Hybrid short term prediction to address limited timeliness of public transport data streams

Abstract

In spite of the diversity of solutions developed in the Internet of Things (IoT) domain, some features are shared by numerous IoT deployments and the data they process. These include incompleteness and latency in data transmission from multiple distributed objects. Among others, the systems tracking the location of vehicles are affected by these problems. The primary objective of this work is to address the way the latency in location data acquisition, referred to also as timeliness, can be dealt with. We propose a hybrid method combining machine learning models such as multilayer perceptrons trained in batch mode and online learning methods to perform short-term prediction of vehicle delay data. In this way, stream instances that have not arrived yet from the sensors can be temporarily replaced with predicted values. The method we propose successfully integrates stream mining methods developed for stationary and non-stationary conditions i.e. also the methods developed for concept drifting data streams. For all examined reference data sets and hybridised stream methods, the method reduced prediction error and addressed the risk of using static prediction models not matching or no longer matching the evolving process for which the prediction is performed.