地球信息科学学报 ›› 2022, Vol. 24 ›› Issue (10): 1925-1940.doi: 10.12082/dqxxkx.2022.220218

• 地理流与出行行为 • 上一篇    下一篇

顾及路网与轨迹多模特征的道路等级分类研究

张彩丽1,2(), 向隆刚1,*(), 李雅丽1, 林志勇3   

  1. 1.武汉大学测绘遥感信息工程国家重点实验室,武汉 430079
    2.邵阳学院城乡建设学院,邵阳 422000
    3.武汉大学遥感信息工程学院,武汉 430079
  • 收稿日期:2022-04-23 修回日期:2022-05-27 出版日期:2022-10-25 发布日期:2022-12-25
  • 通讯作者: *向隆刚(1976— ),男,湖南怀化人,博士,教授,主要从事轨迹数据分析、时空大数据管理。E-mail: geoxlg@whu.edu.cn
  • 作者简介:张彩丽(1989—),女,河南驻马店人,博士,主要从事轨迹数据分析与挖掘。E-mail: cailizhang@whu.edu.cn
  • 基金资助:
    国家自然科学基金项目(41771474);国家自然科学基金项目(42071432)

A Research on Road Type Classification Considering the Multi-mode Features of Road Network and Trajectories

ZHANG Caili1,2(), XIANG Longgang1,*(), LI Yali1, LIN Zhiyong3   

  1. 1. State Key Lab of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
    2. Urban and Rural Construction College, Shaoyang University, Shaoyang 422000, China
    3. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
  • Received:2022-04-23 Revised:2022-05-27 Online:2022-10-25 Published:2022-12-25
  • Contact: XIANG Longgang
  • Supported by:
    National Natural Science Foundation of China(41771474);National Natural Science Foundation of China(42071432)

摘要:

道路等级不仅反映在路网结构的静态骨架信息上,也蕴含在轨迹数据呈现的动态语义信息上。为解决(OpenStreetMap)OSM路网部分路段及路网生成产品等级缺失问题,本文提出一种顾及路网与轨迹多模特征的道路等级分类方法。首先通过轨迹数据的清洗、地图匹配和基于路名的路网合并实现轨迹点与命名道路的联结;然后以命名道路为分析单元,综合考虑路网及轨迹数据,在系统分析路网结构的道路几何特征、道路分布特征、道路拓扑特征及道路单双向信息基础上,进一步挖掘与融合轨迹数据蕴含的道路宽度、道路车流量、道路速度等静动态特征,形成关于道路等级的描述特征集,作为识别道路等级的基础与依据;最后以随机森林(RF)为基本分类器进行特征选择及模型训练实现道路等级识别。为验证本文方法,选取武汉市汉正街区域及二环区域,基于OSM路网数据及众源轨迹数据开展试验。该方法取得了较好的分类结果,小范围汉正街区域的验证集准确率为91.2%,大范围二环区域的验证集准确率达到80.8%。与单类特征相比,集成路网与轨迹特征极大提高了道路等级分类准确率;与原始路段形式进行道路等级分类相比,以路名重构道路形式进行道路等级分类效果更好。

关键词: 智能交通, 众源轨迹数据, 多模特征融合, 命名道路, 道路等级, OSM, 道路属性更新, 随机森林

Abstract:

Path planning and vehicle navigation not only rely on the basic road network structure, but also need information such as grades to assist, to achieve navigation services such as "road priority". Road type is not only reflected in the static skeleton information of the road network, but also in the dynamic semantic information presented by the trajectories. To identify the missing road type of the road sections in the existing road network such as OpenStreetMap (OSM) and road network generation products, a road type classification method considering multi-mode features of the road network and trajectories was proposed. First, the connection between trajectory points and named roads was realized through the cleaning of trajectory data, map matching, and the merging of OSM based on names. Then, a set of descriptive features of road type was formed as the basis for identifying road type by taking the named road as the analytical unit. Specifically, based on the systematic analysis of the road geometric features, road distribution features, road topological features, and one-way and two-way information of the road network structure, we further mined and integrated the static and dynamic features of trajectories, such as width, traffic volume, speed, and so on. Finally, a Random Forest (RF) model was used as the base classifier for feature selection and model training to identify road type. In order to verify our proposed method, we selected the OSM road network and crowd-sourced trajectories in the Hanzheng Street area and the second ring area of Wuhan to carry out the experiment. Our method achieved excellent classification results, the accuracy in verification set of the small area on Hanzheng Street reached 91.2%, and the accuracy in verification set of the larger area on the second ring reached 80.8%. Compared with single-class features, integrated road network and trajectory features greatly improved the accuracy of road type identification. Compared with the road type classification in the form of the original road section, the road type identification in the form of road name reconstruction was better. Compared with existing methods, e.g., commonly used K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and other models, our proposed method achieved a higher accuracy. Feature rationality analysis also verified the effectiveness of the proposed method in this paper.

Key words: intelligent transportation, crowd-sourced trajectories, multi-modal feature fusion, named road, road type, OSM, road attribute update, random forest model