Difference between revisions of "About"
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| − | == | + | == Abstract == |
| − | The project | + | |
| + | <p>The project aims to illustrate the power of visual analytics to highlight patterns vehicles show when traversing through various traffic corridors. By linking the information captured by RFID tags when vehicles move through checkpoints, an interactive application is designed which will help to unravel insights such as frequently travelled corridors, preferred routes amongst vehicles, traffic density, etc. The application will be primarily developed using R, and specifically the versatile <strong><em>ggraph</em> package</strong>, which helps to develop powerful network visualisations.</p> | ||
==Motivation== | ==Motivation== | ||
| − | + | Traffic accumulation is a key problem found in most cities. Urban planning needs to cater to robust planning of vehicle corridors to minimise disruptions in flow, and improve productivity. By visualising the patterns of vehicle movement, it is aimed to better understand the linkages between various points in a predefined vicinity. Capturing timestamp information of vehicle passages helps to understand various parameters such as traffic density, preferred corridors for vehicles and their speeds. The application devised here is also aimed to support the traffic authorities to identify what other alternative corridors might exist for reaching from Point A to point B. | |
| + | |||
| + | ==Practical use cases== | ||
| + | |||
| + | * Traffic planning. | ||
| + | * Systems such as Singapore ERP. | ||
| + | * Implementing diversions during peak periods. | ||
Revision as of 08:40, 24 July 2017
Group1: Unlocking insights from the VAST Challenge 2017
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Abstract
The project aims to illustrate the power of visual analytics to highlight patterns vehicles show when traversing through various traffic corridors. By linking the information captured by RFID tags when vehicles move through checkpoints, an interactive application is designed which will help to unravel insights such as frequently travelled corridors, preferred routes amongst vehicles, traffic density, etc. The application will be primarily developed using R, and specifically the versatile ggraph package, which helps to develop powerful network visualisations.
Motivation
Traffic accumulation is a key problem found in most cities. Urban planning needs to cater to robust planning of vehicle corridors to minimise disruptions in flow, and improve productivity. By visualising the patterns of vehicle movement, it is aimed to better understand the linkages between various points in a predefined vicinity. Capturing timestamp information of vehicle passages helps to understand various parameters such as traffic density, preferred corridors for vehicles and their speeds. The application devised here is also aimed to support the traffic authorities to identify what other alternative corridors might exist for reaching from Point A to point B.
Practical use cases
- Traffic planning.
- Systems such as Singapore ERP.
- Implementing diversions during peak periods.
