An evaluation framework for traffic information systems based on data streams

Transportation Research Part C: Emerging Technologies

Volumn 23, Issue , 2012, Pages 29-55

  Geisler, Sandra ^a   Quix, Christoph ^a   Schiffer, Stefan ^a   Jarke, Matthias ^a,b  

^a RWTH AACHEN UNIVERSITY   (Germany)

^b FRAUNHOFER INSTITUTE FOR APPLIED INFORMATION TECHNOLOGY FIT   (Germany)

Author keywords

Car to X communication; Data stream mining; Data streams

Indexed keywords

COMPUTER SOFTWARE; CONTINUOUS TIME SYSTEMS; DATA MINING; DATABASE SYSTEMS; HIGHWAY TRAFFIC CONTROL; INFORMATION MANAGEMENT; INFORMATION SYSTEMS; INFORMATION USE; MANAGEMENT INFORMATION SYSTEMS; MOBILE TELECOMMUNICATION SYSTEMS; PROBES; REAL TIME SYSTEMS; ROADS AND STREETS; STREET TRAFFIC CONTROL;

CAR-TO-X COMMUNICATIONS; DATA STREAM MANAGEMENT SYSTEMS; DATA STREAM MINING; DESIGN AND IMPLEMENTATIONS; EVALUATION FRAMEWORK; MOBILE COMMUNICATION TECHNOLOGY; TRAFFIC INFORMATION SYSTEMS; TRAFFIC STATE ESTIMATION;

DATA STREAMS;

DATA MANAGEMENT; DATA MINING; INFORMATION SYSTEM; MOBILE COMMUNICATION; TRAFFIC MANAGEMENT;

EID: 84859110322     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trc.2011.08.003     Document Type: Article

References (67)

