顾及路网与轨迹多模特征的导航属性关联关系分析

doi:10.13474/j.cnki.11-2246.2025.0420

测绘通报 ›› 2025, Vol. 0 ›› Issue (4): 120-126.doi: 10.13474/j.cnki.11-2246.2025.0420

• 学术研究 • 上一篇

顾及路网与轨迹多模特征的导航属性关联关系分析

张彩丽¹, 向隆刚², 李雅丽³, 周雨石¹, 刘珍⁴, 鲁春阳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全国重点实验室, 湖北武汉 430000;
3. 沈阳建筑大学交通与测绘工程学院, 辽宁沈阳 110000;
4. 云南省建设投资控股集团有限公司, 云南昆明 650000

收稿日期:2024-08-30 发布日期:2025-04-28
通讯作者: 李雅丽。E-mail:liyali@whu.edu.cn
作者简介:张彩丽(1989—),女,博士,讲师,主要从事轨迹数据分析与挖掘。E-mail:cailizhang@whu.edu.cn
基金资助:
河南城建学院博士科研启动金资助项目(K-Q2023032);河南省高等学校重点科研项目(24B420002);河南城建学院高等教育教学改革研究与实践项目(2024JG158);辽宁省教育厅青年项目(LJ212410153040); 河南省自然科学基金资助项目(252300420836);国家自然科学基金(42471460);河南省科技攻关项目(242102320345);河南省软科学项目(242400410624);河南省哲学社会科学规划项目(2022BJJ026)

Analysis of the relationship among road navigation attributes based on multi-model features of road networks and crowd-sourced trajectories

ZHANG Caili¹, XIANG Longgang², LI Yali³, ZHOU Yushi¹, LIU Zhen⁴, LU Chunyang¹

1. School of Surveying and Urban Spatial Information, Henan University of Urban Construction, Pingdingshan 467000, China;
2. State Key Laboratory of information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430000, China;
3. School of Transportation and Geomatics Engineering, Shenyang Jianzhu University, Shenyang 110000, China;
4. Yunnan construction and investment holding group Co., Ltd., Kunming 650000, China

Received:2024-08-30 Published:2025-04-28

摘要/Abstract

摘要： 道路网的几何和拓扑信息固然重要,但缺乏道路等级、车道数和限速等导航属性信息将严重制约路径规划、车辆导航、位置服务等道路网核心应用的实施。本文在道路网静态结构特征基础上,顾及轨迹数据动态连接特征,以分层、渐进的方式探索道路段等级、限速及车道数之间的关联关系,挖掘了一些潜在的道路等级、车道数和限速的分类方法。首先,对轨迹和路网进行了预处理,实现轨迹点与路段之间的连接;然后,以路段为分析单元,设计路网和轨迹的多元多阶特征;最后,分析总结可能的道路等级、车道数和限速分层递进识别方法。以武汉和西安为例的试验结果表明,本文的探索具有一定的参考价值。

关键词: 智能交通, 众源轨迹数据, 多模特征融合, 导航属性信息, 层级关系, 道路邻接信息

Abstract: Geometrical and topological information about the road network is certainly important,but navigation attribute information such as road classes,number of lanes,and speed limits is also essential for the implementation of core road network applications,and route planning,vehicle navigation,and location services are typical cases. This research explores the hierarchical relationship among these three attributes and proposes potential multi-modal progressive classification methods considering upstream and downstream information for predicting road classes,number of lanes,and speed limits of road sections. First,we preprocessed trajectories and road networks and realized the connection between track points and road sections; then,we took a road section as the analysis unit and mine the multivariate and multi-order features of road networks and crowd-sourced trajectories; finally,potential methods are summarized and analyzed,and these methods are based on random forest algorithm that integrate these complementary features of current and adjacent road sections and considers hierarchical information to identify road classes,number of lanes,and speed limits based on the voting method. Experimental results in Wuhan and Xian show that our exploration has a certain reference value.

Key words: intelligent transportation, crowd-sourced trajectories, multi-mode feature fusion, road navigation attributes, hierarchical information, road adjacency information

中图分类号:

P208

张彩丽, 向隆刚, 李雅丽, 周雨石, 刘珍, 鲁春阳. 顾及路网与轨迹多模特征的导航属性关联关系分析[J]. 测绘通报, 2025, 0(4): 120-126.

ZHANG Caili, XIANG Longgang, LI Yali, ZHOU Yushi, LIU Zhen, LU Chunyang. Analysis of the relationship among road navigation attributes based on multi-model features of road networks and crowd-sourced trajectories[J]. Bulletin of Surveying and Mapping, 2025, 0(4): 120-126.

