A generic trajectory similarity operator in moving object databases

Magdy, Nehal; Sakr, Mahmoud; El-Bahnasy, Khaled

doi:10.1016/j.eij.2016.07.001

Cited by 9 publications

(3 citation statements)

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“…These either evaluate spatial-temporal similarity or only spatial similarity. In [11], a detailed analysis of twelve published metrics was performed. To evaluate routes based on their partial overlap, the Fréchet metric [12], dynamic time warping (DTW), edit distance, and longest common subsequence (LCSS) methods [13] are available.…”

Section: Related Workmentioning

confidence: 99%

“…In [14], the traditional dynamic time warping (DTW) algorithm is used and incorporates several distance factors such as point-segments, consideration of temporal distance factors by converting to spatial distances, and segmentsegment distances. However, using only the length of partial overlap as a static threshold for evaluation may lead to nonidentification of potential matches [11]. All other methods compare two routes completely with each other, which only leads to usable results for scenario [1,1] shown in Figure 1, which leads to disadvantages in other valid scenarios.…”

Section: Related Workmentioning

confidence: 99%

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DLCSS: Dynamic Longest Common Subsequences

Bogdoll

Rauch

Zöllner

2022

2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)

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Autonomous driving is a key technology towards a brighter, more sustainable future. To enable such a future, it is necessary to utilize autonomous vehicles in shared mobility models. However, to evaluate, whether two or more route requests have the potential for a shared ride, is a computeintensive task, if done by rerouting. In this work, we propose the Dynamic Longest Common Subsequences algorithm for fast and cost-efficient comparison of two routes for their compatibility, dynamically only incorporating parts of the routes which are suited for a shared trip. Based on this, one can also estimate, how many autonomous vehicles might be necessary to fulfill the local mobility demands. This can help providers to estimate the necessary fleet sizes, policymakers to better understand mobility patterns and cities to scale necessary infrastructure.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

DLCSS: Dynamic Longest Common Subsequences

Bogdoll

Rauch

Zöllner

2022

2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)

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“…For instance, in transportation, outliers in vehicle trajectory data could lead to incorrect traffic flow analysis, resulting in inefficient traffic management. This makes outlier or noise detection a crucial step in trajectory cleaning Magdy et al (2017). As such, outlier detection is an essential function in mobility data management systems Zimányi et al (2020a); Zimányi et al (2019).…”

Section: Introductionmentioning

confidence: 99%

A Benchmark of Existing Tools for Outlier Detection and Cleaning in Trajectories

Duarte,

Sakr

2023

Preprint

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Outlier detection and cleaning is an essential step in data preprocessing to ensure the integrity and validity of data analyses. This paper focuses on outlier points within a individual trajectories, i.e., points that deviate significantly inside a single trajectory. We benchmark ten open-source libraries to comprehensively evaluate available tools, comparing their efficiency and accuracy in identifying and cleaning outliers. This benchmarking considers the libraries as they are offered to end users, with real-world applicability. We compare existing outlier detection libraries, introduce a method for establishing ground-truth, and aim to guide users in choosing the most appropriate tool for their specific outlier detection needs. Furthermore, we survey the state-of-the-art algorithms for outlier detection and classify them into seven types: Statistic-based methods, Sliding window algorithms, Clustering-based methods, Graph-based methods, Ensemble-based methods, Learning-based methods, and Heuristic-based methods. Our research provides insights into these libraries' performance and contributes to developing data preprocessing and outlier detection methodologies.

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Short and Long Term Vessel Movement Prediction for Maritime Traffic

Farahnakian,

Sheikh

et al. 2024

Lecture Notes in Computer Science

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A generic trajectory similarity operator in moving object databases

Cited by 9 publications

References 10 publications

DLCSS: Dynamic Longest Common Subsequences

DLCSS: Dynamic Longest Common Subsequences

A Benchmark of Existing Tools for Outlier Detection and Cleaning in Trajectories

Short and Long Term Vessel Movement Prediction for Maritime Traffic

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