Escape the Traffic Congestion Using Brainstorming Optimization Algorithm and Density Peak Clustering

Abstract

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.

Detection of Hot Topic in Tweets Using Modified Density Peak Clustering

“…Density peak clustering algorithm [30,31] is used to group the words (represented in vector format using word embedding) taken from all the tweets. The word with highest term frequency is taken as core points for the clustering.…”