Many large-scale and highly specialised distributed systems such as Urban Traffic
Control (UTC) systems produce considerable quantities of data on a real-time basis.
This data is then frequently locked in a proprietary format that makes it difficult to
utilise for external purposes such as historical data analysis or additional end-user
applications. The purpose of this dissertation is to examine the techniques and issues
involved in extracting, processing and presenting such large quantities of data to a
variety of end-user applications in a meaningful and scalable fashion.
In order to gain further insights into this subject it was decided to develop an end-user
interface for a real-life example of a large-scale distributed system that produces high
volumes of data. The example chosen was SCATS, the city of Dublin’s UTC system.
This system measures the traffic flows passing through several hundred junctions
around the city and automatically adjusts the timing of the each intersection’s traffic
lights on a minute-by-minute basis.
Three central issues present themselves in this project. The first of these is the
question of how to extract data from a proprietary system and convert it into a format
that can be universally read and understood. The second issue is how an algorithm can
be created that takes the available data and generates a meaningful measure of road
traffic congestion at any given point in time. The third problem involves presenting
such large quantities of data to a wide variety of end-users in a manner that is clear,
meaningful and intuitive.
The three tier application resulting from the research carried out during the course of
the project is evaluated under several headings. Concurrency issues along with the
performance, scalability and usability of each part of the implementation are
discussed and evaluated. The congestion algorithm is compared against other
measures that are available in SCATS, and formulae from the existing literature.
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