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Evaluation of the u...
Evaluation of the use of streaming graph processing algorithms for road congestion detection
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- Abbas, Zainab (författare)
- KTH,Programvaruteknik och datorsystem, SCS
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- Sigurdsson, Thorsteinn Thorri (författare)
- KTH
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- Al-Shishtawy, Ahmad (författare)
- RISE,SICS,RISE Res Inst Sweden, Stockholm, Sweden.
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- Vlassov, Vladimir (författare)
- KTH,Programvaruteknik och datorsystem, SCS
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers Inc. 2018
- 2018
- Engelska.
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Ingår i: 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.. - 9781728111414 ; , s. 1017-1025
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Nyckelord
- 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
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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