Detecting Road Intersections from Coarse-gained GPS Traces Based on Clustering

Jing, Wu; Zhu, Yanbo; Ku, Tao; Wang, Liang

doi:10.4304/jcp.8.11.2959-2965

Cited by 36 publications

(22 citation statements)

References 13 publications

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“…The intuition behind the selection of these two measures as turn indicators, as authors explain, is that when a vehicle turns it reduces its speed and changes its direction. Wu et al (2013) describe the intersections as transport hubs and propose a method for detecting them in order afterwards to use them for building and updating maps by harnessing coarsegained GPS traces. Similarly to Karagiorgou and Pfoser method, turning points are recognised (heading change greater than 45 o ) and used for defining converging points (see pp.…”

Section: Existing Methods For Intersection Detectionmentioning

confidence: 99%

Intersection Detection Based on Qualitative Spatial Reasoning on Stopping Point Clusters

Zourlidou¹,

Sester²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The purpose of this research is to propose and test a method for detecting intersections by analysing collectively acquired trajectories of moving vehicles. Instead of solely relying on the geometric features of the trajectories, such as heading changes, which may indicate turning points and consequently intersections, we extract semantic features of the trajectories in form of sequences of stops and moves. Under this spatiotemporal prism, the extracted semantic information which indicates where vehicles stop can reveal important locations, such as junctions. The advantage of the proposed approach in comparison with existing turning-points oriented approaches is that it can detect intersections even when not all the crossing road segments are sampled and therefore no turning points are observed in the trajectories. The challenge with this approach is that first of all, not all vehicles stop at the same locationthus, the stop-location is blurred along the direction of the road; this, secondly, leads to the effect that nearby junctions can induce similar stop-locations. As a first step, a density-based clustering is applied on the layer of stop observations and clusters of stop events are found. Representative points of the clusters are determined (one per cluster) and in a last step the existence of an intersection is clarified based on spatial relational cluster reasoning, with which less informative geospatial clusters, in terms of whether a junction exists and where its centre lies, are transformed in more informative ones. Relational reasoning criteria, based on the relative orientation of the clusters with their adjacent ones are discussed for making sense of the relation that connects them, and finally for forming groups of stop events that belong to the same junction.

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Section: Existing Methods For Intersection Detectionmentioning

confidence: 99%

Intersection Detection Based on Qualitative Spatial Reasoning on Stopping Point Clusters

Zourlidou¹,

Sester²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Map update refer both to the road network itself and the various features that come on top of the latter. Dozens of studies have been focused on the automatic generation of the road network from GPS tracks [31][32][33][34][35] and on subtopics that referred to the latter as main topic, such as intersection detection [36][37][38][39]. However, that interest is not uniform for the map feature categories that also need to be automatically updated.…”

Section: Introductionmentioning

confidence: 99%

Traffic Regulator Detection and Identification from Crowdsourced Data—A Systematic Literature Review

Zourlidou¹,

Sester²

2019

IJGI

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Mapping with surveying equipment is a time-consuming and cost-intensive procedure that makes the frequent map updating unaffordable. In the last few years, much research has focused on eliminating such problems by counting on crowdsourced data, such as GPS traces. An important source of information in maps, especially under the consideration of forthcoming self-driving vehicles, is the traffic regulators. This information is largely lacking in maps like OpenstreetMap (OSM) and this article is motivated by this fact. The topic of this systematic literature review (SLR) is the detection and recognition of traffic regulators such as traffic lights (signals), stop-, yield-, priority-signs, right of way priority rules and turning restrictions at intersections, by leveraging non imagery crowdsourced data. More particularly, the aim of this study is (1) to identify the range of detected and recognised regulatory types by crowdsensing means, (2) to indicate the different classification techniques that can be used for these two tasks, (3) to assess the performance of different methods, as well as (4) to identify important aspects of the applicability of these methods. The two largest databases of peer-reviewed literature were used to locate relevant research studies and after different screening steps eleven articles were selected for review. Two major findings were concluded—(a) most regulator types can be identified with over 80% accuracy, even using heuristic-driven approaches and (b) under the current progress on the field, no study can be reproduced for comparative purposes nor can solely rely on open data sources due to lack of publicly available datasets and ground truth maps. Future research directions are highlighted as possible extensions of the reviewed studies.

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“…Ahmed et al [18] reconstructed intersections by utilizing sets of vertices within bounded regions (vertex regions), with regions bounded by the minimum incident angle of the streets at that intersection. Based on sparse GPS trace points, Wu et al [19] converged low-quality raw points using Kernel Density Estimation (KDE) to identify cluster centres as intersection points. Xie et al [20] extracted intersections using the inverse distance-weighted clustering method, and intersection points were identified from GPS points with changing directions.…”

Section: Related Workmentioning

confidence: 99%

Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads

Xing

et al. 2017

IJGI

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Many studies have used Global Navigation Satellite System (GNSS) traces to successfully extract segments of road networks because such data can be rapidly updated at a low cost. However, most studies have not focused on extracting intersections, which are indispensable parts of road networks in terms of connectivity. However, extracted intersections often present unsatisfactory precision and misleading connectivity. This study proposes a novel method for extracting road intersections from Global Position System (GPS) trace points and for capturing intersections with better accuracy. The key to improving the geometric accuracy of intersections is to identify the dominant orientations of road segments around intersections, merge similar orientations and maintain independent conflicting orientations. Extracting intersections by aligning the dominant orientations can largely reduce location offsets and road distortions. Experiments are performed to demonstrate the increased accuracy and connectivity of extracted road intersections by the proposed method.

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Detecting Road Intersections from Coarse-gained GPS Traces Based on Clustering

Cited by 36 publications

References 13 publications

Intersection Detection Based on Qualitative Spatial Reasoning on Stopping Point Clusters

Intersection Detection Based on Qualitative Spatial Reasoning on Stopping Point Clusters

Traffic Regulator Detection and Identification from Crowdsourced Data—A Systematic Literature Review

Extraction of Road Intersections from GPS Traces Based on the Dominant Orientations of Roads

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