城市干道交通拥堵识别技术研究

Urban Trunk Road Traffic Congestion Identification Technology

为快速有效地进行城市干道的交通拥堵识别,文中提出一种基于朴素贝叶斯的城市干道交通拥堵识别算法。最后,基于南京市主干道的交通调查数据,对朴素贝叶斯算法以及基于径向基函数神经网络的城市干道交通拥堵识别算法进行对比。结果表明,朴素贝叶斯算法在对城市干道交通状态的识别上比基于径向基函数神经网络算法具有更好的准确性、优越性以及更低的误判率。

To quickly and efficiently recognize the urban trunk road traffic congestion is important to traffic management. In this paper,an urban trunk road traffic congestion recognition based on naive Bayesian algorithm has been proposed. Based on the traffic survey data in Nanjing,the naive bayes algorithm and neural network traffic congestion identification algorithms were compared. The results show that Naive bayes algorithm on urban trunk road traffic state identification has better accuracy,advantages and lower rate of misjudgment than radial basis function（RBF） neural network algorithm.