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摘要: 介绍了用于短期交通流预测的两大类模型: 统计预测算法和人工神经网络模型。对其中各种模型的特征进行了比较, 将历史平均模型、求和自回归滑动平均模型(ARIMA)、非参数回归模型、径向基函数(RBF) 神经网络模型与贝叶斯组合神经网络模型, 应用于一个真实路网的短期流量预测, 比较了各模型的预测结果。结果表明, 组合神经网络模型预测误差最小, 可靠性最高, 是一种对短期交通流预测的有效方法。Abstract: A large number of techniques have been applied into short-term traffic flow prediction, which can be classified into two groups: statistical models and artificial neural network model. The models and their application were discussed and compared. Several models, including historical average, ARIMA (auto regressive integrated moving average) model, nonparametric regression, RBF (radial basis function) neural network and Bayesian combined neural network model were applied into a numerical example of short-term traffic volume prediction in a field network, their prediction results and performances were compared. It was found that the error of hybrid neural network model is littlest, its prediction reliability is highest, it is the most effective method to predicte short-term traffic flow.
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Key words:
- traffic engineering /
- short-term traffic flow /
- prediction /
- methods /
- comparing
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图 2 预测值与真实值对比
Figure 2. Comparison of observed traffic flow and predicted traffic flow
表 1 误差均值和误差分布概率比较
Table 1. Comparison of MAPE and PPE
模型 EMAPE/% EPPE 历史平均 12.47 0.69 ARIMA (0, 1, 1) 14.87 0.65 非参数回归 11.54 0.71 RBF神经网络 11.06 0.75 组合神经网络 10.20 0.80 
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表 2 应用比较
Table 2. Application comparison
模型 优点 弱项 历史平均 操作简单易行 无法表现交通流变化 ARIMA (0, 1, 1) 时间序列过程的应用 很难处理数据有空缺的情形 非参数回归 无需假定变量关系 对近邻状态的定义不易掌握 RBF神经网络 适合于复杂、非线性关系的描述 “黑盒子”的系统结构 组合神经网络 实时跟踪、自适应调整预测性能 需调试训练多个模型
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