JKL-Openin raportointityökalun kehittäminen

Tutkielmassa pyrittiin kehittämään uusi ja paranneltu versio raportointiyökalusta, joka on JKL-Open-sivustolla. Nykyinen raportointityökalu on todettu vaillinaiseksi, koska se rikkoutuu aina kun kuu vaihtuu keskellä viikkoa. Työkalussa ei myöskään huomioida kaikkea kerättyä dataa vaan esimerkiksi au...

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Main Author: Peltola, Merika
Other Authors: Informaatioteknologian tiedekunta, Faculty of Information Technology, Informaatioteknologia, Information Technology, Jyväskylän yliopisto, University of Jyväskylä
Format: Master's thesis
Language:fin
Published: 2020
Subjects:
Online Access: https://jyx.jyu.fi/handle/123456789/68908
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author Peltola, Merika
author2 Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä
author_facet Peltola, Merika Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä Peltola, Merika Informaatioteknologian tiedekunta Faculty of Information Technology Informaatioteknologia Information Technology Jyväskylän yliopisto University of Jyväskylä
author_sort Peltola, Merika
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description Tutkielmassa pyrittiin kehittämään uusi ja paranneltu versio raportointiyökalusta, joka on JKL-Open-sivustolla. Nykyinen raportointityökalu on todettu vaillinaiseksi, koska se rikkoutuu aina kun kuu vaihtuu keskellä viikkoa. Työkalussa ei myöskään huomioida kaikkea kerättyä dataa vaan esimerkiksi autot lasketaan vuoden 2009 tutkimuksen perusteella. Tutkielmassa käytettiin tutkimusmenetelmänä konstruktiivista tutkimusotetta, jossa tarkoituksena on luoda ratkaisu tosielämän ongelmaan. Teoriaosassa tutkittiin erilaisia sensoreita, joita voidaan käyttää liikenteen havainnoimiseen. Tarkoituksena oli löytää parhaat mahdolliset sensorit, joiden avulla saadaan paljon laadukasta ja kattavaa dataa Jyväskylän kaupungin liikenteestä. Teoriaosiossa tutkittiin myös liikenteen mallintamista, jotta saataisiin selville, kuinka avointa dataa tulisi esittää ja kuinka yleisesti kerätään avointa dataa liikenteestä. Käytännön osassa esitellään, kuinka toinen tutkielman varsinaisista tuloksista, uusi raportointityökalu JKL-Open-sivustolle, toteutettiin. Tutkielmassa tutkittiin neljää erilaista menetelmää datan analysoimiseen sekä siitä ennustamiseen. Käytännön osiossa on esitelty analysoinnin tuloksia ja kuinka SARIMA:lla ja TensorFlow:lla onnistuuttiin tekemään ennusteita tulevasta liikenteestä. Kalman-suodattimen avulla pyrittiin poistamaan häiriötekijät datasta ja iteraatiokierrosten avulla antamaan arvio tulevasta datasta samalla päivittäen edellistä tilaa. PCA:lla pystyttiin onnistuneesti tunnistamaan kerätystä datasta eri parkkihallit eli datan alkulähteet. Tutkielmassa kehitettiin onnistuneesti uusi raportointityökalu, jolla JKL-Open-sivuston vanha raportointityökalu tullaan korvaamaan. Samalla tutkielmassa saatiin onnistuneesti tehtyä muutamia data-analyysejä ja ennustuksia. Data-analyysien ja ennustuksien tarkkuus kärsi hieman datan vähyydestä ja koronaviruksen aiheuttamista liikenteen rajoittamisista. The goal of this thesis was to develop a new and improved version of the reporting tool that is on JKL-Open. The current version has been deemed incomplete because it breaks every time the month changes in the middle of the week. The tool doesn't take into consideration all the data that has been collected instead cars are calculated based on a research from 2009. The research method in this thesis is constructive research, where the goal is to create a solution for a real life problem. One part of the theory consists of research on different types of sensors that can be used to detect traffic. The goal is to find the best possible sensors that can be used to collect a lot of high quality data that also has a high coverage on the traffic of Jyväskylä. Another part of the theory consists of research on modeling traffic, where the goals are to find out how open data should be presented and how open traffic data is collected in general. The practical part of the thesis presents how one of the results, the new reporting tool was developed. Four different methods for data analysis and prediction were researched in this thesis. the practical part describes the results of the analysis and how predictions on upcoming traffic were successfully made with SARIMA and TensorFlow. The goal of using the Kalman filter was to eliminate the background noise and give an estimate of the next state as well as update the current state. By applying PCA to the data, parking garages were successfully recognised. In the thesis a new reporting tool for JKL-Open was successfully created. Also a few data analyses and predictions were done successfully. The results and their preciseness were not the best due to the amount of data available. The corona virus also played a part on the preciseness of the data as there were restrictions that affected traffic, so the predictions were not able to take that into account.
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spellingShingle Peltola, Merika JKL-Openin raportointityökalun kehittäminen sensori kulkutapajakauma Tietotekniikka Mathematical Information Technology 602 liikenne data tietotekniikka ennusteet avoin tieto
title JKL-Openin raportointityökalun kehittäminen
title_full JKL-Openin raportointityökalun kehittäminen
title_fullStr JKL-Openin raportointityökalun kehittäminen JKL-Openin raportointityökalun kehittäminen
title_full_unstemmed JKL-Openin raportointityökalun kehittäminen JKL-Openin raportointityökalun kehittäminen
title_short JKL-Openin raportointityökalun kehittäminen
title_sort jkl openin raportointityökalun kehittäminen
title_txtP JKL-Openin raportointityökalun kehittäminen
topic sensori kulkutapajakauma Tietotekniikka Mathematical Information Technology 602 liikenne data tietotekniikka ennusteet avoin tieto
topic_facet 602 Mathematical Information Technology Tietotekniikka avoin tieto data ennusteet kulkutapajakauma liikenne sensori tietotekniikka
url https://jyx.jyu.fi/handle/123456789/68908 http://www.urn.fi/URN:NBN:fi:jyu-202005113114
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