Searched for: contributor%3A%22Congeduti%2C+E.+%28mentor%29%22
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Üzel, Ziyar (author)
Accurate short-term traffic forecasting plays a crucial role in Intelligent Transportation Systems for effective traffic management and planning. In this study, the performances of three popular forecasting models are explored: Long Short-Term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and Facebook's Prophet, for short-term...
bachelor thesis 2023
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Verlooy, Bas (author)
Traffic prediction plays a big role in efficient transport planning capabilities and can reduce traffic congestion. In this study the application of Long Short-Term Memory (LSTM) models for predicting traffic volumes across varying prediction horizons is investigated. The data used is collected by the municipality of The Hague for a single month...
bachelor thesis 2023
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Nachev, Nikola (author)
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...
bachelor thesis 2023
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Büthker, Wouter (author)
Due to the increasing popularity of various types of sensors in traffic management, it has become significantly easier to collect data on traffic flow. However, the integrity of these data sets is often compromised due to missing values resulting from sensor failures, communication errors, and other malfunctions. This study investigates the...
bachelor thesis 2023
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Kuiper, Thomas (author)
The amount of cars on the roads is increasing at a rapid pace, causing traffic jams to become commonplace. One way to decrease the amount of traffic congestion is by building an Intelligent Transportation System (ITS) which helps traffic flow optimally. An important tool for an ITS is short term traffic forecasting. Better forecasts will enable...
bachelor thesis 2023
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Lenferink, Luc (author)
The ability to model other agents can be of great value in multi-agent sequential decision making problems and has become more accessible due to the introduction of deep learning into reinforcement learning. In this study, the aim is to investigate the usefulness of modelling other agents using variational autoencoder based models in partially...
master thesis 2023
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