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Evaluation of the Use of Streaming Graph Processing Algorithms for Road Congestion Detection
KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.
KTH.
RISE Res Inst Sweden, Stockholm, Sweden..
KTH, Skolan för elektroteknik och datavetenskap (EECS), Programvaruteknik och datorsystem, SCS.ORCID-id: 0000-0002-6779-7435
2018 (engelsk)Inngår i: 2018 IEEE INT CONF ON PARALLEL & DISTRIBUTED PROCESSING WITH APPLICATIONS, UBIQUITOUS COMPUTING & COMMUNICATIONS, BIG DATA & CLOUD COMPUTING, SOCIAL COMPUTING & NETWORKING, SUSTAINABLE COMPUTING & COMMUNICATIONS / [ed] Chen, JJ Yang, LT, IEEE COMPUTER SOC , 2018, s. 1017-1025Konferansepaper, Publicerat paper (Fagfellevurdert)
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 approximate to 94% for Connected Components and up to approximate 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.

sted, utgiver, år, opplag, sider
IEEE COMPUTER SOC , 2018. s. 1017-1025
Serie
IEEE International Symposium on Parallel and Distributed Processing with Applications, ISSN 2158-9178
Emneord [en]
Streaming, Graph Processing, Congestion, Community Detection, Connected Components
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-252672DOI: 10.1109/BDCloud.2018.00148ISI: 000467843200134Scopus ID: 2-s2.0-85063892833ISBN: 978-1-7281-1141-4 (tryckt)OAI: oai:DiVA.org:kth-252672DiVA, id: diva2:1319761
Konferanse
16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom, DEC 11-13, 2018, Melbourne, AUSTRALIA
Merknad

QC 20190603

Tilgjengelig fra: 2019-06-03 Laget: 2019-06-03 Sist oppdatert: 2019-06-11bibliografisk kontrollert

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Abbas, ZainabSigurdsson, Thorsteinn ThorriVlassov, Vladimir

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