Identyfikatory
Warianty tytułu
Identification of patterns in road traffic
Języki publikacji
Abstrakty
Artykuł dotyczy analizy wzorców danych dotyczących stanu ruchu pojazdów. W szczególności skupiono się na analizie częstych sekwencji. Analizowane dane zostały pozyskane w oparciu o wieloagentowy symulator do modelowania i optymalizacji ruchu drogowego.
The paper concerns the analysis of data about road traffic. We are focusing our analysis on the frequent sequences. The analysed data was obtained using multi-agent simulator for modelling and optimisation of road traffic.
Słowa kluczowe
Wydawca
Rocznik
Tom
Strony
263--270
Opis fizyczny
Bibliogr. 15 poz., rys., wykr.
Twórcy
autor
- AGH Akademia Górniczo-Hutnicza, Wydział Elektrotechniki, Automatyki, Informatyki i Elektroniki, Katedra Informatyki
autor
- AGH Akademia Górniczo-Hutnicza, Wydział Elektrotechniki, Automatyki, Informatyki i Elektroniki, Katedra Informatyki
Bibliografia
- [1] Agrawal R., Srikant R., Mining seąuentialpatterns. ICDE'95. 1995.
- [2] Bajwa S., Chung E., Kuwahara M., Performance evaluation ofan adaptive travel timeprediction model. Intelligent Transportation Systems, 2005. Proceedings. 2005 IEEE, vol., no., pp. 1000-1005, 13-15 Sept. 2005.
- [3] Bazzan A.L.C., Kliigl F., Multi-agent systems for traffic and transportation engineering. Information Science Reference, 2009.
- [4] Chung E., Chung, E., Classification oj trafficpattern. lOth World Congress on Intelligent Transport Systems, Madrid, Spain, 2003.
- [5] De Fabritiis, C, Ragona, R., Valenti G., Traffic Estimation And Prediction Based On Real Time Floating Car Data. 2008 llth International IEEE Conference on Intelligent Transportation Systems, 2008, 197-203.
- [6] Han J., Pei J., Mortazavil-Asl B., Chen Q., Dayal U., Hsu M.-C, FreeSpan: Freąuent Pattern-Projected Seąuential Pattern Mining. KDD'0), Boston, MA, August 2000.
- [7] Han J., Kamber M., Data Minining. Concepts and Techniąues. Morgan Kaufmann Publishers, 2006.
- [8] Koźlak J., Dobrowolski G., Kisiel-Dorohinicki M., Nawarecki E., Anticrisis management of city traffic using agent-based approach. Journal of Universal Computer Science, 2008, vol. 14, iss. 14, 2359-2380.
- [9] Mark C.D., Sadek, A.W., Rizzo, D., Predicting experienced travel time with neural networks: a PARAMICS simulation study. Intelligent Transportation Systems, 2004. Proceedings. The 7th International IEEE Conference on , vol, no., pp. 906-911, 3-6 Oct. 2004.
- [10] Srikant R., Agrawal R., Mining seąuential patterns: Generalizations and performance improvements. EDBT'96, 1996. [GSP]
- [11] Theodoridis S., Koutroumbas K., Pattern Recognition. Fourth Edition. Elsevier, 2009.
- [12] Pei J., Han J, Pinto H., ChenQ., Dayal U., Hsu M.-C, PrefixSpan: Mining Seąuential Patterns Efficiently by Prefix-Projected Pattern Growth. ICDE'01, Heidelberg, 2001.
- [13] Wei-Hsun L., Shian-Shyong T., Sheng-Han T., A knowledge based real-time travel time prediction system for urban network. Expert Systems with Applications, vol. 36, iss. 3, Part 1, April 2009, 4239^247.
- [14] Yan X., Han J., Afshar R., CloSpan: Mining Closed Seąuential Patterns in Large Datasets. SDM'03, 2003.
- [15] Zaki M., SPADE: An Effident Algorithm for Mining Freąuent Seąuences. Machine Learning, 2001.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-AGH1-0027-0039