Scalable unsupervised multi-criteria trajectory segmentation and driving preference mining-Reference-Cited by-同舟云学术

Scalable unsupervised multi-criteria trajectory segmentation and driving preference mining

Published:2020-11-03 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 9th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
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Author:

Barth Florian¹,Funke Stefan¹,Jepsen Tobias Skovgaard²,Proissl Claudius¹

Affiliation:

1. Universität Stuttgart, Germany

2. Aalborg University, Denmark

Funder

Det Obelske Familiefond

Villum Fonden

Deutsche Forschungsgemeinschaft

Publisher

ACM

Link

Reference21 articles.

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3. Segmenting trajectories: A framework and algorithms using spatiotemporal criteria;Buchin Maike;Journal of Spatial Information Science,2011

4. Personalized and Situation-Aware Multimodal Route Recommendations;Campigotto Paolo;The FAVOUR Algorithm. Trans. Intell. Transport. Sys.,2017

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3. Personalized Long-distance Fuel-efficient Route Recommendation Through Historical Trajectories Mining;Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining;2022-02-11