Averaging GPS segments competition 2019

Fränti, Pasi; Mariescu-Istodor, Radu

doi:10.1016/j.patcog.2020.107730

Cited by 9 publications

(21 citation statements)

References 25 publications

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“…However, averaging time series is significantly more challenging as the sample points do not necessarily align. It is possible to solve the averaging problem by using mean, which is defined Despite the potential benefits of medoid, practical results in [7] were quite discouraging. All studied averaging heuristics performed significantly better than medoid regardless of the distance function.…”

Section: Segment Averagingmentioning

confidence: 99%

“…However, averaging time series is significantly more challenging as the sample points do not necessarily align. It is possible to solve the averaging problem by using mean, which is defined as any possible sequence in the data space that minimizes the sum of squared distances to all the input sequences [7]:…”

Section: Segment Averagingmentioning

confidence: 99%

“…If included, the problem would become averaging of sequences in 3-d space. The GPS trajectories can also be very noisy due to GPS errors [7]. Thus, the two main challenges of the averaging problem are that (1) the individual points do not align, and (2) the number of points can vary significantly between the different sequences.…”

Section: Introductionmentioning

confidence: 99%

“…These might deteriorate its performance in practice. Despite the potential benefits of medoid, practical results in [7] were quite discouraging. All studied averaging heuristics performed significantly better than medoid regardless of the distance function.…”

Section: Introductionmentioning

confidence: 99%

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Is Medoid Suitable for Averaging GPS Trajectories?

Jimoh

Mariescu-Istodor

Fränti

2022

IJGI

Self Cite

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Averaging GPS trajectories is needed in applications such as clustering and automatic extraction of road segments. Calculating mean for trajectories and other time series data is non-trivial and shown to be an NP-hard problem. medoid has therefore been widely used as a practical alternative and because of its (assumed) better noise tolerance. In this paper, we study the usefulness of the medoid to solve the averaging problem with ten different trajectory-similarity/-distance measures. Our results show that the accuracy of medoid depends mainly on the sample size. Compared to other averaging methods, the performance deteriorates especially when there are only few samples from which the medoid must be selected. Another weakness is that medoid inherits properties such as the sample frequency of the arbitrarily selected sample. The choice of the trajectory distance function becomes less significant. For practical applications, other averaging methods than medoid seem a better alternative for higher accuracy.

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Section: Segment Averagingmentioning

confidence: 99%

“…However, averaging time series is significantly more challenging as the sample points do not necessarily align. It is possible to solve the averaging problem by using mean, which is defined as any possible sequence in the data space that minimizes the sum of squared distances to all the input sequences [7]:…”

Section: Segment Averagingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Is Medoid Suitable for Averaging GPS Trajectories?

Jimoh

Mariescu-Istodor

Fränti

2022

IJGI

Self Cite

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“…C-SIM calculates the number of overlapping cells between the representative cell sequences of two trajectories, then divides it by the total number of cells to produce the similarity for the two trajectories. The authors also proposed Hierarchical Cell Similarity method (HC-SIM) [27], which optimized C-SIM at different zoom levels, extending the cell length to six levels (0.5%, 1%, 2%, 4%, 8%, and 16%). Calculations of the C-SIM measurement results are performed on each layer, taking the average value as the final result.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Similarity Analysis with the Weight of Direction and k-Neighborhood for AIS Data

Nie

Chen

Xia

et al. 2021

IJGI

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Automatic Identification System (AIS) data have been widely used in many fields, such as collision detection, navigation, and maritime traffic management. Similarity analysis is an important process for most AIS trajectory analysis topics. However, most traditional AIS trajectory similarity analysis methods calculate the distance between trajectory points, which requires complex and time-consuming calculations, often leading to substantial errors when processing AIS trajectory data characterized by substantial differences in length or uneven trajectory points. Therefore, we propose a cell-based similarity analysis method that combines the weight of the direction and k-neighborhood (WDN-SIM). This method quantifies the similarity between trajectories based on the degree of proximity and differences in motion direction. In terms of its effectiveness and efficiency, WDN-SIM outperformed seven traditional methods for trajectory similarity analysis. Particularly, WDN-SIM has a high robustness to noise and can distinguish the similarities between trajectories under complex situations, such as when there are opposing directions of motion, large differences in length, and uneven point distributions.

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A constrained spectral clustering method for lane identification using trajectory data

Zhao,

Roncoli

2023

Transportation Research Part C: Emerging Technologies

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Averaging GPS segments competition 2019

Cited by 9 publications

References 25 publications

Is Medoid Suitable for Averaging GPS Trajectories?

Is Medoid Suitable for Averaging GPS Trajectories?

Trajectory Similarity Analysis with the Weight of Direction and k-Neighborhood for AIS Data

A constrained spectral clustering method for lane identification using trajectory data

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