Machine learning applied to traffic forecasting
Typ
Examensarbete på grundnivå
Program
Publicerad
2019
Författare
Aronsson, Linus
Bengtsson, Aron
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Linus Aronsson
Aron Bengtsson
Department of Computer Science and Engineering
Chalmers University of Technology
Abstract
Forecasting future traffic situations is of great importance for improving existing Intelligent
Transportation Systems (ITS). The forecasts allow the various ITS technologies
to function proactively. This provides safer roads with less accidents and lower
congestion levels. Forecasting urban road traffic is challenging as it often follows
complex nonlinear temporal patterns. Traditional statistical forecasting techniques
therefore struggle to achieve accurate predictions. In recent years the computing
power and available historic traffic data has increased drastically. These are two of
the main ingredients required for the field of Machine Learning (ML) to work. Many
ML techniques have been shown to be capable of capturing nonlinear patterns in
data, which makes it a good candidate for traffic forecasting. This thesis therefore
explores various ML technologies and applies them to time series forecasting. Additionally,
some traditional approaches to time series forecasting are evaluated as
baselines for comparison. The experiments conducted used traffic data from central
Gothenburg, which was manually gathered throughout the project. The conclusion
was that the best ML techniques provided a higher forecasting accuracy for both
short-term and long-term predictions. More advanced hyperparameter optimization
and feature engineering would further improve the ML models.
Beskrivning
Ämne/nyckelord
Machine learning , Artificial neural networks , LSTM , Forecasting , Traffic flow , Time series