论文标题
改进的灰色系统模型用于预测流量参数
Improved Grey System Models for Predicting Traffic Parameters
论文作者
论文摘要
在运输应用程序中，例如实时路线指导，坡道计量，拥堵定价和特殊活动交通管理，需要准确的短期交通流量预测。为此，本文提出了几种新颖的\ textIt {在线}灰色系统模型（GM）：GM（1,1 $ | cos（ωt）$），GM（1,1 $ | SIN（ωt）$，$ COS（ωt）$）和GM（$ cos（ωt）$）和GM（1,1 $ | e^{ - e^at} $，$ sin（$ sin（cos cos）$）（$）$（$）。为了评估所提出模型的性能，将它们与一组基准模型进行了比较：GM（1,1）模型，具有和没有傅立叶误差校正的灰色Verhulst模型，线性时间序列模型和非线性时间序列模型。使用循环检测器和来自加利福尼亚，弗吉尼亚州和俄勒冈州的探测器数据进行评估。在基准模型中，误差校正了具有傅立叶的灰色Verhulst模型，优于GM（1,1）模型，线性时间序列和非线性时间序列序列模型。反过来，三种拟议的型号，GM（1,1 $ | cos（ωt）$），GM（1,1 $ | sin（ωt）$，$ cos（ωt）$）和GM（1,1 $ | e^{ - at} $，$ sin（$ sin（ωt）$，$ cos（ωt）$，$ cos（ωt）$，灰色terhmpeftiact at grey tecixtion decixtion decixtion decixtion decixtion diacity decixtion diacity decixtiac $ 16 \％$和$ 11 \％$，在根平方错误方面，$ 82 \％$，$ 58 \％$和$ 42 \％$，分别在平均绝对百分比错误方面。据观察，所提出的灰色系统模型更适应位置（例如，对所有道路类型的类型表现良好）和交通参数（例如，速度，旅行时间，占用时间和体积），并且不需要训练的数据点（发现4个观察值就足够）。
In transportation applications such as real-time route guidance, ramp metering, congestion pricing and special events traffic management, accurate short-term traffic flow prediction is needed. For this purpose, this paper proposes several novel \textit{online} Grey system models (GM): GM(1,1$|cos(ωt)$), GM(1,1$|sin(ωt)$, $cos(ωt)$), and GM(1,1$|e^{-at}$,$sin(ωt)$,$cos(ωt)$). To evaluate the performance of the proposed models, they are compared against a set of benchmark models: GM(1,1) model, Grey Verhulst models with and without Fourier error corrections, linear time series model, and nonlinear time series model. The evaluation is performed using loop detector and probe vehicle data from California, Virginia, and Oregon. Among the benchmark models, the error corrected Grey Verhulst model with Fourier outperformed the GM(1,1) model, linear time series, and non-linear time series models. In turn, the three proposed models, GM(1,1$|cos(ωt)$), GM(1,1$|sin(ωt)$,$cos(ωt)$), and GM(1,1$|e^{-at}$,$sin(ωt)$,$cos(ωt)$), outperformed the Grey Verhulst model in prediction by at least $65\%$, $16\%$, and $11\%$, in terms of Root Mean Squared Error, and by $82\%$, $58\%$, and $42\%$, in terms of Mean Absolute Percentage Error, respectively. It is observed that the proposed Grey system models are more adaptive to location (e.g., perform well for all roadway types) and traffic parameters (e.g., speed, travel time, occupancy, and volume), and they do not require as many data points for training (4 observations are found to be sufficient).
