Big data analytics for smart transportation (1)
In this technologically advanced world, Big data has benefited multiple organizations to gain valuable insights into the different facets that help them make better decisions. In Singapore, urban mobility is important to achieve a balance between economic growth and a sustainable environment. Moving...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/137956 |
| _version_ | 1826121263523823616 |
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| author | Lye, Chun Min |
| author2 | Mo Li |
| author_facet | Mo Li Lye, Chun Min |
| author_sort | Lye, Chun Min |
| collection | NTU |
| description | In this technologically advanced world, Big data has benefited multiple organizations to gain valuable insights into the different facets that help them make better decisions. In Singapore, urban mobility is important to achieve a balance between economic growth and a sustainable environment. Moving forward, Singapore is working towards faster car-lite transportation by 2040. Therefore, to support the vision of a car-lite Singapore, improving walk modes of transport can be done through the discovery of walkway hotspots using crowdsensing data.
This project consists of a series of steps to discover new walkway areas. Firstly, data cleaning is done on the crowdsensing dataset. Secondly, uncharted walkway areas were estimated using an ellipse model. Thirdly, estimated walkway areas were further refined by using a weighting scheme known as the bivariate Gaussian model. Lastly, analyzed data were visualized on a visualization platform using the Django framework web application. As a result, this will provide informative walkway areas for the end-users. |
| first_indexed | 2024-10-01T05:29:49Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/137956 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:29:49Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1379562020-04-20T07:36:08Z Big data analytics for smart transportation (1) Lye, Chun Min Mo Li School of Computer Science and Engineering limo@ntu.edu.sg Engineering::Computer science and engineering In this technologically advanced world, Big data has benefited multiple organizations to gain valuable insights into the different facets that help them make better decisions. In Singapore, urban mobility is important to achieve a balance between economic growth and a sustainable environment. Moving forward, Singapore is working towards faster car-lite transportation by 2040. Therefore, to support the vision of a car-lite Singapore, improving walk modes of transport can be done through the discovery of walkway hotspots using crowdsensing data. This project consists of a series of steps to discover new walkway areas. Firstly, data cleaning is done on the crowdsensing dataset. Secondly, uncharted walkway areas were estimated using an ellipse model. Thirdly, estimated walkway areas were further refined by using a weighting scheme known as the bivariate Gaussian model. Lastly, analyzed data were visualized on a visualization platform using the Django framework web application. As a result, this will provide informative walkway areas for the end-users. Bachelor of Engineering (Computer Science) 2020-04-20T07:36:08Z 2020-04-20T07:36:08Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/137956 en SCSE19-0478 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Lye, Chun Min Big data analytics for smart transportation (1) |
| title | Big data analytics for smart transportation (1) |
| title_full | Big data analytics for smart transportation (1) |
| title_fullStr | Big data analytics for smart transportation (1) |
| title_full_unstemmed | Big data analytics for smart transportation (1) |
| title_short | Big data analytics for smart transportation (1) |
| title_sort | big data analytics for smart transportation 1 |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/137956 |
| work_keys_str_mv | AT lyechunmin bigdataanalyticsforsmarttransportation1 |