A web system for map-matching trajectories on road networks
It is expected that by 2050, more than 2.5 billion people will reside in cities. With the proliferation of location sensing devices, the generation of large urban mobility data such as Global Positioning System (GPS) data points has exploded. This big data embeds rich knowledge about traffic flow an...
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2022
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sg-ntu-dr.10356-1629362022-11-14T05:44:49Z A web system for map-matching trajectories on road networks Tan, Fiora Bei Lun Long Cheng School of Computer Science and Engineering c.long@ntu.edu.sg Engineering::Computer science and engineering::Software::Software engineering It is expected that by 2050, more than 2.5 billion people will reside in cities. With the proliferation of location sensing devices, the generation of large urban mobility data such as Global Positioning System (GPS) data points has exploded. This big data embeds rich knowledge about traffic flow and control, route planning, fare calculation for ride-hailing applications and so on. However, the GPS data recorded are often noisy and do not match the road. There is a need to match these noisy GPS data points to edges in an existing street network to have an accurate depiction of the travel route that was taken by a vehicle. This process is known as map-matching. Map-matching the GPS data points allows them to provide more value and insights for further analysis. This project aims to design a web system that utilises map-matching algorithms to model and predict these dynamics based on the GPS data collected from different domains in the urban space. More specifically, users can utilize the web system to compare recorded geographic coordinates to a logical representation of the real world using different map-matching algorithms. Bachelor of Engineering (Computer Science) 2022-11-14T05:44:48Z 2022-11-14T05:44:48Z 2022 Final Year Project (FYP) Tan, F. B. L. (2022). A web system for map-matching trajectories on road networks. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162936 https://hdl.handle.net/10356/162936 en SCSE21-0918 application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering::Software::Software engineering Tan, Fiora Bei Lun A web system for map-matching trajectories on road networks |
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It is expected that by 2050, more than 2.5 billion people will reside in cities. With the proliferation of location sensing devices, the generation of large urban mobility data such as Global Positioning System (GPS) data points has exploded. This big data embeds rich knowledge about traffic flow and control, route planning, fare calculation for ride-hailing applications and so on.
However, the GPS data recorded are often noisy and do not match the road. There is a need to match these noisy GPS data points to edges in an existing street network to have an accurate depiction of the travel route that was taken by a vehicle. This process is known as map-matching. Map-matching the GPS data points allows them to provide more value and insights for further analysis.
This project aims to design a web system that utilises map-matching algorithms to model and predict these dynamics based on the GPS data collected from different domains in the urban space. More specifically, users can utilize the web system to compare recorded geographic coordinates to a logical representation of the real world using different map-matching algorithms. |
author2 |
Long Cheng |
author_facet |
Long Cheng Tan, Fiora Bei Lun |
format |
Final Year Project |
author |
Tan, Fiora Bei Lun |
author_sort |
Tan, Fiora Bei Lun |
title |
A web system for map-matching trajectories on road networks |
title_short |
A web system for map-matching trajectories on road networks |
title_full |
A web system for map-matching trajectories on road networks |
title_fullStr |
A web system for map-matching trajectories on road networks |
title_full_unstemmed |
A web system for map-matching trajectories on road networks |
title_sort |
web system for map-matching trajectories on road networks |
publisher |
Nanyang Technological University |
publishDate |
2022 |
url |
https://hdl.handle.net/10356/162936 |
_version_ |
1751548594722177024 |