process.