Constructing Street Networks from GPS Trajectories

Ahmed, Mahmuda; Wenk, Carola

doi:10.1007/978-3-642-33090-2_7

Cited by 99 publications

(136 citation statements)

References 8 publications

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“…Based on the experimental tests, the radius of the neighbourhood in the trajectory contraction was 30 m, and the thresholds for the segment growth process were 15 • and a distance of 30 m. For comparison and evaluation, the road inference methods of Davies [22] and Ahmed [18] were used to extract intersections from the same datasets. These two open source algorithms are implemented in Python and Java and retain the original default parameter settings over the experiments.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…Fathi et al [17] developed a classifier that was trained using shape descriptors from two temporally adjacent GPS points from the same vehicle to extract road intersections. 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.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Comparison and Discussionmentioning

confidence: 99%

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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“…The clarified track points were incrementally added to the empty road map or merged based on their distance and heading. 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.…”

Section: Related Workmentioning

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%

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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“…2017, 6, 400 3 of 15 for generating road networks have been proposed in recent years [16,17]. In general, these methods can be organized into three categories [18]: (1) point clustering [19][20][21], which assumes that the input raw data consist of a set of points that are then clustered in various ways (such as by the k-means algorithm) to obtain street segments that are finally connected to form a road network; (2) incremental track insertion [10,[22][23][24][25][26], which constructs a road network by incrementally inserting trajectory data into an initially empty graph; and (3) intersection linking [27][28][29], in which the intersection vertices of the road network are first detected and then linked together by recognizing suitable road segments. Some of the representative algorithms of each category are listed in Table 1.…”

Section: Literature Reviewmentioning

confidence: 99%

An Automatic Road Network Construction Method Using Massive GPS Trajectory Data

Zhang

Liu

Qian

et al. 2017

IJGI

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Automatically acquiring comprehensive, accurate, and real-time mapping information and translating this information into digital maps are challenging problems. Traditional methods are time consuming and costly because they require expensive field surveying and labor-intensive post-processing. Recently, the ubiquitous use of positioning technology in vehicles and other devices has produced massive amounts of trajectory data, which provide new opportunities for digital map production and updating. This paper presents an automatic method for producing road networks from raw vehicle global positioning system (GPS) trajectory data. First, raw GPS positioning data are processed to remove noise using a newly proposed algorithm employing flexible spatial, temporal, and logical constraint rules. Then, a new road network construction algorithm is used to incrementally merge trajectories into a directed graph representing a digital map. Furthermore, the average road traffic volume and speed are calculated and assigned to corresponding road segments. To evaluate the performance of the method, an experiment was conducted using 5.76 million trajectory data points from 200 taxis. The result was qualitatively compared with OpenStreetMap and quantitatively compared with two existing methods based on the F-score. The findings show that our method can automatically generate a road network representing a digital map.

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Constructing Street Networks from GPS Trajectories

Cited by 99 publications

References 8 publications

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

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

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

An Automatic Road Network Construction Method Using Massive GPS Trajectory Data

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