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DKUM
EPF - Faculty of Business and Economics
FE - Faculty of Energy Technology
FERI - Faculty of Electrical Engineering and Computer Science
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UM - University of Maribor
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COBISS
Faculty of Business and Economic, Maribor
Faculty of Agriculture and Life Sciences, Maribor
Faculty of Logistics, Celje, Krško
Faculty of Organizational Sciences, Kranj
Faculty of Criminal Justice and Security, Ljubljana
Faculty of Health Sciences
Library of Technical Faculties, Maribor
Faculty of Medicine, Maribor
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Title:
Napovedovanje gostote prometa z uporabo strojnega učenja
Authors:
ID
Kralj, Andraž
(
Author
)
ID
Mongus, Domen
(
Mentor
)
More about this mentor...
Files:
MAG_Kralj_Andraz_2023.pdf
(17,28 MB)
MD5: E0222F147E3F8D3A3D0ACBACDF688D05
Language:
Slovenian
Work type:
Master's thesis/paper
Typology:
2.09 - Master's Thesis
Organization:
FERI - Faculty of Electrical Engineering and Computer Science
Abstract:
V magistrskem delu naslavljamo problem dolgoročnih napovedi prometa. V ta namen najprej predstavimo sorodno delo in podamo teoretično osnovo izvedbe različnih modelov strojnega učenja ter kodiranja cikličnih podatkov. V nadaljevanju podrobneje predstavimo naš pristop, ki omogoča izvedbo letnih napovedi z urno ločljivostjo. Različne pristope pri tem sistematično primerjamo in z rezultati pokažemo, da smo obravnavni problem najučinkoviteje naslovili z uporabo metode XBoost in kodiranja cikličnih podatkov s podobnostjo.
Keywords:
strojno učenje
,
promet
,
ansambel dreves
,
značilnice
,
obdelava podatkov
Place of publishing:
Maribor
Year of publishing:
2023
PID:
20.500.12556/DKUM-86158-1c270c83-430a-d5cf-602b-03fd2525ac4e
Publication date in DKUM:
07.11.2023
Views:
630
Downloads:
91
Metadata:
Categories:
KTFMB - FERI
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Licences
License:
CC BY-NC-SA 4.0, Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International
Link:
http://creativecommons.org/licenses/by-nc-sa/4.0/
Description:
A Creative Commons license that bans commercial use and requires the user to release any modified works under this license.
Licensing start date:
15.10.2023
Secondary language
Language:
English
Title:
Traffic density forecasting using machine learning
Abstract:
In this master’s thesis, we address the problem of long-term traffic forecasts. For this purpose, we first address related work and provide the theoretical basis for the implementation of various machine learning models and representations of cyclic data. In continuation, we detail the proposed approach that allows for annual forecasting with an hourly temporal resolution. The different machine learning models used are than systematically compared. As shown by the results, the best results for the addressed problem were obtained using the XBoost method and encoding cyclic data with similarity.
Keywords:
machine learning
,
traffic
,
ensemble trees
,
features
,
data processing
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