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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Nanyang Technological University
2020
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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 |
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Engineering::Computer science and engineering Yeoh, Yi Sheng Big data analytics for smart transportation |
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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. |
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Mo Li |
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Mo Li Yeoh, Yi Sheng |
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Final Year Project |
author |
Yeoh, Yi Sheng |
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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 |
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Big data analytics for smart transportation |
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Big data analytics for smart transportation |
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big data analytics for smart transportation |
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Nanyang Technological University |
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2020 |
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https://hdl.handle.net/10356/137962 |
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