基于云模型不确定性推理的地图匹配算法

A Map-Matching Algorithm Based on Uncertain Reasoning of Cloud Model

为了解决对于道路网密集且高程变化较大的城市道路地图匹配精度不高的问题,提出一种能够实现定性概念与定量数值之间不确定性转换的云模型地图匹配算法。通过建立云规则和进行基于云模型的不确定性推理,并且结合高程辅助方法来构筑地图匹配模型。云模型可以将定性概念的模糊性与随机性集成到一起,克服了基于模糊逻辑地图匹配算法中隶属度的确定带有主观色彩的不足。仿真试验以城市路网为例,并借助高程辅助的方法进行了分析,结果证明了该算法具有较高的匹配精度。

For improving map-matching accuracy in cities having dense overpasses,the paper proposed a map-matching algorithm based on cloud model.This approach established a map-matching model by establishing cloud rules and reasoning with uncertainty based on cloud model.The cloud model integrated fuzziness with randomness of qualitative concept so as to overcome the subjective randomness in fuzzy membership grade when being determined.The simulation results,taking road network in the city as example,illustrated that higher accuracy was obtained.