利用车载GNSS轨迹大数据的U-Turn道路结构信息获取方法

doi:10.11947/j.AGCS.2023.20220063

测绘学报 ›› 2023, Vol. 52 ›› Issue (8): 1330-1341.doi: 10.11947/j.AGCS.2023.20220063

利用车载GNSS轨迹大数据的U-Turn道路结构信息获取方法

王梓豪¹, 唐炉亮^1,2, 杨雪³, 戴领⁴, 李朝奎²

1. 武汉大学测绘遥感信息工程国家重点实验室, 湖北武汉 430079;
2. 湖南科技大学地理空间信息技术国家地方联合工程实验室, 湖南湘潭 411201;
3. 中国地质大学(武汉)地理与信息工程学院, 湖北武汉 430074;
4. 百度时代网络技术(北京)有限公司, 北京 100089

收稿日期:2022-01-25 修回日期:2022-11-18 发布日期:2023-09-07
通讯作者: 唐炉亮 E-mail:tll@whu.edu.cn
作者简介:王梓豪(1999-),男,硕士生,研究方向为轨迹大数据分析与挖掘。E-mail:zhwang_whu@163.com
基金资助:
国家自然科学基金(41971405;41671442;41901394);中央高校基本科研业务费专项资金资助

The U-Turn information collecting method using vehicle GNSS trajectory data

WANG Zihao¹, TANG Luliang^1,2, YANG Xue³, DAI Ling⁴, LI Chaokui²

1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
2. National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China;
3. School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China;
4. Baidu Inc. Beijing 100089, China

Received:2022-01-25 Revised:2022-11-18 Published:2023-09-07
Supported by:
The National Natural Science Foundation of China (Nos. 41971405; 41671442; 41901394); The Fundamental Research Funds for the Central Universities

摘要/Abstract

摘要： 随着智慧交通和精细化导航技术的迅速发展,人们对道路地图要素的覆盖度、精准度、丰富度与新鲜度需求越来越高,而U-Turn作为城市路网连通关系的重要因素,成为道路地图更新的重要内容之一。现有专业道路测绘模式对U-Turn数据存在采集成本高、更新周期长、数据处理繁等问题,导致U-Turn数据无法满足智慧交通导航需求。本文采用车载GNSS轨迹大数据,提出了一种U-Turn道路结构信息获取方法。首先通过轨迹跟踪提取车辆掉头点对与行为;然后利用DBSCAN聚类算法提取U-Turn掉头类簇;再依据U-Turn流量占比等特征构建支持向量机二分模型,自适应剔除违规掉头类簇,并区分出U-Turn结构有无通行时间限制;最后根据掉头类簇在道路结构中的分布特征,识别U-Turn位置与空间结构。试验以武汉市滴滴网约车GNSS轨迹,对江汉区183个路段采用本文方法进行探测,U-Turn结构信息识别召回率为88.3%,精确率为87.6%,位置识别的横向和纵向精度分别为3.40 m和5.90 m,试验结果表明本文方法可以有效地从车载GNSS轨迹数据中获取U-Turn的位置与结构类型,为U-Turn数据获取提供了周期短、成本低的有效解决方案。

关键词: 车载GNSS轨迹数据, U-Turn结构信息, 轨迹跟踪, 城市路网

Abstract: With the rapid development of intelligent transportation and refined navigation technology, the requirements of coverage, accuracy, richness and freshness for road maps are becoming higher and higher. As a significant element in connectivity of urban road network, U-Turn has become an important part of road data renewal. It is high-cost and long-term to update by existing professional surveying and mapping models, resulting in poor reality of U-Turn data. In this paper, using vehicle GNSS trajectory big data, an automatic U-Turn information collecting approach is proposed. The turning round point pairs and turning round behaviors are first extracted through trajectory tracking. Then, DBSCAN algorithm is used to extract turning round clusters. Next, a support vector machine model is built based on traffic flow proportion to eliminate illegal turning round behaviors. Finally, U-Turn positions and spatial structures are identified according to distribution characteristics of U-Turn clusters. Taking vehicle GNSS trajectory data of DiDi in Wuhan as an example, the experiment detects 183 road sections in Jianghan District. The recognition recall rate of U-Turn structures was 88.3%, and the precision rate was 87.6%. At the same time, the horizontal and vertical position accuracy of U-turn positions are 3.40 m and 5.90 m, respectively. Results show that the proposed method can effectively collect the position and structural category of U-Turn from vehicle GNSS trajectory big data, and can provide a promising solution for short-term and low-cost collection of U-Turn data.

Key words: vehicle GNSS trajectory data, U-Turn information, trajectory tracking, urban road network

中图分类号:

P227

王梓豪, 唐炉亮, 杨雪, 戴领, 李朝奎. 利用车载GNSS轨迹大数据的U-Turn道路结构信息获取方法[J]. 测绘学报, 2023, 52(8): 1330-1341.

WANG Zihao, TANG Luliang, YANG Xue, DAI Ling, LI Chaokui. The U-Turn information collecting method using vehicle GNSS trajectory data[J]. Acta Geodaetica et Cartographica Sinica, 2023, 52(8): 1330-1341.

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利用车载GNSS轨迹大数据的U-Turn道路结构信息获取方法

The U-Turn information collecting method using vehicle GNSS trajectory data

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