A fitted B-spline method to derive a representative 3D axis from a set of multiple road traces

Reinoso, J. F.; López, Francisco Javier Ariza; Barrera, D.; Gómez-Blanco, A.; Romero-Záliz, Rocío

doi:10.1080/10106049.2015.1086902

Cited by 6 publications

(8 citation statements)

References 13 publications

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“…This incremental manner of constructing a road map from scratch has been continuously improved; using a curve-graph matching distance measure and minimum-link representative edge, Ahmed et al [17] proposed a map construction method with theoretical error estimation. Some of the methods reviewed [17] are capable of handling three-dimensional input traces, while there are also dedicated methods for extracting three-dimensional road maps [18]. It is beyond the scope of this paper to incrementally construct the two-dimensional road map from low-frequency data and so is not discussed.…”

Section: Methodsmentioning

confidence: 99%

“…Some of the methods reviewed [17] are capable of handling three-dimensional input traces, while there are also dedicated methods for extracting three-dimensional road maps [18]. It is beyond the scope of this paper to incrementally construct the two-dimensional road map from low-frequency data and so is not discussed.…”

Section: Related Workmentioning

confidence: 99%

“…Each edge between neighboring track points is supposed to be a valid sample of the street. However, this is often challenged or has not held true for data sources like practical vehicle tracking applications, since strategies for data collection, transmission, and storage yield a relatively low time resolution for sampled locations [18]. A map construction algorithm for big trace data should also consider this factor when producing a robust inference of the road geometry.…”

Section: Introductionmentioning

confidence: 99%

“…A vast body of literature addressing the problem of road map construction from vehicle tracking data currently exists [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], which can be divided into two categories in terms of the form of data input, offline and online algorithms. These methods take a full collection or a continuous stream of data as input, respectively, which will be detailed in the following section.…”

Section: Introductionmentioning

confidence: 99%

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

Tang

Ren

Liu

et al. 2017

IJGI

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Abstract:With the rapid development of urban transportation, people urgently need high-precision and up-to-date road maps. At the same time, people themselves are an important source of road information for detailed map construction, as they can detect real-world road surfaces with GPS devices in the course of their everyday life. Big trace data makes it possible and provides a great opportunity to extract and refine road maps at relatively low cost. In this paper, a new refinement method is proposed for incremental road map construction using big trace data, employing Delaunay triangulation for higher accuracy during the GPS trace stream fusion process. An experiment and evaluation were carried out on the GPS traces collected by taxis in Wuhan, China. The results show that the proposed method is practical and improves upon existing incremental methods in terms of accuracy.

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Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Tang

Ren

Liu

et al. 2017

IJGI

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“…However, these sources are limited (e.g., focused on a single domain, limited spatial coverage) and are updated slowly due to insufficient user participation [5,6]. Another option is to extract POI data from global positioning system (GPS) trajectory data [7][8][9][10][11]. At present, extracting and updating spatial data using collections of GPS traces is a relatively new area of research, and these works include map updates [8], data extraction such as road data [9,10], POI data [4,7], and activity behavior analysis [7,11].…”

Section: Introductionmentioning

confidence: 99%

POI Information Enhancement Using Crowdsourcing Vehicle Trace Data and Social Media Data: A Case Study of Gas Station

Yang

2018

IJGI

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Abstract:Points of interest (POIs) such as stores, gas stations, and parking lots are particularly important for maps. Using gas station as a case study, this paper proposed a novel approach to enhance POI information using low-frequency vehicle trajectory data and social media data. First, the proposed method extracted spatial information of the gas station from sparse vehicle trace data in two steps. The first step proposed the velocity sequence linear clustering algorithm to extract refueling stop tracks from the individual trace line after modeling the vehicle refueling stop behavior using movement features. The second step used the Delaunay triangulation to extract the spatial information of gas stations from the collective refueling stop tracks. Second, attribute information and dimension sentiment semantic information of the gas station were extracted from social media data using the text mining method and tripartite graph model. Third, the gas station information was enhanced by fusing the extracted spatial data and semantic data using a matching method. Experiments were conducted using the 15-day vehicle trajectories of 12,000 taxis and social media data from the Dazhongdianping in Beijing, China, and the results showed that the proposed method could extract the spatial information, attribute information, and review information of gas stations simultaneously. Compared with ground truth data, the automatically enhanced gas station was proved to be of higher quality in terms of the correctness, completeness, and real-time.

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An iterative method for obtaining a mean 3D axis from a set of GNSS traces for use in positional controls

Mozas‐Calvache

López

2016

Survey Review

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A fitted B-spline method to derive a representative 3D axis from a set of multiple road traces

Cited by 6 publications

References 13 publications

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

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

POI Information Enhancement Using Crowdsourcing Vehicle Trace Data and Social Media Data: A Case Study of Gas Station

An iterative method for obtaining a mean 3D axis from a set of GNSS traces for use in positional controls

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