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■ Combining Stream Mining and Neural Networks for Short Term Delay Prediction

M. Grzenda, K. Kwasiborska, T. Zaremba: Combining Stream Mining and Neural Networks for Short Term Delay Prediction. Accepted in International Conference on Soft Computing Models in Industrial and Environmental Applications, Leon, Spain. SOCO’17. 6th-8th September 2017

Abstract:

The systems monitoring the location of public transport vehicles rely on wireless transmission. The location readings from GPS-based devices
are received with some latency caused by periodical data transmission and temporal problems preventing data transmission. This negatively affects identification of delayed vehicles.
The primary objective of the work is to propose short term hybrid delay prediction method. The method relies on adaptive selection of Hoeffding trees, being stream classification technique and multilayer perceptrons. In this way, the hybrid method proposed in this study provides anytime predictions and eliminates the need to collect extensive training data before any predictions can be made. Moreover, the use of neural networks increases the accuracy of the predictions compared  with the use of Hoeffding trees only.

BIBTEX:
@Inbook{Grzenda2018,
author="Grzenda, Maciej
and Kwasiborska, Karolina
and Zaremba, Tomasz",
editor="P{\'e}rez Garc{\'i}a, Hilde
and Alfonso-Cend{\'o}n, Javier
and S{\'a}nchez Gonz{\'a}lez, Lidia
and Quinti{\'a}n, H{\'e}ctor
and Corchado, Emilio",
title="Combining Stream Mining and Neural Networks for Short Term Delay Prediction",
bookTitle="International Joint Conference SOCO'17-CISIS'17-ICEUTE'17 Le{\'o}n, Spain, September 6--8, 2017, Proceeding",
year="2018",
publisher="Springer International Publishing",
address="Cham",
pages="188--197",
isbn="978-3-319-67180-2",
doi="10.1007/978-3-319-67180-2_18",
url="https://doi.org/10.1007/978-3-319-67180-2_18"
}