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A Path-Clustering Driving Travel-Route Excavation

A Path-Clustering Driving Travel-Route Excavation

Can Yang
Copyright: © 2022 |Volume: 18 |Issue: 1 |Pages: 16
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781799893967|DOI: 10.4018/IJSWIS.306750
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MLA

Yang, Can. "A Path-Clustering Driving Travel-Route Excavation." IJSWIS vol.18, no.1 2022: pp.1-16. http://doi.org/10.4018/IJSWIS.306750

APA

Yang, C. (2022). A Path-Clustering Driving Travel-Route Excavation. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-16. http://doi.org/10.4018/IJSWIS.306750

Chicago

Yang, Can. "A Path-Clustering Driving Travel-Route Excavation," International Journal on Semantic Web and Information Systems (IJSWIS) 18, no.1: 1-16. http://doi.org/10.4018/IJSWIS.306750

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Abstract

The refueling trajectory of self-driving tourists is sparse, and it is difficult to restore the real travel route. A sparse trajectory clustering algorithm is proposed based on semantic representation to mine popular self-driving travel routes. Different from the traditional trajectory clustering algorithm based on trajectory point matching, the semantic relationship between different trajectory points is researched in this algorithm, and the low-dimensional vector representation of the trajectory is learned. First, the neural network language model is used to learn the distributed vector representation of the fueling station; then, the average of all the station vectors in each trajectory is taken as the vector representation of the trajectory. Finally, the classic k-means algorithm is used to cluster the trajectory vectors. The final visualization results show that the proposed algorithm effectively mines two popular self-driving travel routes.