Hybrid evolutionary algorithms in a SVR traffic flow forecasting model
Hybrid evolutionary algorithms in a SVR traffic flow forecasting model
复制标题
SVR交通流预测模型中的混合进化算法
DOI：
10.1016/j.amc.2011.01.073
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发表时间：
2011-04-01
影响因子：
4
通讯作者：
Wei, Shih Yung
中科院分区：
文献类型：
--
作者：
Hong, Wei-Chiang;Dong, Yucheng;Wei, Shih Yung
准确的城市交通流预测是智能交通系统发展和实施的关键，因此，它一直是道路交通拥堵研究中的重要问题之一。由于城市交通流具有复杂的非线性数据模式，文献中的交通流预测方法种类繁多，很难得出哪种预测方法优于其他预测方法的一般性结论。近年来，支持向量回归模型(SVR)被广泛应用于解决非线性回归和时间序列问题。提出了一种SVR交通流预测模型，该模型采用遗传-模拟退火法(GA-SA)相结合的混合算法来确定其合适的参数组合。此外，以台湾北部交通流资料为例，验证了SVRGA-SA模型的预测效能。预测结果表明，该模型比季节自回归综合移动平均模型(SARIMA)、反向传播神经网络(BPNN)、霍尔特-温特斯(HW)和季节性霍尔特-温特斯(SHW)模型具有更高的预测精度。因此，SVRGA-SA模型是一种很有前途的交通流量预测方法。(C)2011 Elsevier Inc.保留所有权利。
Accurate urban traffic flow forecasting is critical to intelligent transportation system developments and implementations, thus, it has been one of the most important issues in the research on road traffic congestion. Due to complex nonlinear data pattern of the urban traffic flow, there are many kinds of traffic flow forecasting techniques in literature, thus, it is difficult to make a general conclusion which forecasting technique is superior to others. Recently, the support vector regression model (SVR) has been widely used to solve nonlinear regression and time series problems. This investigation presents a SVR traffic flow forecasting model which employs the hybrid genetic algorithm-simulated annealing algorithm (GA-SA) to determine its suitable parameter combination. Additionally, a numerical example of traffic flow data from northern Taiwan is used to elucidate the forecasting performance of the proposed SVRGA-SA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the seasonal autoregressive integrated moving average (SARIMA), back-propagation neural network (BPNN), Holt-Winters (HW) and seasonal Holt-Winters (SHW) models. Therefore, the SVRGA-SA model is a promising alternative for forecasting traffic flow. (C) 2011 Elsevier Inc. All rights reserved.