基于时空分析的路口相似度计算方法

在交通领域中,传统的交通时空数据分析方法存在拥堵路口的时空数据相似性检索效率低、可靠性差的问题,从空间场景相似度和动态数据相似度等角度出发,提出一种基于时空分析的交通路口相似度计算方法.该方法由交通路口静态数据模型和动态数据模型构成,路口静态数据模型将交通路口作为空间场景,计算目标路口与数据库路口的空间场景相似度,路口动态数据模型根据路口检测器数据的时间属性,创建时序数据库,在时间维度上对路口动态数据进行切片,计算目标路口与数据库路口在相同时段内的相似度,在此基础上构建路口相似度计算模型,对满足相似检索的路口进行排序,以增强检索结果的可靠性.实验结果表明,与时空索引检索算法相比,该方法能够有效提高交通路口检索的准确性,且具有较强的实用性.

Intersection Similarity Computing Method Based on Spatiotemporal Analysis

In the traffic field,the traditional traffic spatiotemporal data analysis method has drawbacks such low efficiency and poor reliability of spatiotemporal data similarity retrieval at congested intersections.From the perspectives of spatial scene similarity and dynamic data similarity,this paper proposes an intersection similarity calculation method based on spatiotemporal analysis.This method is comprised of a static data model and a dynamic model of intersections.The static data model takes the intersections as the spatial scene to calculate the spatial scene similarities between the target intersection and the database intersection.The dynamic data model builds a time series database according to the time attribute of the intersection detector data.Then the dynamic model slices the dynamic data of intersections in time dimension and calculates the similarities between the target intersection and the database intersection in the same period.On this basis,the similarity computing model is built and the intersections that satisfy the similarity retrieval are sorted to enhance the reliability of retrieval results.Experimental results show that compared with the spatial index retrieval algorithm,the proposed method can effectively improve the accuracy and efficiency of intersection retrieval.