基于定性动态概率网络的交通状态预测及改进

 交通问题已经成为了制约城市发展的一个主要问题.城市的交通状态是可以预测和加以改进的.有效的交通状态预测在一定程度上能优化交通状态,减少交通堵塞.定性动态概率网络(QDPNs)是目前进行动态地推理不确定知识领域最有效的模型之一.提出了一种基于定性动态概率网络的交通状态预测及改进的方法,该方法从系统的角度对城市的交通状态进行建模,通过推理,能够找到交通问题的症结,以便采取有针对性的措施来解决交通拥堵问题.

Traffic state prediction and improvement based on qualitative dynamic probabilistic networks

The traffic problem has become a major obstacle of cities’ development.The traffic state of cities could be predicted and improved.An efficient traffic state prediction can improve the traffic state and reduce the traffic obstruction.Qualitative Dynamic Probabilistic Networks(QDPNs) is one of the most efficient models in the uncertain knowledge and dynamically reasoning field.A traffic state predictionand improvement method based on QDPNs has been presented in this paper.The method can systematically model the traffic state of city.By reasoning it,the model can help us find the crux of the problem so that we will be able to take targeted measures to address the traffic congestion.