参考文献

[1] 徐智邦.结合POI数据的城市道路网自动选取方法研究[D].兰州:兰州交通大学,2018.
[2] YIN Y F,VARADARAJAN J,WANG G F,et al.A multi-task learning framework for road attribute updating via joint analysis of map data and GPS traces[C]//Proceedings of 2020 Web Conference.Taipei:[s.n.],2020.
[3] LI J,QIN Q M,HAN J W,et al.Mining trajectory data and geotagged data in social media for road map inference[J].Transactions in GIS,2014,19(1):1-18.
[4] 廖律超,蒋新华,林铭榛,等.基于交通轨迹数据挖掘的道路限速信息识别方法[J].交通运输工程学报,2015,15(5):118-126.
[5] VAN W K,BILJECKI F,STEFAN V D S.Automatic update of road attributes by mining GPS tracks[J].Transactions in GIS,2016,20(5):664-683.
[6] WAGSTAFF K,CARDIE C,Rogers S,et al.Constrained K-means clustering with background knowledge[C]//Proceedings of the 18th International Conference on Machine Learning.Williamstown,MA:[s.n.],2001: 577-584.
[7] UDUWARAGODA E,PERERA A S,DIAS S A D.Generating lane level road data from vehicle trajectories using kernel density estimation[C]//Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems.The Hague:IEEE,2013: 384-391.
[8] 唐炉亮,杨雪,阚子涵,等.一种基于朴素贝叶斯分类的车道数量探测[J].中国公路学报,2016,29(3):116-123.
[9] CHEN Y H,KRUMM J.Probabilistic modeling of traffic lanes from GPS traces[C]//Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems.New York:ACM Press,2010: 81-88.
[10] 唐炉亮,杨雪,靳晨,等.基于约束高斯混合模型的车道信息获取[J].武汉大学学报(信息科学版),2017,42(3): 341-347.
[11] KNOOP V L,DE BAKKER P F,TIBERIUS C C J M,et al.Lane determination with GPS precise point positioning[J].IEEE Transactions on Intelligent Transportation Systems,2017,18(9): 2503-2513.
[12] SHU J G,WANG S L,JIA X H,et al.Efficient lane-level map building via vehicle-based crowdsourcing[J].IEEE Transactions on Intelligent Transportation Systems,2022,23(5): 4049-4062.
[13] YUAN M Y,YUE P,YANG C,et al.Generating lane-level road networks from high-precision trajectory data with lane-changing behavior analysis[J].International Journal of Geographical Information Science,2024,38(2): 243-273.
[14] FAN L,ZHANG J F,WAN C P,et al.Lane-level road map construction considering vehicle lane-changing behavior[J].Journal of Advanced Transportation,2022,2022: 6040122.
[15] LI X L,WU Y Z,TAN Y B,et al.Method based on floating car data and gradient-boosted decision tree classification for the detection of auxiliary through lanes at intersections[J].ISPRS International Journal of Geo-Information,2018,7(8): 317.
[16] BARMPOUNAKIS E,SAUVIN G M,GEROLIMINIS N.Lane detection and lane-changing identification with high-resolution data from a swarm of drones[J].Transportation Research Record: Journal of the Transportation Research Board,2020,2674(7): 1-15.
[17] ZHAO W M,RONCOLI C.A constrained spectral clustering method for lane identification using trajectory data[J].Transportation Research Part C: Emerging Technologies,2023,155: 104270.
[18] LI X L,ZHANG Y,XIANG L G,et al.Urban road lane number mining from low-frequency floating car data based on deep learning[J].ISPRS International Journal of Geo-Information,2023,12(11): 467.
[19] 张彩丽,向隆刚,李雅丽,等.顾及路网与轨迹多模特征的道路等级分类研究[J].地球信息科学学报,2022,24(10): 1925-1940.
[20] 张彩丽,向隆刚,李雅丽,等.路段级导航属性信息挖掘[J].测绘学报,2024,53(2): 367-378.
[21] YIN Y F,TRAN A,ZHANG Y,et al.Multimodal fusion of satellite images and crowdsourced GPS traces for robust road attribute detection[C]//Proceedings of the 29th International Conference on Advances in Geographic Information Systems.Beijing:ACM Press,2021:107-116.

顾及路网与轨迹多模特征的导航属性关联关系分析

Analysis of the relationship among road navigation attributes based on multi-model features of road networks and crowd-sourced trajectories

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 2

编辑推荐

Metrics

本文评价

[1]	滕文鑫, 张建辰, 邵杰. 集成时空邻近与卷积网络车道级高精度定位算法[J]. 测绘通报, 2022, 0(6): 1-5,61.
[2]	段宗涛, 霍明生, 康军. 一种改进的轨迹地图匹配算法[J]. 测绘通报, 2018, 0(5): 77-81.