z-logo
open-access-imgOpen Access
Escape the Traffic Congestion Using Brainstorming Optimization Algorithm and Density Peak Clustering
Author(s) -
Nagaraju Devarakonda,
Kavitha Dasari,
Raviteja Kamarajugadda
Publication year - 2021
Publication title -
ingénierie des systèmes d'information/ingénierie des systèmes d'information
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.161
H-Index - 8
eISSN - 2116-7125
pISSN - 1633-1311
DOI - 10.18280/isi.260305
Subject(s) - brainstorming , cluster analysis , computer science , traffic congestion , data mining , artificial intelligence , transport engineering , engineering
In recent days many people are working on twitter data as the tweets are easily available and also provide reliable data. Collecting and processing these tweets produces promising and accurate results in solving many real world problems. Common problem faced by most of the people is traffic congestion. Traffic congestion results in traffic jams, mental and physical health disturbance. So to avoid this, our paper tried to show the methodology which can bring out promising results. In this paper for processing the tweet data we have used the common approach of Term Frequency-Inverse Document Frequency (TF-IDF) and discussed the application of brainstorming optimization algorithm (BSO) to avoid traffic congestion. We have also introduced the density peak clustering (DPC) to train the brain storming optimization technique. This paper has shown the modified BSO and DPC on the tweets to bring out the results which show traffic conditions at various places. We have justified our work by conducting the experiment.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here