Intelligent transport support systems have had a major impact on people’s urban mobility. In large urban centers, transportation services still need ways to optimize vehicle supply in certain areas, according to the demand in each of them. This work proposes a stream learning methodology for taxi demand prediction, that tackles concept drift detection on a real taxi data stream.

A Methodology for Taxi Demand Prediction Using Stream Learning

Smart cities should be built on harmonious relationships with the environment, with rational measures to use environmental resources in many ways. Resources essential to human life such as water and energy must have special attention. With the advance of technologies use, human being becomes one of the main promoters of information and solutions for smart cities. Stream machine learning is an approach where data becomes available sequentially, called data stream, which adapts the prediction model with fixed window-sized data stream. The objective of this work is to analyze the effectiveness of buildings energy consumption using stream learning. To achieve this goal, MOA tool was used to evaluate the performance of the selected stream learning algorithms on an an open database. Batch algorithms were also used as testbed, to analyze the performance of stream algorithms related to them

Energy Consumption Prediction using Data Stream Learning for Commercial Buildings

Signal and Image Processing for Smart Cities I

Signal and Image Processing for Smart Cities II

technical paper

The 27th International Conference on Systems, Signals and Image Processing.

IWSSIP 2020

live discussion

Universidade Federal Fluminennse

online learning

big data

energy

urban mobility

stream data mining

predictive algorithm

data stream learning

Flavia Bernardini

4

Presentations

A Methodology for Taxi Demand Prediction Using Stream Learning

Energy Consumption Prediction using Data Stream Learning for Commercial Buildings

Signal and Image Processing for Smart Cities I

Signal and Image Processing for Smart Cities II

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