基于对我国城市交通流的物性分析 ,提出了一种基于模糊神经网络的实时路段行程时间估计模型 ,用于将来自于交通控制中心的实时交通数据转换成为能够反映路段实时运行状况的直观参数 :路段行程时间 ,从而为交通流诱导服务 .这种方法用具有更高智能的神经网络实现了对抽象模糊规则的自动纠错的记忆 ,符合人类认识的模式 ,能令人满意地表达经验知识 ,而且模糊输入输出关系具有了明确的表达能力 . Based on the physical property analysis of urban traffic flow in our country, a real-time road segment travel time estimation model based on fuzzy neural network is proposed, which is used to convert real-time traffic data from traffic control center into visual parameters that can reflect the real-time running status of road segments: Which can serve as a guide for traffic flow.This method uses a more intelligent neural network to realize the automatic error correction of abstract fuzzy rules, which is in line with the pattern of human cognition and can express the experience knowledge satisfactorily, And the relationship between fuzzy input and output has a clear expression.