Effects of weather data on traffic flow predictions using an LSTM deep learning model

Bachelor thesis (2023)

Authors

N. Nachev Electrical Engineering, Mathematics and Computer Science

Contributors

E. Congeduti Computer Science & Engineering-Teaching Team - EEMCS (supervisor 1)
G. Iosifidis Networked Systems - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Nikola Nachev
LSTM Long short term memory Traffic flow predictions Weather effects
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Published Date

28-06-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Accurate traffic forecasts are a key element in improving the traffic flow of urban cities. An efficient approach to this problem is to use a deep learning Long Short Term Memory (LSTM) model. Including weather data in the model can improve prediction accuracy because traffic volumes are sensitive to weather changes. The aim of this study is to show how such a model can be constructed for traffic flow predictions, and how it can be improved with the use of weather data. Results show that an LSTM model gives accurate predictions as a baseline model, and the inclusion of weather data gives a slight improvement in accuracy when predicting single sensors. The improvement was higher on long term predictions of 2.5 hours, and the best prediction results were obtained when adding a lag of 30 minutes to the rain data.