High Impact Factor : 4.396 icon | Submit Manuscript Online icon |

Traffic Reduction based on Social Media Data using 5W Model

Author(s):

A E Deepthy , Krishnasamy college of Engineering & Technology, Cuddalore.; V. Gopikrishnan, Krishnasamy college of Engineering & Technology, Cuddalore.

Keywords:

Crowdsourcing, Online Social Network, Short Text Classifier, Secondary Personalised Word Prediction

Abstract

Crowdsourcing is a technique that helps to create solutions that improve urban environment, human life quality, and city operation systems. Detection about urban emergency events, e.g., fires, storms, traffic jams is of great importance to protect the security of humans. A novel platform for providing geographical information is done by using social media feeds. The content from social media usually includes references to urban emergency events occurring at, or affecting specific locations. The detection of urban emergency events is done using the 5W Model. The spatial and temporal information from the social media are extracted to detect the real time event occurring at a particular location. A Global information system based annotation of the detected urban emergency event is shown. The proposed method is evaluated with prioritizing the events based on the crowd and instant notifications of the events are sent to the concerned people to reduce traffic and time. It also enhances the security of social network by introducing content filtering in user wall using short text classifier and secondary personalized word prediction in chat. The results show the accuracy and efficiency of the proposed method with great importance to protect the security of humans.

Other Details

Paper ID: IJSRDV5I31171
Published in: Volume : 5, Issue : 3
Publication Date: 01/06/2017
Page(s): 1555-1558

Article Preview

Download Article