基于FWADE-ELM的短时交通流预测方法

受道路环境和人为因素影响,实际交通系统可视为一个复杂的非线性动力系统,交通流数据具有较强的非线性、时变性和易受随机噪声影响等特征.针对复杂环境下的短时交通流预测问题,提出一种基于烟花差分进化混合算法-极限学习机的短时交通流预测方法.采用奇异谱分析方法滤除原始交通流数据中包含的噪声成分,降噪后的交通流数据用于训练极限学习机(ELM)网络预测模型.进行相空间重构,利用C-C算法确定ELM网络的结构和关键参数.通过融合烟花算法和差分进化算法提出一种烟花差分进化混合算法,可有效提高基本算法的整体优化性能.将改进的混合优化算法用于优化ELM网络的权阈值(结构为9-11-1,维数为110),建立短时交通流预测模型.测试与应用结果表明,所构建的短时交通流预测模型具有较高的预测精度和较强的泛化能力(均方误差为7.75,平均绝对百分比误差为0.086 7),预测值与实际值的拟合程度较好.

Short-term traffic flow forecasting based on hybrid FWADE-ELM

Affected by road condition and human factors, the practical traffic system can be considered as a complex nonlinear dynamical system and the traffic flow data have the properties of strong non-linearity, time-variety and susceptibility to random noises. To solve the problems of short-term traffic flow forecasting in complex environment, a prediction method based on the hybrid FWADE-ELM is proposed. The singular spectrum analysis(SSA) technique is used to filter the noise existing in the original traffic flow data and then the ELM neural network prediction model is trained with the preprocessed data. The structure and key parameters of ELM network are determined using the C-C algorithm after reconstructing phase space. A hybrid optimization method integrating the fireworks algorithm with the differential evolution algorithm is developed to improve the optimal performance of the basic algorithms. The ELM network forecasting model is built and its weights and biases (9-11-1 structure and 110 dimensions) are optimized using the proposed FWADE hybrid algorithm. The results of property testing and practical application show that this short-term traffic flow forecasting model has higher forecasting accuracy and better generalization ability, and the predicted values comply well with the actual values.