融合视觉特征与运动特征的众源轨迹数据道路交叉口识别方法

doi:10.11947/j.AGCS.2025.20240101

测绘学报 ›› 2025, Vol. 54 ›› Issue (1): 182-193.doi: 10.11947/j.AGCS.2025.20240101

• 地图学与地理信息 • 上一篇

融合视觉特征与运动特征的众源轨迹数据道路交叉口识别方法

唐建波¹^,²(), 胡致远¹, 彭举¹(), 夏何炎¹, 丁俊杰¹, 张玉玉¹, 梅小明¹

^1.中南大学地球科学与信息物理学院，湖南　长沙　410083
^2.湖南省地理空间信息工程技术研究中心，湖南　长沙　410007

收稿日期:2024-03-14 修回日期:2024-12-12 发布日期:2025-02-17
通讯作者: 彭举 E-mail:jianbo.tang@csu.edu.cn;daisy_pj@csu.edu.cn
作者简介:唐建波（1987—），男，博士，副教授，研究方向为时空大数据挖掘与分析。　E-mail：jianbo.tang@csu.edu.cn
基金资助:
国家自然科学基金(42430110);湖南省重点研发计划(2024AQ2026);湖南省自然科学基金(2024JJ1009);湖南省教育厅资助科研项目

A road intersection recognition method in crowdsourced trajectory data by fusing visual features and motion features

Jianbo TANG¹^,²(), Zhiyuan HU¹, Ju PENG¹(), Heyan XIA¹, Junjie DING¹, Yuyu ZHANG¹, Xiaoming MEI¹

^1.School of Geosciences and Info-physics, Central South University, Changsha 410083, China
^2.Hunan Geospatial Information Engineering and Technology Research Center, Changsha 410007, China

Received:2024-03-14 Revised:2024-12-12 Published:2025-02-17
Contact: Ju PENG E-mail:jianbo.tang@csu.edu.cn;daisy_pj@csu.edu.cn
About author:TANG Jianbo (1987—), male, PhD, associate professor, majors in spatio-temporal big data mining and analysis.　E-mail: jianbo.tang@csu.edu.cn
Supported by:
The National Natural Science Foundation of China(42430110);Funds of the Science and Technology Innovation Program of Hunan Province(2024AQ2026);Hunan Provincial Natural Science Foundation of China(2024JJ1009);Scientific Research Fund of Hunan Provincial Education Department

摘要/Abstract

摘要：

随着移动定位技术的快速发展，众源车辆轨迹数据已成为导航路网地图构建与实时更新的重要数据源。道路交叉口是路网地图和路径规划的关键结点，准确识别轨迹数据中的道路交叉口是基于众源轨迹数据构建导航路网地图的重要基础。目前基于众源轨迹数据的道路交叉口识别方法主要分为基于运动特征、视觉特征和深度学习的方法。然而，由于交叉口形状、大小的差异性及轨迹数据密度分布的异质性，采用单一策略或方法难以满足不同轨迹数据场景下（如轨迹稀疏区域和交叉口分布密集区域）的道路交叉口准确完整提取，而导致交叉口的漏提取或错误识别等问题。为此，本文基于组合优化思想，提出了一种融合视觉特征与运动特征的众源轨迹数据道路交叉口识别方法。该方法在提取车辆运动特征识别道路交叉口的基础上，结合人类在观察轨迹数据时的视觉认知过程，通过融合轨迹数据的运动特征与视觉特征，实现不同复杂场景下的道路交叉口识别与结果优化。采用成都市网约车轨迹数据和武汉市出租车轨迹数据进行试验与对比分析，结果表明相比于现有代表性方法，本文方法对道路交叉口识别精度和召回率均具有显著提升。

关键词: 道路交叉口, 众源轨迹数据, 数据稀疏, 特征融合, 路网构建

Abstract:

