Automatic Update of Road Attributes by Mining GPS Tracks

K, Winden; Biljecki, Filip; Spek, Stefan van der

doi:10.1111/tgis.12186

Cited by 29 publications

(20 citation statements)

References 28 publications

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“…D i and Vi represent, respectively, the distance and the speed between points i, and i − 1. Similarly, SpeedRate represents the velocity change rate as suggested by [51].…”

Section: Indicators Description Formulamentioning

confidence: 99%

A Filtering-Based Approach for Improving Crowdsourced GNSS Traces in a Data Update Context

Ivanovic¹,

Olteanu‐Raimond²,

Mustière³

et al. 2019

IJGI

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Traces collected by citizens using GNSS (Global Navigation Satellite System) devices during sports activities such as running, hiking or biking are now widely available through different sport-oriented collaborative websites. The traces are collected by citizens for their own purposes and frequently shared with the sports community on the internet. Our research assumption is that crowdsourced GNSS traces may be a valuable source of information to detect updates in authoritative datasets. Despite their availability, the traces present some issues such as poor metadata, attribute incompleteness and heterogeneous positional accuracy. Moreover, certain parts of the traces (GNSS points composing the traces) are results of the displacements made out of the existing paths. In our context (i.e., update authoritative data) these off path GNSS points are considered as noise and should be filtered. Two types of noise are examined in this research: Points representing secondary activities (e.g., having a lunch break) and points representing errors during the acquisition. The first ones we named secondary human behaviour (SHB), whereas we named the second ones outliers. The goal of this paper is to improve the smoothness of traces by detecting and filtering both SHB and outliers. Two methods are proposed. The first one allows for the detection secondary human behaviour by analysing only traces geometry. The second one is a rule-based machine learning method that detects outliers by taking into account the intrinsic characteristics of points composing the traces, as well as the environmental conditions during traces acquisition. The proposed approaches are tested on crowdsourced GNSS traces collected in mountain areas during sports activities.

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“…D i and Vi represent, respectively, the distance and the speed between points i, and i − 1. Similarly, SpeedRate represents the velocity change rate as suggested by [51].…”

Section: Indicators Description Formulamentioning

confidence: 99%

A Filtering-Based Approach for Improving Crowdsourced GNSS Traces in a Data Update Context

Ivanovic¹,

Olteanu‐Raimond²,

Mustière³

et al. 2019

IJGI

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“…Many approaches for automatic lane detection have been reported in the literature: (Assidiq 2008), (Fernando 2014), (Saha 2012), (Van Winden 2016) and (Wang 2006), to mention a few.…”

Section: Introductionmentioning

confidence: 99%

“…In (Van Winden 2016), the authors describe a method for automatically deriving road attributes by analysing and mining movement trajectories (e.g. GPS tracks).…”

Section: Introductionmentioning

confidence: 99%

Efficient Lane Detection Based on Artificial Neural Networks

Arce

Zamora

Hernández

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Lane detection is a problem that has attracted in the last years the attention of the computer vision community. Most of approaches used until now to face this problem combine conventional image processing, image analysis and pattern classification techniques. In this paper, we propose a methodology based on so-called Ellipsoidal Neural Networks with Dendritic Processing (ENNDPs) as a new approach to provide a solution to this important problem. The functioning and performance of the proposed methodology is validated with a real video taken by a camera mounted on a car circulating on urban highway of Mexico City.

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“…Specific measures in GPS trajectory often use speed, acceleration, angle change, etc. [13,14,15,16]. Such non-model-based methods in GPS trajectories can be attributed as ‘threshold methods’.…”

Section: Introductionmentioning

confidence: 99%

Trend-Residual Dual Modeling for Detection of Outliers in Low-Cost GPS Trajectories

Chen

Cui

et al. 2016

Sensors

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Low-cost GPS (receiver) has become a ubiquitous and integral part of our daily life. Despite noticeable advantages such as being cheap, small, light, and easy to use, its limited positioning accuracy devalues and hampers its wide applications for reliable mapping and analysis. Two conventional techniques to remove outliers in a GPS trajectory are thresholding and Kalman-based methods, which are difficult in selecting appropriate thresholds and modeling the trajectories. Moreover, they are insensitive to medium and small outliers, especially for low-sample-rate trajectories. This paper proposes a model-based GPS trajectory cleaner. Rather than examining speed and acceleration or assuming a pre-determined trajectory model, we first use cubic smooth spline to adaptively model the trend of the trajectory. The residuals, i.e., the differences between the trend and GPS measurements, are then further modeled by time series method. Outliers are detected by scoring the residuals at every GPS trajectory point. Comparing to the conventional procedures, the trend-residual dual modeling approach has the following features: (a) it is able to model trajectories and detect outliers adaptively; (b) only one critical value for outlier scores needs to be set; (c) it is able to robustly detect unapparent outliers; and (d) it is effective in cleaning outliers for GPS trajectories with low sample rates. Tests are carried out on three real-world GPS trajectories datasets. The evaluation demonstrates an average of 9.27 times better performance in outlier detection for GPS trajectories than thresholding and Kalman-based techniques.

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Automatic Update of Road Attributes by Mining GPS Tracks

Cited by 29 publications

References 28 publications

A Filtering-Based Approach for Improving Crowdsourced GNSS Traces in a Data Update Context

A Filtering-Based Approach for Improving Crowdsourced GNSS Traces in a Data Update Context

Efficient Lane Detection Based on Artificial Neural Networks

Trend-Residual Dual Modeling for Detection of Outliers in Low-Cost GPS Trajectories

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