Big data analytics for smart transportation

This report presents how data visualisation can be done using a different approach – by uploading a file containing geographical coordinates – as compared to the previous approaches, which are based on historical data – as well as how data prediction can be done. As data may come in different forms...

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Main Author: Yeoh, Yi Sheng
Other Authors: Mo Li
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/137962
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1379622020-04-20T09:32:55Z Big data analytics for smart transportation Yeoh, Yi Sheng Mo Li School of Computer Science and Engineering limo@ntu.edu.sg Engineering::Computer science and engineering This report presents how data visualisation can be done using a different approach – by uploading a file containing geographical coordinates – as compared to the previous approaches, which are based on historical data – as well as how data prediction can be done. As data may come in different forms and sources, one unique way of visualising data is through plotting of geographical coordinates. This functionality is also tested and proven, by using a Python code, to be able to handle and plot high influx of geographical coordinates fast. Prediction of the MRT Station footfalls for each MRT station is also done, to optimise future situations – deterring the occurrences of negative situations, or maximising benefits from positive situations. Bachelor of Engineering (Computer Science) 2020-04-20T09:32:55Z 2020-04-20T09:32:55Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/137962 en SCSE19-0481 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Computer science and engineering
spellingShingle Engineering::Computer science and engineering
Yeoh, Yi Sheng
Big data analytics for smart transportation
description This report presents how data visualisation can be done using a different approach – by uploading a file containing geographical coordinates – as compared to the previous approaches, which are based on historical data – as well as how data prediction can be done. As data may come in different forms and sources, one unique way of visualising data is through plotting of geographical coordinates. This functionality is also tested and proven, by using a Python code, to be able to handle and plot high influx of geographical coordinates fast. Prediction of the MRT Station footfalls for each MRT station is also done, to optimise future situations – deterring the occurrences of negative situations, or maximising benefits from positive situations.
author2 Mo Li
author_facet Mo Li
Yeoh, Yi Sheng
format Final Year Project
author Yeoh, Yi Sheng
author_sort Yeoh, Yi Sheng
title Big data analytics for smart transportation
title_short Big data analytics for smart transportation
title_full Big data analytics for smart transportation
title_fullStr Big data analytics for smart transportation
title_full_unstemmed Big data analytics for smart transportation
title_sort big data analytics for smart transportation
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/137962
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