城市道路交通的不确定性、非线性和时空相关性使得交通系统参数描述和知识获取极为困难，从而使短时交通预测难以获得满意结果。利用多核混合支持向量机识别和处理不同类别输入数据的能力，提出了一种基于多核混合支持向量机的城市短时交通预测方法。该方法在统计分析交通状态数据样本的基础上，继承了支持向量机良好的泛化能力、全局最优和较强自适应性的特点，并采用改进的粒子群算法对支持向量机的参数进行了优化选择。同时，针对道路实时交通状态与历史平均交通状态较强的线性相关性、道路实时交通状态与前几时段交通状态及上下游路段实时交通状态的非线性相关性，分别设计了线性核函数和非线性核函数对城市交通状态进行映射和拟合。该方法既考虑到交通状态历史规律对预测的指导意义，又顾及交通的时变特征，充分提取了交通系统相关参数的知识信息。实验结果表明，本文提出的短时交通流预测方法具有准确性、鲁棒性和自适应性特点，具有较好的实际应用价值。

City road traffic system is characterized as a system of nonlinearity, uncertainty and spatial-temporal correlation, which makes traffic system parameters description and knowledge extraction difficult, and results in current short-term traffic forecast methods can not obtain satisfactory accuracy. This paper presents a hybrid multiple-kernel support vector machine model (MSVM) for conducting short-term traffic forecast. With statistical analysis of large amounts of traffic condition data samples, the proposed model not only has a capacity of recognizing and dealing with different types of input data separately, but also takes advantages of global optimization, generalization and adaptability of support vector machine. Moreover, the parameters of the hybrid model is optimized with an improved particle swarm algorithm (PSO). Aiming at the linear correlation between real time and historical traffic condition, the nonlinear correlation between real time and previous time period, and also up and downstream traffic condition, the proposed model uses a linear kernel to extract the linear pattern of traffic flow and a nonlinear kernel to map the nonlinear pattern of residuals from the linear kernel. Both the historical regularity and time-variation characteristics of city road traffic are considered in the MSVM model so as to obtain the knowledge from the influential factors of real time traffic condition in order to improve forecast accuracy. The experimental results show that the proposed model behaves satisfactory performance and robustness, and has a good potential for applications.