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Taxi demand prediction using deep learning and crowd insights

Master-uppsats

Jolérus, Henrik

KTH/Skolan för elektroteknik och datavetenskap (EECS)

Publicerad: 2024

Språk: Engelska

Nyckelord

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⌕Artificial Intelligence⌕machine learning⌕Artificiell Intelligens⌕maskininlärning⌕deep learning⌕Djupinlärning⌕Long Short Term Memory (LSTM)⌕Graph neural networks⌕Time series regression⌕Recurrent Neural Networks (RNN)⌕Graph convolutional networks (GCN)⌕Intelligent transportation systems (ITP)⌕Short term traffic prediction (STTP)⌕Traffic forecasting⌕Crowd insights⌕Regression av tidsserier⌕Smarta transportsystem⌕Trafikprognos⌕Folkströmsdata

Sammanfattning

Real-time prediction of taxi demand in a discrete geographical space is useful as it can minimise service disequilibrium by informing idle drivers of the imbalance, incentivising them to reduce it. This, in turn, can lead to improved efficiency, more stimulating work conditions, and a better customer experience. This study aims to investigate the possibility of utilising an artificial neural network model to make such a prediction for Stockholm. The model was trained on historical demand data and - uniquely - crowd flow data from a cellular provider (aggregated and anonymised). Results showed that the final model could generate very helpful predictions (only off by less than 1 booking on average). External factors - including crowd flow data - had a minor positive impact on performance, but limitations regarding the setup of the zones lead to the study being unable to make a definitive conclusion about whether crowd flow data is effective in improving taxi demand predictors or not.

Information

Författare
Jolérus, Henrik
Lärosäte / institution
KTH/Skolan för elektroteknik och datavetenskap (EECS)
Publiceringsdatum
2024
Uppsatstyp
Master-uppsats
Språk
Engelska
Nyckelord
⌕Artificial Intelligence⌕machine learning⌕Artificiell Intelligens⌕maskininlärning⌕deep learning⌕Djupinlärning⌕Long Short Term Memory (LSTM)⌕Graph neural networks⌕Time series regression⌕Recurrent Neural Networks (RNN)⌕Graph convolutional networks (GCN)⌕Intelligent transportation systems (ITP)⌕Short term traffic prediction (STTP)⌕Traffic forecasting⌕Crowd insights⌕Regression av tidsserier⌕Smarta transportsystem⌕Trafikprognos⌕Folkströmsdata

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