University of Oulu

C. Bermejo, T. Wu, X. Su and P. Hui, "Optimal Seeds Discovery of Traffic Congestions," 2020 IEEE 36th International Conference on Data Engineering Workshops (ICDEW), Dallas, TX, USA, 2020, pp. 71-78, doi: 10.1109/ICDEW49219.2020.000-5

Optimal seeds discovery of traffic congestions

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Author: Bermejo, Carlos1; Wu, Ting1; Su, Xiang2,3;
Organizations: 1Department of Computer Science and Engineering, Hong Kong University of Science and Technology
2Department of Computer Science, University of Helsinki
3Department of Computer Science, University of Oulu
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.3 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe20201217101060
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-12-17
Description:

Abstract

With the rapid adoption of wireless sensor networks (WSNs) into smart cities and vehicle networks, traffic problems can be evaluated and predicted in real-time. In this paper, we propose a data-driven approach to find out the most influential causes of traffic congestions. We first find the top most influential regions and use the Fortune’s algorithm to partition the city. Second, we propose a model with three correlations to measure the dependency between two traffic events, which are spatial correlation, temporal correlation, and logical correlation. Third, we adapt the Independent Cascade model with a pruning algorithm to address traffic congestions. At last, we conduct intensive experiments on large real-world GPS trajectories generated by more than 10,200 taxis in Shanghai to demonstrate the performance of our approaches.

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Series: IEEE International Conference on Data Engineering workshop
ISSN: 1943-2895
ISSN-E: 2473-3490
ISSN-L: 1943-2895
ISBN: 978-1-7281-4266-1
ISBN Print: 978-1-7281-4267-8
Pages: 71 - 78
DOI: 10.1109/ICDEW49219.2020.000-5
OADOI: https://oadoi.org/10.1109/ICDEW49219.2020.000-5
Host publication: 2020 IEEE 36th International Conference on Data Engineering Workshops (ICDEW)
Conference: IEEE International Conference on Data Engineering Workshops
Type of Publication: A4 Article in conference proceedings
Field of Science: 113 Computer and information sciences
213 Electronic, automation and communications engineering, electronics
Subjects:
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