Computer Science & Electrical

Computer Science & Electrical

ARTIFICIAL INTELLIGENCE TRAFFIC CONTROL

Pages: 16  ,  Volume: 48  ,  Issue: 1 , March   2020
Received: 16 Mar 2020  ,  Published: 16 March 2020
Views: 172  ,  Download: 50

Authors

# Author Name
1 Aslam Anver

Abstract

I am pleasant to submit this innovative project proposal to International College of Business
and Technology. In order to complete my final year project, I have decided to come up with a
unique solution of a major problem in Sri Lanka. Therefore I named this project as Artificial
Intelligence Traffic Control.


Most of the roads in our country are crowded due to traffic congestion and Sri Lanka police
unit has been appointed many traffic polices to control the vehicle jam in particular times.
But they even don’t know the upcoming traffic situation on many roads.


Here the solution we bring is to make the traffic signal lights to artificial intelligence based
and also there is an opportunity to record the data of each roads and store them to the
database which we will be needed to make machine learning model to predict the traffic
status of future and let our AI act more accurate. I am only covering AI based traffic signal
application here based on the object detection system but also this idea can be extended into
many deep learning models to solve the problem 95% such as feeding the model to predict
future traffic situation, connecting all the traffic signals to let them take decision by
communications between the signals. I totally mean the object detection system to detect the
vehicle, take decision and implement through signals.


This project proposal is comprised of all the necessary information regarding the complete
predicted process of the implementation of the proposed system such as what are the
technologies we are going to use, feasibility of this project and the final outcomes etc.

Keywords

  • artificial intelligence
  • Machine Learning
  • traffic management
  • Deep Learning
  • References

    DL4J, 2018. A Beginner’s Guide to Recurrent Networks and LSTMs. [Online]
    Available at: https://deeplearning4j.org/lstm.html
    [Accessed 3 June 2018].