改进鲸鱼优化支持向量机的交通流量模糊粒化预测

针对支持向量机（SVM）在交通流量预测中存在波动性且预测精度低的问题，提出了采用模糊信息粒化（FIG）和改进鲸鱼优化算法（IWOA）的SVM模型来预测交通流量的变化趋势和动态区间。首先，对数据处理采用FIG方法进行处理，从而得到交通流量变化区间的上界（Up）、下界（Low）和趋势值（R）；其次，在鲸鱼优化算法（WOA）的种群初始化中采用动态对立学习来增加种群多样性，并引入了非线性收敛因子和自适应权重来增强算法的全局搜索及局部寻优能力，然后建立了IWOA模型，并分析了IWOA的复杂度；最后，以预测交通流量的均方误差（MSE）为目标函数，在IWOA迭代过程中不断优化SVM的超参数，建立了基于FIG-IWOA-SVM的交通流量区间预测模型。在国内和国外交通流量数据集上进行测试的结果表明，在国外交通流量预测上，与基于遗传算法优化的支持向量机（GA-SVM）、基于粒子群优化算法优化的支持向量机（PSO-SVM）和基于鲸鱼优化算法的支持向量机（WOA-SVM）相比，IWOA-SVM模型的平均绝对误差（MAE）分别降低了89.5%、81.5%和1.5%；而FIG-IWOA-SVM模型在交通流量动态区间和趋势预测上与FIG-GA-SVM、FIG-PSO-SVM和FIG-WOA-SVM等模型相比预测精度更高且预测范围更平稳。实验结果表明，在不增加算法复杂度的前提下，FIG-IWOA-SVM模型能够合理地预测交通流量的变化趋势和变化区间，为后续的交通规划和流量控制提供依据。

Fuzzy granulation prediction of traffic flow based on improved whale optimization support vector machine

Focusing on the problems of Support Vector Machine (SVM) in traffic flow prediction:volatility and low prediction accuracy, an SVM model using Fuzzy Information Granulation (FIG) and Improved Whale Optimization Algorithm(IWOA) was proposed to predict the traffic flow trends and dynamic ranges. Firstly, the FIG method was performed to the data to obtain the Upper bound (Up), Lower bound (Low) and Trend value (R) of the traffic flow change interval. Secondly, in the population initialization of Whale Optimization Algorithm (WOA), the dynamic opposition-based learning was used to increase the population diversity, and the nonlinear convergence factor and adaptive weight were introduced to enhance the global search and local optimization capabilities of the algorithm. After that, the IWOA model was established and the complexity of IWOA was analyzed. Finally, with the Mean Square Error (MSE) of the predicted traffic flow as the objective function, the hyperparameters of SVM were optimized continuously in the iteration process of IWOA, and a traffic flow interval prediction model based on FIG-IWOA-SVM was established. The tests on domestic and foreign traffic flow datasets were carried out. The results show that, in the prediction of foreign traffic flow, compared with Support Vector Machine based on Genetic Algorithm optimization (GA-SVM), Support Vector Machine based on Particle Swarm Optimization algorithm (PSO-SVM) and Support Vector Machine based on Whale Optimization Algorithm (WOA-SVM), the proposed IWOA-SVM model has the Mean Absolute Error (MAE) reduced by 89.5%, 81.5% and 1.5% respectively. Compared with the FIG-GA-SVM, FIG-PSO-SVM and FIG-WOA-SVM models, the FIG-IWOA-SVM model has higher prediction accuracy and the more stable prediction range in the traffic flow dynamic interval and trend prediction. Experimental results show that, without increasing the complexity of the algorithm, the proposed FIG-IWOA-SVM model can reasonably predict the change trend and change interval of traffic flow, and provide a basis for subsequent traffic planning and flow control.