El Fattahi, L. and Sbai, E.H. (2019) Vehicle trajectory clustering using variable kernel estimator. Lecture Notes in Electrical Engineering, 519. pp. 107-112.

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Official URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Abstract

Wrestling against road unsafety is a mandatory issue that has very serious effect on human health. Therefore, many researches were tackled the vehicle trajectory modeling and measurement to examine the interaction between the driver, the vehicle and the infrastructure in order to identify the safe and unsafe zones of the trajectories. The aim of this study is to determine the different driving behaviors through a real trajectories analysis using unsupervised clustering based on density estimation through the variable kernel estimator. Also, we introduce a new method of data transformation for trajectory data before considering any clustering algorithm. © 2019, Springer Nature Singapore Pte Ltd.

Item Type: Article
Uncontrolled Keywords: Metadata; Principal component analysis; Trajectories; Vehicles, Clustering; Data transformation; Density estimation; Driving behavior; Real trajectories; Unsupervised clustering; Variable kernels; Vehicle trajectories, Clustering algorithms
Subjects: Engineering
Divisions: SCIENTIFIC PRODUCTION > Engineering
Depositing User: Administrateur Eprints Administrateur Eprints
Last Modified: 31 Jan 2020 15:47
URI: http://eprints.umi.ac.ma/id/eprint/3144

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