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

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

Nachev, Nikola (TU Delft Electrical Engineering, Mathematics and Computer Science)

Congeduti, E. (mentor)
Iosifidis, G. (graduation committee)

Delft University of Technology

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-06-28

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.

Traffic flow predictions
long short term memory
weather effects
LSTM

http://resolver.tudelft.nl/uuid:fcf3a9b3-9bee-4e52-a63f-8ccc7f1cef0c

Student theses

bachelor thesis

© 2023 Nikola Nachev