基于众源轨迹的OSM路网转向信息增强

doi:10.12082/dqxxkx.2023.230070

摘要/Abstract

摘要：

OpenStreetMap（OSM）路网数据是一个开放性的数据集，旨在为全球用户提供免费的数字地图资源，但路口转向信息的缺失，成为制约其进一步服务于车辆导航和路径规划的瓶颈。为此，本文提出一种基于地图匹配和字符串映射的路口转向探测方法，通过挖掘众源GNSS轨迹数据在交通路口的动态连接信息，为OSM路网结构赋予转向关系。首先，基于一种自上而下的四叉树分裂思想，设计了OSM路口结构探测方法，进而将路口结构简化为一个连接点；在此基础上改进HMM地图匹配算法，识别漂移异常的轨迹序列，从而将低频、高噪的众源轨迹投影到OSM路段上；接着，引入面向路口的路段字符编码技术，将路口相关的轨迹映射为转向过程中的方向字符串，进一步借助于最优路径分析思想，设计了空字符的信息增强处理方法还原低频轨迹行驶的路线信息，以有效提高短路段的轨迹支持度；最后，直接针对轨迹方向字符串，通过字符串匹配挖掘轨迹在目标路口的转向类别，实现OSM路网的转向信息增强。本文将复杂的路口转向关系识别转化为简单的字符串匹配，基于上海市数据的试验表明，该方法可以识别结构与大小各异的路口转向关系，其精确率达到90%，召回率超过98%，F1值超94%。

关键词: 转向关系, 地图匹配, 众源轨迹数据, 字符编码, 字符匹配, OSM路网, 路口转向信息增强, 路径分析

Abstract:

OpenStreetMap (OSM) road network is an open-source dataset that is dedicated to providing people with a globe-free digital map resource, and it has been widely used in spatial analysis and applications such as route planning and vehicle navigation services. Meanwhile, in order to regulate traffic order and reduce heavy traffic congestion, the constraints of turning rules are generally implemented at intersections in urban areas. These constraints should be respected in the applications based on OSM road network. However, OSM road network lacks turning relationships at intersections, preventing its services from route planning and vehicle navigation. For this reason, to endow OSM road network structure with turning relationships, this study presents an intersection turning detection method based on map matching and string mapping, which takes advantage of crowdsourcing GNSS trajectory data in terms of its dynamic connection information at traffic intersections. Firstly, a structure detection method for OSM intersections is designed based on a top-down quadtree splitting idea, then the intersections with different sizes and complex and various shapes are reduced to the connection points. On this basis, the improved Hidden Markov Model (HMM) map matching algorithm is introduced to project low-frequency and high-noise trajectories onto OSM road segments. This algorithm considers the direction consistency between roads and trajectories, as well as the effective drift distance between two adjacent trajectory points, can identify trajectory sequences with semantic anomalies during the driving process. Secondly, to simplify turning relationship detection, a character encoding technology facing the intersection-related road segments is presented to map the trajectories crossing through intersections to the directional strings in turning process. The information enhancement method regarding the empty characters based on optimal path analysis is further designed to enhance turning trajectory support for short road segments. This approach helps restore the driving route information for low-frequency trajectories. Finally, the different turn modes of trajectories at the target intersection are mined by directly targeting the trajectory directional strings based on a string matching method, thus this study realizes turning information enhancement for OSM intersections referencing to the "consensus knowledge" of crowdsourcing trajectories. The complicated turning relationship identification for OSM intersections is transformed into the simple string matching. The experiment based on crowdsourcing trajectory data in Shanghai shows that the proposed method can detect turning relationships for OSM intersections with a precision rate of 90%, a recall rate of over 98%, and an F1-score of over 94%.

Key words: turning relationships, map matching, crowdsourcing trajectory, string mapping, string matching, OSM road network, intersection's turning information enhancement, path analysis

陈欣, 向隆刚, 焦凤伟. 基于众源轨迹的OSM路网转向信息增强[J]. 地球信息科学学报, 2023, 25(10): 1954-1967.DOI:10.12082/dqxxkx.2023.230070

CHEN Xin, XIANG Longgang, JIAO Fengwei. Turning Information Enhancement of OpenStreetMap Road Network Based on Crowdsourcing Trajectory Data[J]. Journal of Geo-information Science, 2023, 25(10): 1954-1967.DOI:10.12082/dqxxkx.2023.230070

图/表 18

图1

图2

图3

图4

表1

路口的转向关系

路口	路段字符表示	转向频率	转向属性
$I N$	$l 1, l 2, l 3, l 4, l 5$ , $l 6, l 7, l 8$	( $l 1, l 2$ ):0\|…\|( $l 6, l 7$ ):88\|…	( $l 1, l 2$ )=0\|…\|( $l 6, l 7$ )=1\|…

表1

图5

图6

图7

图8

图9

表2

图10

图11

表3

图12

图13

表4

图14

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方法	Precision	Recall	F1-score
Efentakis	89.75	73.86	81.03
本文方法	90.43	96.22	93.24

转向	Precision	Recall	F1-score
左转	94.12	91.43	92.76
右转	89.74	92.10	90.90
直行	100.00	100.00	100.00
掉头	80.00	77.41	75.73

方法	评价指标	支持度阈值SUP
方法	评价指标	1	5	10	20	30	40
无信息增强处理	Precision	88.51	91.64	94.01	96.11	96.68	96.83
	Recall	94.91	80.64	70.48	57.57	49.05	42.86
	F1-score	91.60	85.79	80.56	72.01	65.08	59.42
有信息增强处理	Precision	89.02	90.29	90.43	90.90	91.73	92.43
	Recall	99.91	98.53	96.22	89.64	83.51	77.69
	F1-score	94.15	94.23	93.24	90.27	87.43	84.42