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Evaluation of the use of streaming graph processing algorithms for road congestion detection
KTH Royal Institute of Technology, Sweden.
KTH Royal Institute of Technology, Sweden.
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0002-9546-4937
KTH Royal Institute of Technology, Sweden.
2018 (English)In: Proceedings - 16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 17th IEEE International Conference on Ubiquitous Computing and Communications, 8th IEEE International Conference on Big Data and Cloud Computing, 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 1017-1025Conference paper, Published paper (Refereed)
Abstract [en]

Real-time road congestion detection allows improving traffic safety and route planning. In this work, we propose to use streaming graph processing algorithms for road congestion detection and evaluate their accuracy and performance. We represent road infrastructure sensors in the form of a directed weighted graph and adapt the Connected Components algorithm and some existing graph processing algorithms, originally used for community detection in social network graphs, for the task of road congestion detection. In our approach, we detect Connected Components or communities of sensors with similarly weighted edges that reflect different states in the traffic, e.g., free flow or congested state, in regions covered by detected sensor groups. We have adapted and implemented the Connected Components and community detection algorithms for detecting groups in the weighted sensor graphs in batch and streaming manner. We evaluate our approach by building and processing the road infrastructure sensor graph for Stockholm's highways using real-world data from the Motorway Control System operated by the Swedish traffic authority. Our results indicate that the Connected Components and DenGraph community detection algorithms can detect congestion with accuracy up to ? 94% for Connected Components and up to ? 88% for DenGraph. The Louvain Modularity algorithm for community detection fails to detect congestion regions for sparsely connected graphs, representing roads that we have considered in this study. The Hierarchical Clustering algorithm using speed and density readings is able to detect congestion without details, such as shockwaves.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2018. p. 1017-1025
Keywords [en]
Community Detection, Congestion, Connected Components, Graph Processing, Streaming, Acoustic streaming, Big data, Cloud computing, Directed graphs, Population dynamics, Roads and streets, Signal detection, Traffic congestion, Ubiquitous computing, Community detection algorithms, Connected component, Hierarchical clustering algorithms, Road infrastructures, Traffic authorities, Clustering algorithms
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-38390DOI: 10.1109/BDCloud.2018.00148Scopus ID: 2-s2.0-85063892833ISBN: 9781728111414 (print)OAI: oai:DiVA.org:ri-38390DiVA, id: diva2:1314949
Conference
16th IEEE International Symposium on Parallel and Distributed Processing with Applications, 17th IEEE International Conference on Ubiquitous Computing and Communications, 8th IEEE International Conference on Big Data and Cloud Computing, 11th IEEE International Conference on Social Computing and Networking and 8th IEEE International Conference on Sustainable Computing and Communications, ISPA/IUCC/BDCloud/SocialCom/SustainCom 2018, 11 December 2018 through 13 December 2018
Note

Funding details: Fellowships Fund Incorporated; Funding details: VINNOVA; Funding details: 20140221; Funding details: European Commission, FPA 2012-0030; Funding details: Education, Audiovisual and Culture Executive Agency; Funding details: 2015-00677; Funding text 1: ACKNOWLEDGMENT This work was supported by the Erasmus Mundus Joint Doctorate in Distributed Computing (EMJD-DC) funded by the Education, Audiovisual and Culture Executive Agency (EACEA) of the European Commission under FPA 2012-0030, by the project BADA: Big Automotive Data Analytics in the funding program FFI: Strategic Vehicle Research and Innovation (grant 2015-00677) administrated by VINNOVA the Swedish government agency for innovation systems, and by the project BIDAF: Big Data Analytics Framework for a Smart Society (grant 20140221) funded by KKS the Swedish Knowledge Foundation.

Available from: 2019-05-10 Created: 2019-05-10 Last updated: 2019-05-10Bibliographically approved

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Al-Shishtawy, Ahmad

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