基于高阶神经网络的城市交通诱导理论模型

The Theoretical Models of Urban Traffic Flow Guidance Based on Higher-order Generalized Neural Network

交通诱导是智能运输系统的主要研究内容和解决城市网络局部拥挤的最佳途径。为实现快速准确的诱导功能，本文以高阶广义神经元模型为基础，设计了交通流预测和动态分配的理论模型。

The traffic flow guidance is one of the core contents of the Intelligent Transportation Systems(ITS) and the best way to solve the problem of local congestion in urban network.Based on higherorder intelligent neuron model,we offered a set of theoretical models about traffic flow prediction and DTA which will be helpful for realizing the guidance function quickly and accurately.