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Advances in Statistical Pattern Recognition
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A Statistical Learning/Pattern Recognition Glossary
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Niya Yadav. Jeremy Orina , Bioinformatics student. Specifically, data captured and communicated by intelligent systems are to be supplemented by reliable predictive travel time. This paper reports a model for real-time prediction of urban bus running time that is based on statistical pattern recognition technique, namely locally weighted scatter smoothing.
Given a pattern that characterizes the conditions for which bus running time is being predicted, the trained model automatically searches through the historical patterns which are the most similar to the current pattern and on that basis, the prediction is made.
ENEE633: Statistical Pattern Recognition
For training and testing of the methodology, data retrieved from the automatic vehicle location and automatic passenger counter systems of OC Transpo Ottawa, Canada were used. A comparison with other methodologies shows enhanced predictive capability.
The authors thank the staff of OC Transpo in Ottawa for data provision. Skip to Main Content.
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