未来智能网联车与非网联车混行将带来更多的多源交通数据；为了提高数据的可靠性，结合传统交通数据获取方式提出了一种基于粒子群优化径向基神经网络的多源交通数据融合方法。首先选取不同来源的数据构建多源数据集并设置对照数据，利用Elbow Method方法和K-Means算法对多源数据集进行聚类，再以聚类中心坐标为参考构建相应径向基神经网络，最后在神经网络训练过程中引入粒子群算法，以融合结果与对照数据的差值作为粒子群算法迭代的目标函数，帮助求解神经网络中的参数。使用MATLAB实现神经网络并选取一组多源交通流量进行测试，同时再把这组交通流量数据用卡尔曼滤波算法融合，将两种方法的融合结果进行对比。结果表明：相比于传统卡尔曼滤波，使用粒子群优化的径向基神经网络对混行条件下的多源交通流量进行融合时数据误差均降低60%以上。

In the future，the combination of intelligent connected vehicles and traditional vehicles will bring more multi-source traffic data.In order to improve the reliability of data，a multi-source traffic data fusion method based on particle swarm optimization radial basis function neural network was proposed by combining traditional traffic data obtaining method.Firstly，the data from different sources were selected to construct multi-source data set and a group of contrast data.The multi-source data set was clustered by the Elbow Method and K-means algorithm，and then the corresponding radial basis function neural network was constructed with the reference of the cluster center coordinates.Finally，the particle swarm algorithm was introduced in the neural network training process，the difference between the fusion result and the control data was used as the objective function of the particle swarm algorithm iteration to help solve the parameters in neural network.The neural network was realized by MATLAB，and a group of multi-source traffic flow was selected for test.The same data was fused by Kalman filter algorithm at the same time，and the fusion results of the two methods were compared.The results show that，compared with the traditional Kalman filter，the data error is increased by more than 60% when the particle swarm optimization radial basis function neural network is employed to fuse multi-source traffic flow under mixed traffic conditions.