Automatic generation of road network map from massive GPS, vehicle trajectories

Shi, Wenjing; Shen, Shuhan; Liu, Yuncai

doi:10.1109/itsc.2009.5309871

Cited by 104 publications

(76 citation statements)

References 11 publications

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“…1) Rasterization method: This type of research mainly convert GPS tracks to a raster map and then use digital image processing methods such as mathematical morphology to get the line of road segments (Chen & Cheng, 2008;Shi, Shen, & Liu, 2009). The resulting road map image is not able to contain topological information so it is hard to use for route planning and the low-frequency sampling problem is not well solved especially for different kinds of road intersections;…”

Section: Related Workmentioning

confidence: 99%

Complex Road Intersection Modelling Based on Low-Frequency GPS Track Data

Huang¹,

Deng²,

Zhang³

et al. 2017

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

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ABSTRACT:It is widely accepted that digital map becomes an indispensable guide for human daily traveling. Traditional road network maps are produced in the time-consuming and labour-intensive ways, such as digitizing printed maps and extraction from remote sensing images. At present, a large number of GPS trajectory data collected by floating vehicles makes it a reality to extract high-detailed and up-to-date road network information. Road intersections are often accident-prone areas and very critical to route planning and the connectivity of road networks is mainly determined by the topological geometry of road intersections. A few studies paid attention on detecting complex road intersections and mining the attached traffic information (e.g., connectivity, topology and turning restriction) from massive GPS traces. To the authors' knowledge, recent studies mainly used high frequency (1s sampling rate) trajectory data to detect the crossroads regions or extract rough intersection models. It is still difficult to make use of low frequency (20-100s) and easily available trajectory data to modelling complex road intersections geometrically and semantically. The paper thus attempts to construct precise models for complex road intersection by using low frequency GPS traces. We propose to firstly extract the complex road intersections by a LCSS-based (Longest Common Subsequence) trajectory clustering method, then delineate the geometry shapes of complex road intersections by a K-segment principle curve algorithm, and finally infer the traffic constraint rules inside the complex intersections.

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Section: Related Workmentioning

confidence: 99%

Complex Road Intersection Modelling Based on Low-Frequency GPS Track Data

Huang¹,

Deng²,

Zhang³

et al. 2017

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

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show abstract

“…. , T 2 be points of r (1) and r (2) , where T 1 and T 2 are the numbers of points in each trace, respectively. The first stage of implementing dynamic programming is to create a two-dimensional (2D) T 1 × T 2 length matrix L. The value at each cell, L(t 1 , t 2 ), represents the length of the longest common subsequences between the prefixes of the given traces, r (1) (i), i = 1, .…”

Section: Longest Common Subsequencementioning

confidence: 99%

“…A road network is a system of interconnecting lines that represent the interconnecting roads in a given area [1][2][3]. Traditionally, the road networks are constructed from geographic surveying through devices, such as telescopes and sextants.…”

Section: Introductionmentioning

confidence: 99%

Detecting Road Intersections from GPS Traces Using Longest Common Subsequence Algorithm

Xie

Liao

Aghajan

et al. 2016

IJGI

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Abstract:Intersections are important components of road networks, which are critical to both route planning and path optimization. Most existing methods define the intersections as locations where the road users change their moving directions and identify the intersections from GPS traces through analyzing the road users' turning behaviors. However, these methods suffer from finding an appropriate threshold for the moving direction change, leading to true intersections being undetected or spurious intersections being falsely detected. In this paper, the intersections are defined as locations that connect three or more road segments in different directions. We propose to detect the intersections under this definition by finding the common sub-tracks of the GPS traces. We first detect the Longest Common Subsequences (LCSS) between each pair of GPS traces using the dynamic programming approach. Second, we partition the longest nonconsecutive subsequences into consecutive sub-tracks. The starting and ending points of the common sub-tracks are collected as connecting points. At last, intersections are detected from the connecting points through Kernel Density Estimation (KDE). Experimental results show that our proposed method outperforms the turning point-based methods in terms of the F-score.

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“…There have been a few other existing algorithms for building maps with vehicular GPS trajectories such as image processing-based methods [2,3], clustering-based methods [4][5][6], machine learning-based methods [7], and a few other methods [8][9][10]. However, these algorithms are either high time costly, or sensitive to the granularity and noise of the positioning points, or require an auxiliary reference map.…”

Section: Road Network Constructionmentioning

confidence: 99%

Road Network Construction and Hot Routes Inferring with Vehicular Trajectories

Jing

Zhu

Chen

2014

Intelligent Computing in Bioinformatics

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Abstract. This work proposes a novel hot routes inferring approach without the support of real road network. Discovery of hot routes is important to the applications that requiring classifies the traffic flow or profiles the dynamic of the city, such as targeted advertising, traffic management and control. The advances of location-acquisition technologies have led to a huge collection of objects' trajectories in the road network, which give us the chances to finding hot routes conveniently. However, it is difficult to effectively detect hot routes without the support of the available road map. To address this issue, we first develop a Road Network Constructing Algorithm (RNCA) that extract road network from vehicular trajectories using image processing methods, and then propose a Hot Route Inferring Algorithm (HRRA) based on the extracted road network. Meanwhile, a novel road matching operation is also developed to match trajectory points onto roads. We have conducted extensive experiments on real dataset of taxi trajectories. Simulation results show that the proposed RNCA and HRRA are both effective and efficient.

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Automatic generation of road network map from massive GPS, vehicle trajectories

Cited by 104 publications

References 11 publications

Complex Road Intersection Modelling Based on Low-Frequency GPS Track Data

Complex Road Intersection Modelling Based on Low-Frequency GPS Track Data

Detecting Road Intersections from GPS Traces Using Longest Common Subsequence Algorithm

Road Network Construction and Hot Routes Inferring with Vehicular Trajectories

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