1. Abadi, D.J., Ahmad, Y., Balazinska, M., çetintemel, U., Cherniack, M., Hwang, J.H., Lindner, W., Maskey, A., Rasin, A., Ryvkina, E., Tatbul, N., Xing, Y., Zdonik, S.B., 2005. The design of the borealis stream processing engine. In: Proc. 2nd Biennal Conference on Innovative Data Systems Research (CIDR). Asilomar, CA, USA, pp. 277-289.
2. Aberer K., Hauswirth M., Salehi A. A middleware for fast and flexible sensor network deployment. Proc. 32nd Intl. Conference on Very Large Data Bases (VLDB) 2006, 1199-1202. ACM Press. U. Dayal, K.Y. Whang, D.B. Lomet, G. Alonso, G.M. Lohman, M.L. Kersten, S.K. Cha, Y.K. Kim (Eds.).
3. Aggarwal C., Yu P. A framework for clustering uncertain data streams. IEEE 24th International Conference on Data Engineering 2008, 150-159. IEEE.
4. Ahmad, Y., çetintemel, U., 2009. Data stream management architectures and prototypes. In: Liu and özsu (2009), pp. 639-643 (Chapter D).
5. Ali, M., Chandramouli, B., Raman, B.S., Katibah, E., 2010a. Real-time spatio-temporal analystics using microsoft streaminsight. In: Proc. of the 18th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, USA, pp. 542-543.
6. Ali M.H., Chandramouli B., Raman B.S., Katibah E. Spatio-temporal stream processing in microsoft streaminsight. IEEE Data Engineering Bulletin 2010, 33:69-74.
7. Angelov, P., Zhou, X., 2008. Online learning fuzzy rule-based system structure from data streams. In: Proc. of the IEEE International Conference on Fuzzy Systems, pp. 915-922.
8. Arasu A., Babcock B., Babu S., Datar M., Ito K., Nishizawa I., Rosenstein J., Widom J. STREAM: the stanford stream data manager. Proc. of the ACM SIGMOD Intl. Conference on Management of Data 2003, ACM, San Diego, California, USA, p. 665. A.Y. Halevy, Z.G. Ives, A. Doan (Eds.).
9. Arasu A., Babu S., Widom J. The CQL continuous query language: semantic foundations and query execution. The VLDB Journal 2006, 15:121-142.
10. 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: Proc. of the 30th International Conference on Very Large Data Bases (VLDB), VLDB Endowment, pp. 480-491.
11. Babcock B., Babu S., Datar M., Motwani R., Widom J. Models and issues in data stream systems. Proc. 21st ACM Symposium on Principles of Database Systems (PODS) 2002, 1-16. ACM Press, Madison, Wisconsin. L. Popa (Ed.).
12. Bai, Y., Thakkar, H., Luo, C., Wang, H., Zaniolo, C., 2006. A data stream language and system designed for power and extensibility. In: Yu, P.S., Tsotras, V.J., Fox, E.A., Liu, B. (Eds.), Proc. ACM Intl. Conf. on Information and Knowledge Management (CIKM). Arlington, Virginia, pp. 337-346.
13. BASt, 1999. Merkblatt für die Ausstattung von Verkehrsrechnerzentralen und Unterzentralen (MARZ). Bundesanstalt für Straßenwesen. (in German).
14. Batista G., Prati R., Monard M. A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explorations Newsletter 2004, 6:20-29.
15. Ben-Akiva M., Bierlaire M., Burton D., Koutsopoulos H., Mishalani R. Network state estimation and prediction for real-time traffic management. Networks and Spatial Economics 2001, 1:293-318.
16. Biem, A., Bouillet, E., Feng, H., Ranganathan, A., Riabov, A., Verscheure, O., Koutsopoulos, H., Moran, C., 2010. IBM infosphere streams for scalable, real-time, intelligent transportation services. In: Proc. of SIGMOD'10, pp. 1093-1104.
17. Bifet, A., Gavalda, R., 2007. Learning from time-changing data with adaptive windowing. In: Proc. of the SIAM International Conference on Data Mining, pp. 443-448.
18. Bifet A., Holmes G., Kirkby R., Pfahringer B. MOA: massive online analysis. Journal of Machine Learning Research 2010, 11:1601-1604.
19. Bifet, A., Holmes, G., Pfahringer, B., Kranen, P., Kremer, H., Jansen, T., Seidl, T., 2010b. MOA: massive online analysis, a framework for stream classification and clustering. In: Proc. of the Intl. Workshop on Handling Concept Drift in Adaptive Information Systems (HaCDAIS), pp. 3-16.
20. Bifet A., Kirkby R. Data Stream Mining - A Practical Approach 2009, University of Waikato, .
21. Bifet A., Kirkby R. Massive Online Analysis - Manual 2009, The University of Waikato, .
22. 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, 5-12. (in German).
23. Chan, T.N., Lee, C., Hellinga, B., 2003. Real-time Identification and tracking of traffic queues based on average link speed. In: 82nd Annual Meeting of the Transportation Research Board.
24. Chen H., Grant-Muller S. Use of sequential learning for short-term traffic flow forecasting. Transportation Research Part C: Emerging Technologies 2001, 9:319-336.
25. Cheng J., Ke Y., Ng W. A survey on algorithms for mining frequent itemsets over data streams. Knowledge and Information Systems 2008, 16:1-27.
26. Cherniack, M., Zdonik, S., 2009. Stream-oriented query languages and architectures. In: Liu and özsu (2009). pp. 2848-2854 (Chapter S).
27. Delot T., Ilarri S., Thilliez M., Vargas-Solar G., Bellengier M. Highly mobile query processing. Networks and Technologies (ANT 2010), Paris (France) 2010, 101-108. Austrian Computer Society (OCG).
28. Domingos P., Hulten G. Mining high-speed data streams. Proc. of 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2000, 71-80. ACM.
29. Domingos P., Hulten G. A general framework for mining massive data streams. Journal of Computational and Graphical Statistics 2003, 12:945-949.
30. Etzion O., Niblett P. Event Processing in Action 2011, Manning Publications Co.
31. Fontaine M.D., Smith B.L. Probe-based traffic monitoring systems with wireless location technology. Transportation Research Record 2005, 1925:3-11.
32. Fontaine M.D., Smith B.L. Investigation of the performance of wireless location technology-based traffic monitoring systems. Journal of Transportation Engineering 2007, 133:157-165.
33. Gama J., Medas P., Castillo G., Rodrigues P. Learning with drift detection. Proc. 17th Brazilian Symposium on Artificial Intelligence (SBIA) 2004, Springer, p. 286.
34. Gama J., Rodrigues P. An overview on mining data streams. Foundations of Computational Intelligence 2009, 6:29-45.
