Uncertainty in Spatial Trajectories

Trajcevski, Goce

doi:10.1007/978-1-4614-1629-6_3

Cited by 23 publications

(10 citation statements)

References 120 publications

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“…There are two approaches to model such spatial uncertainty in trajectories [12,11]: 1. Pdf-based models, such as associating a two-dimensional probability density function with each time-stamped position.…”

Section: Modeling Uncertainty In Recon-structed Intersectionsmentioning

confidence: 99%

Probabilistic street-intersection reconstruction from GPS trajectories

Ahmed

Wenk

2012

Proceedings of the Third ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data

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Analyzing and mining geo-referenced trajectory data has different aspects to researchers from different communities. For example, animal location data provides ecologists live points of contact between ecologies and the species. And by studying movements of individual animals, they have gained insight into population distributions, important resources, dispersal settings, social interaction or general patterns of how the space was used in an ecological system. Similarly, geologists and environmentalists use earthquake positional data for predicting the location of the next earthquake. Intelligent Transportation Systems and GIS communities use heuristic algorithms on vehicle trajectory data sets to construct or update digital street-maps that represent the data set. Recently, the Computational Geometry community started to give attention to the street-map construction problems as well, applying different approaches and providing quality guarantees. Although different communities use different types or aspects of the GPS data, they face one challenge in common: how to model or incorporate the impreciseness of the input data in their output. In this paper we discuss specifically the impact of spatial inaccuracy of GPS trajectory data on street-map reconstruction algorithms. In particular, we discuss approaches and challenges to associate that impreciseness with the reconstructed street-intersections.

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Section: Modeling Uncertainty In Recon-structed Intersectionsmentioning

confidence: 99%

Probabilistic street-intersection reconstruction from GPS trajectories

Ahmed

Wenk

2012

Proceedings of the Third ACM SIGSPATIAL International Workshop on Querying and Mining Uncertain Spatio-Temporal Data

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“…However, the GPS trajectories sampling rate is not regular and do not include spatio-temporal information of the mobile nodes regularly for all needed times. Moreover, particularly in urban environments we need to provide map-matching [12], [18], [19] to reduce GPS noise and make our trajectories more dependable. In previous related works such as [5], linear interpolation has been applied on the raw GPS trajectories to interpolate missing waypoints (at needed sampling times).…”

Section: A Trajectory Pre-processingmentioning

confidence: 99%

Measuring Similarity Between Mobility Models and Real World Motion Trajectories

Barroudi

Harwood

Karunasekera

2014

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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“…Uncertainty quantification of spatial data is of much importance for geological or seismic scientists [19], [24]. It is the process of quantifying the uncertainty error of each point in the spatial cube space, which requires computing a Probability Density Function (PDF) of each point [13].…”

Section: Introductionmentioning

confidence: 99%

Parallel computation of PDFs on big spatial data using Spark

Lemus²,

Pacitti

et al. 2019

Distrib Parallel Databases

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We consider big spatial data, which is typically produced in scientific areas such as geological or seismic interpretation. The spatial data can be produced by observation (e.g. using sensors or soil instrument) or numerical simulation programs and correspond to points that represent a 3D soil cube area. However, errors in signal processing and modeling create some uncertainty, and thus a lack of accuracy in identifying geological or seismic phenomenons. Such uncertainty must be carefully analyzed. To analyze uncertainty, the main solution is to compute a Probability Density Function (PDF) of each point in the spatial cube area. However, computing PDFs on big spatial data can be very time consuming (from several hours to even months on a parallel computer). In this paper, we propose a new solution to efficiently compute such PDFs in parallel using Spark, with three methods: data grouping, machine learning prediction and sampling. We evaluate our solution by extensive experiments on different computer clusters using big data ranging from hundreds of GB to several TB. The experimental results show that our solution scales up very well and can reduce the execution time by a factor of 33 (in the order of seconds or minutes) compared with a baseline method.

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Uncertainty in Spatial Trajectories

Cited by 23 publications

References 120 publications

Probabilistic street-intersection reconstruction from GPS trajectories

Probabilistic street-intersection reconstruction from GPS trajectories

Measuring Similarity Between Mobility Models and Real World Motion Trajectories

Parallel computation of PDFs on big spatial data using Spark

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