A Road Map Refinement Method Using Delaunay Triangulation for Big Trace Data

Tang, Luliang; Ren, Chang; Liu, Zhang; Li, Qingquan

doi:10.3390/ijgi6020045

Cited by 36 publications

(23 citation statements)

References 27 publications

(57 reference statements)

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“…Bierlaire et al [26] developed a statistical map matching method that estimated the conditional probability of a trajectory with the consideration of both point coordinates and temporal information. Tang et al [27] employed Delaunay triangulation and a weighted skeleton for incremental road map construction from low-frequency vehicle trajectories. He et al [28] presented a road map inference method that utilizes the long-term observation of vehicle trajectories to create accurate road networks in dense urban regional and intricate intersections.…”

Section: Related Workmentioning

confidence: 99%

Road Network Extraction from Low-Frequency Trajectories Based on a Road Structure-Aware Filter

2019

IJGI

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Many studies have utilized global navigation satellite system (such as global positioning system (GPS)) trajectories in order to successfully infer road networks because such data can reveal the geometry and development of a road network, can be obtained in a timely manner, and updated on a low budget. Unfortunately, existing studies for inferring road networks from vehicle traces suffer from low accuracy, especially in dense urban regions and locations with complex structures, such as roundabouts, overpasses, and complex intersections. This study presents a novel two-stage approach for inferring road networks from trajectory points and capturing road geometry with better accuracy. First, a lane structure-aware filter is proposed to cluster vehicle trajectories influenced by high noise and outliers in order to reveal the continuous structure points of lane curves from massive trajectory points. Second, a road tracing operator is utilized to segment the road network geometry by inserting new vertices and segments to a vigorous vertex in the heading of the structure points that are extracted in the first step. Experimental results demonstrate the increased accuracy of the extracted roads and show that the proposed method exhibits strong robustness to noise and various sampling rates.

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Section: Related Workmentioning

confidence: 99%

Road Network Extraction from Low-Frequency Trajectories Based on a Road Structure-Aware Filter

2019

IJGI

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“…Identification of the road boundary is also critical in the field of geographic information and plays a vital role in mapping, public management and road data updates. Many documents propose using GPS trajectories to extract and update road data, which generally involves using clustering [6][7][8], kernel density estimation [9,10], trajectory merging [11,12] and geometric modelling [13,14]. However, these methods extract road boundaries by analyzing and processing a large number of GPS trajectories, which are generally of meter-level accuracy [15].…”

Section: Introductionmentioning

confidence: 99%

“…According to actual construction requirements, the length of Pavement Construction Area (PCA) boundaries is generally only about 50 m and the width is only 10 to 20 m, but the positioning needs to be accurate to 1 decimeter. In addition, three types 8], kernel density estimation [9,10], trajectory merging [11,12] and geometric modelling [13,14]. However, these methods extract road boundaries by analyzing and processing a large number of GPS trajectories, which are generally of meter-level accuracy [15].…”

Section: Introductionmentioning

confidence: 99%

Bidirectional Sliding Window for Boundary Recognition of Pavement Construction Area Using GPS-RTK

Xu¹,

Chen²,

Wang³

et al. 2020

Applied Sciences

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Unmanned pavement construction is of great significance in China, and the primary issue to be solved is how to identify the boundaries of the Pavement Construction Area (PCA). In this paper, we present a simple yet effective method, named the Bidirectional Sliding Window (BSW) method, for PCA boundary recognition. We first collected the latitude and longitude coordinates of the four vertices of straight quadrilaterals using the Global Positioning System-Real Time Kinematic (GPS-RTK) measurement principle for precise single-point positioning, analyzed single-point positioning accuracy, and determined the measurement error distribution models. Next, we took points at equal intervals along one straight line segment and two curved line segments with curvature radii of 70 m to 300 m, for simulation experiments. BSW was adopted to recognize the Possible Irrelevant Points (PIP) and Relevant Points (RP), which were used to identify PCA boundaries. Experiments show that when the proposed BSW algorithm is used and the single-point positioning accuracy is at the centimeter level, PCA boundary recognition for straight polygons reaches single-point positioning accuracy, and that for curved polygons reaches decimeter-level accuracy.

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“…That uses significant human and financial resources to update the road maps over time. Public vehicles such as taxis or buses are natural sensors of road network information [2]. With more and more public vehicles are being equipped with GPS devices, a large amount of GPS trajectory data are now available.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Method for Detection and Update of Additive Changes in Road Network with GPS Trajectory Data

Tang

Deng

Huang

et al. 2019

IJGI

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Ubiquitous trajectory data provides new opportunities for production and update of the road network. A number of methods have been proposed for road network construction and update based on trajectory data. However, existing methods were mainly focused on reconstruction of the existing road network, and the update of newly added roads was not given much attention. Besides, most of existing methods were designed for high sampling rate trajectory data, while the commonly available GPS trajectory data are usually low-quality data with noise, low sampling rates, and uneven spatial distributions. In this paper, we present an automatic method for detection and update of newly added roads based on the common low-quality trajectory data. First, additive changes (i.e., newly added roads) are detected using a point-to-segment matching algorithm. Then, the geometric structures of new roads are constructed based on a newly developed decomposition-combination map generation algorithm. Finally, the detected new roads are refined and combined with the original road network. Seven trajectory data were used to test the proposed method. Experiments show that the proposed method can successfully detect the additive changes and generate a road network which updates efficiently.

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A Road Map Refinement Method Using Delaunay Triangulation for Big Trace Data

Cited by 36 publications

References 27 publications

Road Network Extraction from Low-Frequency Trajectories Based on a Road Structure-Aware Filter

Road Network Extraction from Low-Frequency Trajectories Based on a Road Structure-Aware Filter

Bidirectional Sliding Window for Boundary Recognition of Pavement Construction Area Using GPS-RTK

An Automatic Method for Detection and Update of Additive Changes in Road Network with GPS Trajectory Data

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