35. Gama J., Sebastião R., Rodrigues P.P. Issues in evaluation of stream learning algorithms. Proc. of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2009, 329-338. ACM. J.F. Elder, F. Fogelman-Soulié, P.A. Flach, M.J. Zaki (Eds.).
36. Garcia-Molina H., Ullman J.D., Widom J. Extended operators of relational algebra. Database Systems: The Complete Book 2009, 213-222. Prentice Hall Press, Upper Saddle River, NJ, USA. second ed.
37. Geisler, S., Chen, Y., Quix, C., Gehlen, G., 2010a. Accuracy assessment for traffic information derived from floating phone data. In: Proc. of the 17th World Congress on Intelligent Transportation Systems and Services.
38. Geisler, S., Quix, C., Gehlen, G.G., Jodlauk, G., 2009. A quality- and priority-based traffic information fusion architecture. In: Proc. of the 16th World Congress on Intelligent Transport Systems and Services.
39. Geisler, S., Quix, C., Schiffer, S., 2010b. A data stream-based evaluation framework for traffic information systems. In: Ali, M., Hoel, E., Shahabi, C. (Eds.), Proc. of the 1st ACM SIGSPATIAL International Workshop on GeoStreaming, pp. 11-18.
40. Golab L., özsu M.T. Data stream management. Synthesis Lectures on Data Management 2010, Morgan & Claypool Publishers.
41. Han J., Kamber M. Classification and predicition. Data Mining: Concepts and Techniques 2006, 285-382. Morgan Kaufmann.
42. Hashemi S., Yang Y., Mirzamomen Z., Kangavari M. Adapted one-versus-all decision trees for data stream classification. IEEE Transactions On Knowledge and Data Engineering 2009, 21:624-637.
43. Horovitz O., Krishnaswamy S., Gaber M.M. A fuzzy approach for interpretation of ubiquitous data stream clustering and its application to road safety. Intelligent Data Analysis 2007, 11:89-108.
44. Hoyer, R., 2003. Hinweise zur Datenvervollständigung und Datenaufbereitung in verkehrstechnischen Anwendungen. Technical Report. Forschungsgesellschaft für Strassen- und Verkehrswesen. (in German).
45. Huber, W., 2001. Vehicle-generated Data for Aquiring Traffic Information. Ph.D. Thesis. TU München.
46. Hulten G., Spencer L., Domingos P. Mining time-changing data streams. Proc. of the 7th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining 2001, 97-106. ACM.
47. Jain N., Mishra S., Srinivasan A., Gehrke J., Widom J., Balakrishnan H., çetintemel U., Cherniack M., Tibbetts R., Zdonik S. Towards a streaming SQL standard. Proceedings of the VLDB Endowment 2008, 1:1379-1390.
48. Jensen, C.S., Tradisauskas, N., 2009. Map matching. In: Liu and özsu (2009). (Chapter M).
49. Kargupta H., Sarkar K., Gilligan M. MineFleet®: an overview of a widely adopted distributed vehicle performance data mining system. Proc. of the 16th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining 2010, 37-46. ACM.
50. Kerner B.S. The Physics of Traffic 2004, Springer.
51. Khan A. Intelligent infrastructure-based queue-end warning system for avoiding rear impacts. IET Intelligent Transport Systems 2007, 1:138-143.
52. Laskov P., Gehl C., Krüger S., Müller K. Incremental support vector learning: analysis, implementation and applications. The Journal of Machine Learning Research 2006, 7:1909-1936.
53. Encyclopedia of Database Systems 2009, Springer. L. Liu, M.T. özsu (Eds.).
54. Liu Y., Choudhary A., Zhou J., Khokhar A. A scalable distributed stream mining system for highway traffic data. Knowledge Discovery in Databases 2006, 309-321. Springer. J. Fürnkranz, T. Scheffer, M. Spiliopoulou (Eds.).
55. Nagel K., Schreckenberg M. A cellular automaton model for freeway traffic. Journal de Physique 1992, 2:2221-2229.
56. Oza N. Online bagging and boostin. Proc. of the IEEE International Conference on Systems, Man and Cybernetics 2006, 2340-2345. IEEE.
57. Oza, N.C., Russell, S., 2001. Online bagging and boosting. In: Jaakkola, T., Richardson, T. (Eds.), Proc. of the 8th International Workshop on Artificial Intelligence and Statistics. Morgan Kaufmann, Key West, Florida. USA. pp. 105-112.
58. Patroumpas, K., Sellis, T.K., 2006. Window specification over data streams. In: Current Trends in Database Technology - EDBT 2006 Workshops, pp. 445-464.
59. Pelossof, R., Jones, M., Vovsha, I., Rudin, C., 2010. Online coordinate boosting. In: Proc. of the 12th International Conference on Computer Vision, Workshops, IEEE. pp. 1354-1361.
60. Srivastava U., Widom J. Flexible time management in data stream systems. Proc. 23rd ACM Symposium on Principles of Database Systems (PODS) 2004, 263-274. ACM, Paris, France. A. Deutsch (Ed.).
61. Stathopoulos A., Karlaftis M. A multivariate state space approach for urban traffic flow modeling and prediction. Transportation Research Part C 2003, 11:121-135.
62. Stonebraker M., çetintemel U., Zdonik S.B. The 8 requirements of real-time stream processing. SIGMOD Record 2005, 34:42-47.
63. Thakkar, H., Mozafari, B., Zaniolo, C., 2008. Designing an inductive data stream management system: the stream mill experience. In: Proc. of the 2nd International Workshop on Scalable Stream Processing System. ACM, pp. 79-88.
64. Tsymbal, A., 2004. The problem of concept drift: definitions and related work. Technical Report TCD-CS-2004-15. Informe técnico: Departament of Computer Science, Trinity College. Dublin, IE. <>. http://https://www.cs.tcd.ie/publications/techreports/reports.
65. Wang Y., Papageorgiou M. Real-time freeway traffic state estimation based on extended Kalman filter: a general approach. Transportation Research Part B: Methodological 2005, 39:141-167.
66. White C.E., Bernstein D., Kornhauser A.L. Some map matching algorithms for personal navigation assistants. Transportation Research Part C: Emerging Technologies 2000, 8:91-108.
67. Wojnarski, M., Góra, P., Szczuka, M.S., Nguyen, H.S., Swietlicka, J., Zeinalipour-Yazti, D., 2010. IEEE ICDM 2010 contest: tomtom traffic prediction for intelligent GPS navigation. In: Fan, W., Hsu, W., Webb, G.I., Liu, B., Zhang, C., Gunopulos, D., Wu, X. (Eds.), 10th IEEE Intl. Conf. on Data Mining - Workshop Proceedings (ICDMW). IEEE Computer Society, Sydney, Australia, pp. 1372-1376.