一种基于概率的路径预测与查询算法

研究了路网空间内的路径预测与查询技术,设计了基于统计信息和概率论的最优路径预测算法。实际应用中,路网错综复杂。提出可能路径集合的概念,并设计算法来提取当前路径预测涉及到的路网子网,减小路网规模和路径预测的复杂度。在空间网络环境下,现有移动对象位置预测技术主要针对短期预测,不能预测下一路口的交通情况。为了弥补这一缺陷,降低用户端的位置更新率,设计了路网移动模型来简洁描述提取自大量历史移动路径的移动统计特征,捕捉路口处转向模式。基于移动模型,提出了具有较高精度的交通预测模型来预测对象的运动路径。

Path Prediction and Query Algorithm Based on Probability

This paper studied the path prediction and query technology in road network and proposed the optimal path prediction algorithm based on statistics and probability theory.In practical applications,the road network is complex.We proposed the concept of possible path set,and designed an algorithm to extract sub-network of road network to be involved in the path prediction,which can help to reduce the network scale and the complexity of path prediction.In the spacial road network environment,the existing position prediction technologies of mobile object are mainly used for short-term forecasting,and they can not predict the traffic situation of the next road intersection.In order to overcome this defect and reduce location updating rate in terminal devices,this paper designed a simple mobility model of road network to extract mobile statistical features from a large number of historical mobile pathes,which can capture the stee-ring mode in road intersection.Based on this mobile model,a traffic forecasting model with high accuracy was proposed to predict the moving path of the object.