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.
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Acknowledgements
This research has been supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 688380 VaVeL: Variety, Veracity, VaLue: Handling the Multiplicity of Urban Sensors.
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Grzenda, M., Kwasiborska, K., Zaremba, T. (2018). Combining Stream Mining and Neural Networks for Short Term Delay Prediction. In: Pérez García, H., Alfonso-Cendón, J., Sánchez González, L., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding. SOCO ICEUTE CISIS 2017 2017 2017. Advances in Intelligent Systems and Computing, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-319-67180-2_18
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DOI: https://doi.org/10.1007/978-3-319-67180-2_18
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