Předpovídání časových řad v městské dopravě pomocí RNN LSTM
City traffic time series forecasting using RNN LSTM
Type of document
diplomová prácemaster thesis
Author
Martin Křeček
Supervisor
Cejnek Matouš
Opponent
Peichl Adam
Field of study
Automatizace a průmyslová informatikaStudy program
Automatizační a přístrojová technikaInstitutions assigning rank
ústav přístrojové a řídící technikyRights
A university thesis is a work protected by the Copyright Act. Extracts, copies and transcripts of the thesis are allowed for personal use only and at one?s own expense. The use of thesis should be in compliance with the Copyright Act http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf and the citation ethics http://knihovny.cvut.cz/vychova/vskp.htmlVysokoškolská závěrečná práce je dílo chráněné autorským zákonem. Je možné pořizovat z něj na své náklady a pro svoji osobní potřebu výpisy, opisy a rozmnoženiny. Jeho využití musí být v souladu s autorským zákonem http://www.mkcr.cz/assets/autorske-pravo/01-3982006.pdf a citační etikou http://knihovny.cvut.cz/vychova/vskp.html
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Show full item recordAbstract
This diploma thesis focuses on the development of a comprehensive forecasting system for urban mobility, with a specific emphasis on parking occupancy and public transport delays. Robust data collection and preprocessing pipelines are established to incorporate data from diverse sources. State-of-the-art machine learning techniques, particularly Long Short-Term Memory (LSTM) models, are implemented to forecast urban mobility variables. External factors such as weather data are integrated to enhance forecasting accuracy. A user-friendly web application is developed for data exploration and analysis. The research highlights the potential of data-driven forecasting systems in optimizing transportation efficiency and contributes to the field of intelligent transportation systems. This diploma thesis focuses on the development of a comprehensive forecasting system for urban mobility, with a specific emphasis on parking occupancy and public transport delays. Robust data collection and preprocessing pipelines are established to incorporate data from diverse sources. State-of-the-art machine learning techniques, particularly Long Short-Term Memory (LSTM) models, are implemented to forecast urban mobility variables. External factors such as weather data are integrated to enhance forecasting accuracy. A user-friendly web application is developed for data exploration and analysis. The research highlights the potential of data-driven forecasting systems in optimizing transportation efficiency and contributes to the field of intelligent transportation systems.
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- Diplomové práce - 12110 [158]