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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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/137962 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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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