ANALYSIS OF NEURAL NETWORK AND TIME SERIES MODELS FOR PREDICTION OF THE NUMBER OF VEHICLES ENTERING BANDUNG CITY THROUGH THE PASTEUR TOLL
Prediction of traffic information is a complicated problem because of the many factors that influence it. Toll roads are often the main focus in predicting traffic information. Data on the number of vehicles on toll roads is arranged based on the time of observation, thus forming a time series da...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/39141 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Prediction of traffic information is a complicated problem because of the many factors
that influence it. Toll roads are often the main focus in predicting traffic information.
Data on the number of vehicles on toll roads is arranged based on the time of
observation, thus forming a time series data. To find out information on traffic in the
future, it takes information from the past to describe the time series. The mathematical
model used to predict time series data is ARIMA and Seasonal ARIMA. Along with
technological developments, alternative methods emerged such as artificial neural
networks and recurrent neural networks. In this final project a comparative analysis is
carried out between Seasonal ARIMA models, artificial neural networks, and recurrent
neural networks on data on the number of vehicles entering Bandung via Pasteur Toll
Road. The neural network model produces more satisfying results based on mean
absolute error value produced. |
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