DocumentCode :
2901187
Title :
Online train delay recognition and running time prediction
Author :
Hansen, Ingo A. ; Goverde, Rob M P ; Van Der Meer, Dirk J.
Author_Institution :
Delft Univ. of Technol., Delft, Netherlands
fYear :
2010
fDate :
19-22 Sept. 2010
Firstpage :
1783
Lastpage :
1788
Abstract :
Data mining and analysis of standard track occupation data is used to compute accurate actual train delays at stations. Based on historical train describer records the delays can be automatically classified into initial and consecutive ones through comparison of actual with scheduled blocking times. The distributions of running times and dwell times of each line and direction can be estimated conditional on the amount of original delays, route conflicts and factors such as type of rolling stock, peak hours or even weather conditions. The running times of actual trains until the next downstream stations can be predicted at a high precision by means of a new online model whose parameters have been calibrated and tested in the Dutch railway corridor Rotterdam - The Hague.
Keywords :
data analysis; data mining; delays; pattern recognition; railways; scheduling; Dutch railway corridor Rotterdam; actual train delays; data mining; online train delay recognition; rolling stock; running time prediction; standard track occupation data analysis; Data models; Delay; Integrated circuits; Object oriented modeling; Prediction algorithms; Predictive models; Rail transportation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Transportation Systems (ITSC), 2010 13th International IEEE Conference on
Conference_Location :
Funchal
ISSN :
2153-0009
Print_ISBN :
978-1-4244-7657-2
Type :
conf
DOI :
10.1109/ITSC.2010.5625081
Filename :
5625081
Link To Document :
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