Abstract
Traffic management today requires the analysis of a huge amount of data in real-time in order to provide current information about the traffic state or hazards to road users and traffic control authorities. Modern cars are equipped with several sensors which can produce useful data for the analysis of traffic situations. Using mobile communication technologies, such data can be integrated and aggregated from several cars which enables intelligent transportation systems (ITS) to monitor the traffic state in a large area at relatively low costs. However, processing and analyzing data poses numerous challenges for data management solutions in such systems. Real-time analysis with high accuracy and confidence is one important requirement in this context. We present a summary of our work on a comprehensive evaluation framework for data stream-based ITS. The goal of the framework is to identify appropriate configurations for ITS and to evaluate different mining methods for data analysis. The framework consists of a traffic simulation software, a data stream management system, utilizes data stream mining algorithms, and provides a flexible ontology-based component for data quality monitoring during data stream processing. The work has been done in the context of a project on Car-To-X communication using mobile communication networks. The results give some interesting insights for the setup and configuration 0 traffic information systems that use Car-To-X messages as primary source for deriving traffic information and also point out challenges for data stream management and data stream mining.
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Notes
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Other products can also be used in the architecture. We implemented the same functionality also for PostgreSQL and PostGIS.
References
Abadi DJ, Carney D, Çetintemel U, Cherniack M, Convey C, Lee S, Stonebraker M, Tatbul N, Zdonik SB (2003) Aurora: a new model and architecture for data stream management. VLDB J 12(2):120–139
Abadi DJ, Ahmad Y, Balazinska M, Çetintemel U, Cherniack M, Hwang JH, Lindner W, Maskey A, Rasin A, Ryvkina E, Tatbul N, Xing Y, Zdonik SB (2005) The design of the Borealis stream processing engine. In: Proceedings of the CIDR, Asilomar, pp 277–289
Aberer K, Hauswirth M, Salehi A (2006) A middleware for fast and flexible sensor network deployment. In: Proceedings of the VLDB’06, Seoul, pp 1199–1202
Ali MH, Chandramouli B, Raman BS, Katibah E (2010) Spatio-temporal stream processing in Microsoft StreamInsight. IEEE Data Eng Bull 33(2):69–74
Arasu A, Cherniack M, Galvez E, Maier D, Maskey A, Ryvkina E, Stonebraker M, Tibbetts R (2004) Linear road: a stream data management benchmark. In: Nascimento MA, Özsu MT, Kossmann D, Miller RJ, Blakeley JA, Schiefer KB (eds) Proceedings of the 30th international conference on very large data bases (VLDB), Toronto. Morgan Kaufmann, pp 480–491
BASt (1999) Merkblatt für die Ausstattung von Verkehrsrechnerzentralen und Unterzentralen (MARZ). Bundesanstalt für Straßenwesen. (in German)
Biem A, Bouillet E, Feng H, Ranganathan A, Riabov A, Verscheure O, Koutsopoulos HN, Moran C (2010) IBM InfoSphere streams for scalable, real-time, intelligent transportation services. In: Elmagarmid AK, Agrawal D (eds) Proceedings of the ACM international conference on management of data (SIGMOD), Indianapolis. ACM, pp 1093–1104
Bifet A, Kirkby R (2009) Data stream mining – a practical approach. University of Waikato. http://www.cs.waikato.ac.nz/~abifet/MOA/StreamMining.pdf
Bifet A, Holmes G, Kirkby R, Pfahringer B (2010) MOA: massive online analysis. J Mach Learn Res 11:1601–1604
Bogenberger K, Belzner H, Kates R (2003) Ein hybrides Modell basierend auf einem Neuronalen Netz und einem ARIMA-Zeitreihenmodell zur Prognose lokaler Verkehrskenngrößen. Straßenverkehrstechnik 47(1):5–12. (in German)
Chen H, Grant-Muller S (2001) Use of sequential learning for short-term traffic flow forecasting. Transp Res Part C Emerg Technol 9(5):319–336
Domingos P, Hulten G (2000) Mining high-speed data streams. In: Proceedings of the 6th ACM SIGKDD international conference on knowledge discovery and data mining, Boston. ACM, pp 71–80
Fiege G, Gasser T, Gehlen G, Geisler S, Jodlauk G, Phan MA, Quix C, Rembarz R, Wiecker M, Westhoff D (2011) ITS services and communication architecture. Deliverable D03, Cooperative Cars eXtended
Fontaine MD, Smith BL (2007) Investigation of the performance of wireless location technology-based traffic monitoring systems. J Transp Eng 133:157–165
Geisler S, Quix C, Gehlen GG, Jodlauk G (2009) A quality- and priority-based traffic information fusion architecture. Proc. of the 16th World Congress on intelligent transport systems and services (ITS), Stockholm, Sweden
Geisler S, Chen Y, Quix C, Gehlen G (2010) Accuracy assessment for traffic information derived from floating phone data. Proc. of the 16th World Congress on intelligent transport systems and services, Busan, Korea
Geisler S, Weber S, Quix C (2011) An ontology-based data quality framework for data stream applications. In: Proceedings of the ICIQ, Adelaide
Geisler S, Quix C, Schiffer S, Jarke M (2012) An evaluation framework for traffic information systems based on data streams. Transp Res Part C 23:29–55
Kargupta H, Sarkar K, Gilligan M (2010) MineFleet®;: an overview of a widely adopted distributed vehicle performance data mining system. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, Washington. ACM, pp 37–46
Khan A (2007) Intelligent Infrastructure-based queue-end warning system for avoiding rear impacts. IET Intell Transp Syst 1:138–143
Klein A, Lehner W (2009) Representing data quality in sensor data streaming environments. ACM J Data Inf Qual 1(2):1–28
Oza N (2005) Online bagging and boosting. Proceedings of the IEEE International conference on systems, man and cybernetics, Waikoloa, Hawaii, USA, pp 2340–2345. IEEE. http://dx.doi.org/10.1109/ICSMC.2005.1571498
Schmidt S, Berthold H, Lehner W (2004) QStream: deterministic querying of data streams. In: Nascimento MA, Özsu MT, Kossmann D, Miller RJ, Blakeley JA, Schiefer KB (eds) Proceedings of the thirtieth International conference on very large data bases (VLDB), Toronto. Morgan Kaufmann, San Francisco, Toronto, Canada, pp 1365–1368. http://www.vldb.org/conf/2004/DEMP29.PDF
Statistisches Bundesamt (2011) Unfallentwicklung auf deutschen Straßen 2010. http://www.destatis.de
Acknowledgements
This work has been supported by the German Federal Ministry of Education and Research (BMBF) under the grant 01BU0915 (Project Cooperative Cars eXtended, http://www.aktiv-online.org/english/aktiv-cocar.html ) and by the Research Cluster on Ultra High-Speed Mobile Information and Communication UMIC (http://www.umic.rwth-aachen.de). We thank the PTV AG for kindly providing us with a VISSIM license. We also thank the reviewers for their valuable comments.
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Geisler, S., Quix, C. (2014). Evaluation of Real-Time Traffic Applications Based on Data Stream Mining. In: Cervone, G., Lin, J., Waters, N. (eds) Data Mining for Geoinformatics. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7669-6_5
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