Difference between revisions of "About"

From Visual Analytics and Applications
Jump to navigation Jump to search
(about page edit 1)
Line 32: Line 32:
 
|}
 
|}
 
<!--MAIN HEADER -->
 
<!--MAIN HEADER -->
== About this project ==
+
== Abstract ==
The project here aims to solve the questions posed in the Visual Analytics Science and Technology challenge [[http://www.vacommunity.org/VAST+Challenge+2017]]. The team chose the project, as it gives a chance to apply varied concepts of visualizing through geography, image sensing and sensor detection. An amalgamation of the three different mini challenges [[http://www.vacommunity.org/VAST+Challenge+2017#Overview]] allows the group to build an unified application to explore the unknown, to unlock the potential reasons on why the Red Pipit bird is disappearing. Whilst this might not be a real world data set, the application of these kind of data is very much pragmatic.
+
 
 +
<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==
  
In regions with minimal human access such as the wildlife preserve shown in the challenge, drones are fast gaining traction. This kind of application is aimed to provide an insight on how to carry out path analysis, how to trace anomalies based on wind data and how to use satellite imagery, to get a clearer picture and draw out an inference.
+
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

Introduction

About

Project Proposal

Project Timeline

App & Deliverables

Poster

 

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.