Abstract:
Data is changing the face of our world. In every aspect of human life, data
is needed to innovate sustainable and efficient solutions for the future. Mobile
phone penetration has been increasing with an alarming rate, generating massive
amount of data. With a large amount of data originating from the telecom sector,
the data can be brought together, analyzed to discern patterns and extract useful
information. In crowded urban spaces, traffic congestion has significant impact on
peoples’ life wasting time of commuters, increasing air pollution, and inducing
mental stress among those who face these extreme situations.
The aim of this project is to formulate a predictive model to forecast the
traffic congestion. Using telecommunication data it will perform real-time
prediction of the traffic congestion in the city. Current traffic reporting systems are
using GPS-enabled smart phones which receives data of through the application
installed. These applications are only useful in a locality with a large number of
smart phone users. Hence this application is less effective for areas with lesser
smartphone penetration.
Mobile phone location data from telecom operators in the form of Call
Detail Record (CDR) has been widely studied, especially to extract insights into
urban dynamics. By using this type of data, we tend to reach a larger audience by
providing an intelligent traffic system. The system will provide real-time traffic
congestion predicition using data mining techniques. Data visualization tools
tableau is used to demonstrate the congestion level of the traffic for the specific
locations.
In summary, we believe that our idea has strength to save billions of
dollars, directly and indirectly, while making a real difference in people’s
lives.