With the rapid development of mobile positioning technology, crowdsourced vehicle trajectory data has become an important data source for map construction and real-time update of road network maps. Road intersections are the key nodes of a road network in path planning. Accurate identification of road intersections in trajectory data is an important basis for constructing navigation road maps based on crowdsource trajectory data. At present, the road intersection recognition methods based on crowdsourced trajectory data are mainly divided into motion feature-based methods, visual feature-based methods, and deep learning-based methods. Due to the differences in the shape and size of intersections and the heterogeneity of the density distribution of crowdsourced trajectory data, it is still difficult to extract road intersections accurately and completely under different data scenarios (such as areas with sparse data and areas containing dense distributed intersections) by using a single strategy and method, which leads to problems such as omission or wrong recognition of intersections. Therefore, based on the idea of combinatorial optimization, this paper proposes a road intersection recognition method in crowdsourced trajectory data by fusing visual features and motion features. This method first extracts vehicle motion features to recognize road intersections, and then mimics human visual cognitive process to realize road intersection recognition in different complex scenes by fusing motion features and visual features. Experimental results on trajectory datasets in Chengdu and Wuhan show that compared with the existing representative methods, the proposed method has significantly improved the accuracy and recall rate of road intersection recognition.

Key words: road intersection, crowdsourced trajectory data, data sparsity, feature fusion, road network construction

中图分类号:

P208

唐建波, 胡致远, 彭举, 夏何炎, 丁俊杰, 张玉玉, 梅小明. 融合视觉特征与运动特征的众源轨迹数据道路交叉口识别方法[J]. 测绘学报, 2025, 54(1): 182-193.

Jianbo TANG, Zhiyuan HU, Ju PENG, Heyan XIA, Junjie DING, Yuyu ZHANG, Xiaoming MEI. A road intersection recognition method in crowdsourced trajectory data by fusing visual features and motion features[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(1): 182-193.

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研究区	指标	基于视觉特征的识别方法^[25]	基于转向角权重的均值漂移聚类方法^[35]	基于运动特征的识别方法^[17]	密度峰值聚类与数学形态学融合的方法^[27]	转向点对聚类方法^[5]	VM-UNet^[36]	本文方法
R1	精确度/(%)	96.43	70.87	97.27	71.05	78.85	73.44	98.44
	召回率/(%)	82.44	55.73	81.68	61.83	62.60	71.76	96.18
	F₁值	0.888 9	0.623 9	0.888 0	0.661 2	0.697 9	0.725 9	0.973 0
R2	精确度/(%)	94.12	71.54	97.58	70.21	77.17	80.83	96.67
	召回率/(%)	75.17	59.06	81.21	66.44	65.77	65.10	97.32
	F₁值	0.835 8	0.647 1	0.886 4	0.682 8	0.710 1	0.721 2	0.969 9
R3	精确度/(%)	96.88	72.52	97.04	79.51	75.11	75.59	97.12
	召回率/(%)	64.05	78.51	81.40	67.36	68.60	79.34	97.52
	F₁值	0.771 1	0.754 0	0.885 4	0.729 3	0.717 1	0.774 2	0.973 2
R4	精确度/(%)	96.36	73.53	97.40	76.16	80.67	71.05	98.20
	召回率/(%)	63.10	74.40	89.29	68.45	72.02	80.36	97.62
	F₁值	0.762 6	0.739 6	0.931 7	0.721 0	0.761 0	0.754 2	0.979 1

方法	R1识别用时	R2识别用时	R3识别用时	R4识别用时
基于视觉特征的识别方法^[25]	29.81	49.63	30.87	33.09
基于转向角权重的均值漂移聚类方法^[35]	3 186.18	12 183.47	1 966.00	1 921.70
基于运动特征的识别方法^[17]	324.34	1 300.60	105.06	131.26
密度峰值聚类与数学形态学融合的识别方法^[27]	348.24	740.25	80.20	81.65
转向点对聚类方法^[5]	1 359.30	2 512.02	700.07	737.13
VM-U Net^[36]	31.72	51.52	33.28	35.79
本文方法	355.77	1 351.90	136.24	164.57

阈值	10%	20%	30%	40%	50%	60%	70%	80%	90%	95%
精确度/(%)	96.97	96.97	98.20	98.20	98.20	98.20	98.20	98.20	98.20	98.20
召回率/(%)	95.24	95.24	97.62	97.62	97.62	97.62	97.62	97.62	97.62	97.62
F₁值	0.961 0	0.961 0	0.979 1	0.979 1	0.979 1	0.979 1	0.979 1	0.979 1	0.979 1	0.979 1

融合视觉特征与运动特征的众源轨迹数据道路交叉口识别方法

A road intersection recognition method in crowdsourced trajectory data by fusing visual features and motion features

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