顾及高低速信息的道路限速识别方法  

Study on Road Speed Limit Identification Taking into Account High and Low Speed Information

作　　者：张彩丽 向隆刚[2] 李雅丽 Zhang Caili;Xiang Longgang;Li Yali(School of Surveying and Urban Spatial Information,Henan University of Urban Construction,Pingdingshan 467000,China;State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China;School of Transportation and Geomatics Engineering,Shenyang Jianzhu University,Shenyang 110000,China)

机构地区：[1]河南城建学院测绘与城市空间信息学院,河南平顶山467000 [2]武汉大学测绘遥感信息工程国家重点实验室,湖北武汉430079 [3]沈阳建筑大学交通与测绘工程学院,辽宁沈阳110000

出　　处：《南京师大学报（自然科学版）》2024年第4期31-38,共8页Journal of Nanjing Normal University(Natural Science Edition)

基　　金：河南城建学院博士科研启动金资助项目(K-Q2023032);河南城建学院高等教育教学改革研究与实践项目(2024JG158);河南省高等学校重点科研项目(24B420002);辽宁省教育厅青年项目(LJ212410153040);国家自然科学基金项目(41771474、42071432)。

摘　　要：随着新兴行业对地图细节丰富度和要素完备性的需求,开展精细可导航路网构建研究是大势所趋,然而基于轨迹数据的道路信息挖掘,多聚焦于路网骨架的识别,较少对道路段限速信息进行识别.考虑低速与高速路段的轨迹运行速度分布不同,本文基于轨迹数据对高低速路段分别进行建模,开展轨迹数据限速识别优化.在此基础上融合路网数据限速识别结果,最终完成限速信息的识别.以武汉市二环路区域与成都市二环路区域为例进行实验,验证了方法的有效性.实验结果表明:(1)高低速信息分别建模明显提升了轨迹限速识别准确率;(2)融合轨迹数据限速识别优化结果与路网数据限速识别结果也进一步提升了限速识别结果.With the demand of emerging industries for map detail richness and element completeness,it is a general trend to carry out research on the construction of fine navigable road networks.However,road information mining based on trajectory data focuses more on the identification of road network skeletons and less on the identification of road speed limit information.Considering the difference between the trajectory running speed distribution of the low speed and high speed sections,this paper models the high speed and low speed sections separately based on the trajectory data,and carries out the speed limit identification optimization of the trajectory data.Then on this basis,the speed limit identification results based on road network are fused to complete the identification of speed limit information.Experiments are carried out taking the Second Ring Road area of Wuhan City and Chengdu City as examples to verify the effectiveness of the method.Experimental results show that:(1)the separate modeling of high and low speed information significantly improves the speed limit recognition accuracy based on trajectories;(2)the integration of speed limit recognition optimization results based on trajectories with speed limit recognition results based on road network also further improves the speed limit recognition results.

关 键 词：车辆轨迹 道路网信息 邻接路段 高低速信息 道路限速识别 

分 类 号：P208[天文地球—地图制图学与地理信息工程]