A survey of trajectory distance measures and performance evaluation

Su, Han; Liu, Shuncheng; Zheng, Bolong; Zhou, Xiaofang; Zheng, Kai

doi:10.1007/s00778-019-00574-9

Cited by 124 publications

(76 citation statements)

References 74 publications

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“…Thus it may lead to classifications of such trajectories into different routes. Using partial match measures such as the LCSS distance (see Su et al, 2020 for review) lower the risk of classifying several disturbed trajectories belonging to the same route into different routes.…”

Section: Discussionmentioning

confidence: 99%

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From Paths to Routes: A Method for Path Classification

Gonsek

Jeschke

Rönnau

et al. 2021

Front. Behav. Neurosci.

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Many animals establish, learn and optimize routes between locations to commute efficiently. One step in understanding route following is defining measures of similarities between the paths taken by the animals. Paths have commonly been compared by using several descriptors (e.g., the speed, distance traveled, or the amount of meandering) or were visually classified into categories by the experimenters. However, similar quantities obtained from such descriptors do not guarantee similar paths, and qualitative classification by experimenters is prone to observer biases. Here we propose a novel method to classify paths based on their similarity with different distance functions and clustering algorithms based on the trajectories of bumblebees flying through a cluttered environment. We established a method based on two distance functions (Dynamic Time Warping and Fréchet Distance). For all combinations of trajectories, the distance was calculated with each measure. Based on these distance values, we grouped similar trajectories by applying the Monte Carlo Reference-Based Consensus Clustering algorithm. Our procedure provides new options for trajectory analysis based on path similarities in a variety of experimental paradigms.

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Section: Discussionmentioning

confidence: 99%

“…Our second method to describe each path was based on similarity measures of their structure. Several functions can evaluate the similarity between two trajectories (Magdy et al, 2015 ; Su et al, 2020 ). These functions yield a distance which is the inverse of the similarity between the two trajectories.…”

Section: Methodsmentioning

confidence: 99%

From Paths to Routes: A Method for Path Classification

Gonsek

Jeschke

Rönnau

et al. 2021

Front. Behav. Neurosci.

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“…Movement analyses can be conducted on spatiotemporal trajectory(s) (see [102][103][104]). A spatiotemporal trajectory is an ascendant connected timestamp of geometric relocations of an autonomous moving agent performed in order to achieve a goal (see [105] a detailed description of trajectories).…”

Section: A Call For Adopting Causal Concepts In Movement Analysismentioning

confidence: 99%

Beyond Objects in Space-Time: Towards a Movement Analysis Framework with ‘How’ and ‘Why’ Elements

Rahimi

Moore

Whigham

2021

IJGI

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Current spatiotemporal data has facilitated movement studies to shift objectives from descriptive models to explanations of the underlying causes of movement. From both a practical and theoretical standpoint, progress in developing approaches for these explanations should be founded on a conceptual model. This paper presents such a model in which three conceptual levels of abstraction are proposed to frame an agent-based representation of movement decision-making processes: ‘attribute,’ ‘actor,’ and ‘autonomous agent’. These in combination with three temporal, spatial, and spatiotemporal general forms of observations distinguish nine (3x3) representation typologies of movement data within the agent framework. Thirdly, there are three levels of cognitive reasoning: ‘association,’ ‘intervention,’ and ‘counterfactual’. This makes for 27 possible types of operation embedded in a conceptual cube with the level of abstraction, type of observation, and degree of cognitive reasoning forming the three axes. The conceptual model is an arena where movement queries and the statement of relevant objectives takes place. An example implementation of a tightly constrained spatiotemporal scenario to ground the agent-structure was summarised. The platform has been well-defined so as to accommodate different tools and techniques to drive causal inference in computational movement analysis as an immediate future step.

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“…In this section, we describe the similarity measures that were used in the experiments to evaluate the trajectory compression algorithms. Several trajectory similarity algorithms have been proposed and evaluated in the literature [42]- [47], each with its own advantages and drawbacks. Similarity measures are employed for different purposes in information retrieval and data mining, such as top-K similarity queries, trajectory outlier detection and clustering techniques [48].…”

Section: Trajectory Similarity Measuresmentioning

confidence: 99%

A Comparison of Trajectory Compression Algorithms Over AIS Data

et al. 2021

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Today's industry is flooded with tracking data originating from vessels across the globe that transmit their position at frequent intervals. These voluminous and high-speed streams of data has led researchers to develop novel ways to compress them in order to speed-up processing without losing valuable information. To this end, several algorithms have been developed that try to compress streams of vessel tracking data without compromising their spatio-temporal and kinematic features. In this paper, we present a wide range of several well-known trajectory compression algorithms and evaluate their performance on data originating from vessel trajectories. Trajectory compression algorithms included in this research are suitable for either historical data (offline compression) or real-time data streams (online compression). The performance evaluation is three-fold and each algorithm is evaluated in terms of compression ratio, execution speed and information loss. Experiments demonstrated that each algorithm has its own benefits and limitations and that the choice of a suitable compression algorithm is application-dependent. Finally, considering all assessed aspects, the Dead-Reckoning algorithm not only presented the best performance, but it also works over streaming data, which constitutes an important criterion in maritime surveillance.

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A survey of trajectory distance measures and performance evaluation

Cited by 124 publications

References 74 publications

From Paths to Routes: A Method for Path Classification

From Paths to Routes: A Method for Path Classification

Beyond Objects in Space-Time: Towards a Movement Analysis Framework with ‘How’ and ‘Why’ Elements

A Comparison of Trajectory Compression Algorithms Over AIS Data

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