Project Proposal

Discovering traffic patterns by using network graph visualisations

Introduction

About

Project Proposal

Project Timeline

App & Deliverables

Poster

Project Proposal

Steps in planning and preparation of the application

• Making sense of the data provided.
• Selecting a real world practical use-case (Traffic networks).
• Choosing R and deriving maximum value of the ggraph and ggnetwork packages.
• Making the dataset reactive to user inputs and choosing the appropriate inputs.
• Compiling the deliverables to make a complete story through an interactive application.
• Drafting poster for quick readability and preliminary understanding.

Using R to visualise traffic networks

An overview on previous works on network graphic visualisations in R

Network visualisation in R has been quite popular in order to derive trends on association, social networks, etc. It has increasingly gained traction with new trends emerging especially in the field of social networks. Facebook launched the Graph API to track social network behavior in the form of nodes, edges and fields. These keywords form the pedestal upon which most network visualisations are built.
Developers have constantly strived to produce visualisations using various R packages such as visNetwork, ggnet, network, sna, etc. The blogs published by such developers provide an initial overview and reference for us to develop network visualisations. With ggraph being a relatively new package, similar ideologies applied in other network packages in R can be compared to see how ggraph produces visual outputs. A good example is provided by Katya Ognyanova and Francois Biratte.

What are the R packages needed?

The key packages used in the application include:

shiny

Shiny provides a web application framework for R. It enables easy development of interactive web applications by wrapping an user interface with a server script. Rending the app allows various interactive features such as dropdowns, sliders which will greatly enhance the user experience whilst using R. An example of a shiny interface which shows dropdowns, data range selectors is as shown.

shinydashboard

As business increasingly adopt the buzzword called ‘dashboards’ where a one-stop view is readily accessible, shinydashboard enables to embed a shiny application in a similar form. With a real world traction, we believe shinydashboard helps to make a comprehensive dashboard with customisable panes to show aspects such as data, visualisations and text. An example of how our dashboard helps achieve this is as shown in the below figure. In this example, we have included a set of presentation slides, text inputs, data tables which are rendered based on selection, and the overall shiny interface itself.

User Guide to develop the visualisations

Transforming data in R to develop network graphs

In order to make the network graph render, a well defined nodes and edges table is necessary. Once the main data table is uploaded onto R, the data manipulation functions from R are used in order to achieve the objectives to segregate into a nodes table and edges table.

Deriving new variables for user interactivity:

In order to make the network graph render, a well defined nodes and edges table is necessary. Once the main data table is uploaded onto R, the data manipulation functions from R are used in order to achieve the objectives to segregate into a nodes table and edges table.

Developing the nodes table

In order to make the network graph render, a well defined nodes and edges table is necessary. Once the main data table is uploaded onto R, the data manipulation functions from R are used in order to achieve the objectives to segregate into a nodes table and edges table.

Developing the edges table

In order to make the network graph render, a well defined nodes and edges table is necessary. Once the main data table is uploaded onto R, the data manipulation functions from R are used in order to achieve the objectives to segregate into a nodes table and edges table.

Developing a network graph using ‘ggraph’:

In order to make the network graph render, a well defined nodes and edges table is necessary. Once the main data table is uploaded onto R, the data manipulation functions from R are used in order to achieve the objectives to segregate into a nodes table and edges table.

Developing a network graph using ‘ggnetwork’:

In order to make the network graph render, a well defined nodes and edges table is necessary. Once the main data table is uploaded onto R, the data manipulation functions from R are used in order to achieve the objectives to segregate into a nodes table and edges table.

Adding interactivity to the plots using ggplotly:

In order to make the network graph render, a well defined nodes and edges table is necessary. Once the main data table is uploaded onto R, the data manipulation functions from R are used in order to achieve the objectives to segregate into a nodes table and edges table.

Making the shiny dashboard:

Design Framework: A detail description of the design principles used and data visualisation elements built (Refer to Section 3: Interface of this paper [1].

The application developed is available at the App & Deliverables tab.

User takeaways

Network analysis can be a very useful tool to provide insights in the realm of data analytics. In our case, it is used to visualize time series data by building a network map revealing the patterns of traffic volumes at different points of the day at different corridors. Concepts such as betweenness and closeness centrality help us identify nodes that play an important role in facilitating paths from any two points and measures such as road expansion to smoothen the traffic or installation of gantries to divert them away may be necessary.

Going on further, network analysis can be applied to a myriad of problems and definitely not limited to just the visualization of traffic. For instance, suppose now the nodes were changed to people on facebook and the edges represent the relationship between them weighted by the number of likes and comments (the amount of activity). Such visualization can allow us to identify people who are influential on social media using relevant concepts such as eigenvector centrality and this information will be valuable to companies launching a new product and seeking to penetrate the social media effectively. In short, the usage of network analysis is highly relevant in solving real world problems and data analysts incorporating that tool into their arsenal can gain a significant competitive advantage.

Assumptions

The main underlying assumption lies with the mapping of the travel route for each vehicle. The data does not provide the GPS location of the cars at the different timestamps. Hence, a sorting of the timestamps is done based on records provided by the different entrances and it follows that a car travels directly to the next entrance after passing the current one where in fact it could have made a detour or take other routes without passing any entrances hence no data is being recorded.

Limitations

The ggraph package needs a well defined nodes and edges table in order to produce visualisations. While R Shiny enables development of quick and open source applications, extensive data transformation and reshaping is needed from the dataset in order to make full utilisation of the package for seamless performance of the application.

Future Scope

Future Work - A description of how your system could be extended or refined.

With the help of the timestamp and coordinate information of specified nodes, speeds of various vehicles can be derived, since the distance travelled and time spent between any two nodes are known. This will help to understand corridors in a vicinity where most speeding incidents occur, where there is higher congestion, etc. Also, at corridors with higher congestion typically in rush hours or after work hours, ERP pricings can be revised to divert the traffic to less congested areas.