路段级导航属性信息挖掘

doi:10.11947/j.AGCS.2024.20220649

测绘学报 ›› 2024, Vol. 53 ›› Issue (2): 367-378.doi: 10.11947/j.AGCS.2024.20220649

路段级导航属性信息挖掘

张彩丽¹, 向隆刚², 李雅丽³, 高松峰¹, 潘传姣¹

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

收稿日期:2022-11-15 修回日期:2023-03-23 发布日期:2024-03-08
通讯作者: 向隆刚 E-mail:geoxlg@whu.edu.cn
作者简介:张彩丽(1989-),女,博士,讲师,研究方向为轨迹数据挖掘、可导航路网构建。E-mail:cailizhang@whu.edu.cn
基金资助:
河南省高等学校重点科研项目（24B420002）；国家自然科学基金（41771474；42071432）

Road section navigation attribute mining

ZHANG Caili¹, XIANG Longgang², LI Yali³, GAO Songfeng¹, PAN Chuanjiao¹

1. School of Surveying and Urban Spatial Information, Henan University of Urban Construction, Pingdingshan 467000, China;
2. State Key Lab of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
3. School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang 110000, China

Received:2022-11-15 Revised:2023-03-23 Published:2024-03-08
Supported by:
The Key Research Projects of Higher Education Institutions in Henan Province (No. 24B420002); The National Natural Science Foundation of China (Nos. 41771474;42071432)

摘要/Abstract

摘要： 大量的自动或半自动道路提取方法如雨后春笋，但生成的产品通常缺乏导航属性信息，如路段的等级、限速等，制约“大路优先”“限速提醒”等智能导航服务。因此，本文以路段为分析单元，考虑上下游邻接路段强相关性，提出一种改进的路段等级、限速属性信息挖掘方法。首先进行轨迹、路网数据预处理，实现轨迹点与归属路段的连接。然后基于对数据和任务的认识进行多模态道路互补特征设计。最后顾及目标路段及其上下游邻接信息，利用随机森林开展面向路段的等级、限速信息分析。与单类特征相比，集成路网与轨迹特征之后提高了路段等级、限速分类准确率。与仅顾及目标路段进行路段等级、限速分类相比，顾及空间邻接信息进行路段等级、限速分类效果更好。

关键词: 众源轨迹, 导航属性挖掘, 多模特征融合, 空间邻接信息, 道路等级识别, 道路限速识别

Abstract: A large number of automatic or semi-automatic road extraction methods have sprung up, but the generated products usually lacked navigation attribute information, such as road hierarchies, road speed limits, etc., which restrict intelligent navigation services such as "main road priority" and "speed limit alert". Therefore, taking road sections as the unit of analysis and considering the strong correlation between adjacent upstream and downstream road sections, a method for mining attribute information of road hierarchies and road speed limits was proposed. First, we preprocessed tracks and road networks and realized the connection between track points and road sections. Then, multi-modal features were designed based on the understanding of the data and the task. Finally, the random forest was used to recognize road hierarchies and road speed limits, taking into account the information of the target road segments and their upstream and downstream adjacency information. Compared with single-class features, the integration features of road networks and trajectories improve the classification accuracy of road hierarchies and road speed limits; compared with the classification of road hierarchies and road speed limits considering only the target road segment, the classification of road hierarchies and road speed limits considering spatial adjacency information is more effective.

Key words: crowd-sourced trajectories, navigation attribute mining, multi-mode feature fusion, spatial adjacency information, road hierarchies recognition, road speed limits recognition

中图分类号:

P208

张彩丽, 向隆刚, 李雅丽, 高松峰, 潘传姣. 路段级导航属性信息挖掘[J]. 测绘学报, 2024, 53(2): 367-378.

ZHANG Caili, XIANG Longgang, LI Yali, GAO Songfeng, PAN Chuanjiao. Road section navigation attribute mining[J]. Acta Geodaetica et Cartographica Sinica, 2024, 53(2): 367-378.

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路段级导航属性信息挖掘

Road section navigation attribute mining

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