Difference between revisions of "About"
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== Abstract == | == Abstract == | ||
| − | <p> | + | <p><span style="font-size: 11pt; font-family: Arial; color: #000000; background-color: transparent; font-weight: 400; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">Transportation networks are key lifelines that aid movement of people, goods, services and resources that are vital to the productivity of a nation. A good visualization of corridors along which vehicular transport moves is key in understanding patterns of such movement. Using a dataset that captures that timestamp information of vehicles passing through a wildlife preserve, a network visualization application is created using R Shiny as the platform. The insights derived can help understand metrics such as traffic density along corridors, the directions of traffic flow, and the daily and seasonal patterns of traffic flow.</span></p> |
==Motivation== | ==Motivation== | ||
Revision as of 14:56, 4 August 2017
Group1: Unlocking insights from the VAST Challenge 2017
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Abstract
Transportation networks are key lifelines that aid movement of people, goods, services and resources that are vital to the productivity of a nation. A good visualization of corridors along which vehicular transport moves is key in understanding patterns of such movement. Using a dataset that captures that timestamp information of vehicles passing through a wildlife preserve, a network visualization application is created using R Shiny as the platform. The insights derived can help understand metrics such as traffic density along corridors, the directions of traffic flow, and the daily and seasonal patterns of traffic flow.
Motivation
Network patterns can reveal very interesting insights but it is very difficult to implement with off-the-shelf software tools such as Tableau®. Gephi®, an open-source and free software is one of the leading tools to visualise network graphs. But, in order to make our findings easily accessible to everyone without any installation of any tools at their end, we propose the usage of the recently introduced ggraph package from R. Besides bringing the same kind of flexibility offered by a commercial tool, it offers an extension on the well-acclaimed ggplot2 package in R. Built specifically for supporting relational data structures such as networks, graphs and trees, the API provides a self-contained set of facets and customisations, enhancing the quality of visualisations.
Practical use cases
- Traffic planning.
- Systems such as Singapore ERP.
- Implementing diversions during peak periods.
