Real Time Traffic Detection using Semantic Analysis
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10.5120/ijca2021921640 |
Semil Jain and Riya Singh. Real Time Traffic Detection using Semantic Analysis. International Journal of Computer Applications 183(25):1-5, September 2021. BibTeX
@article{10.5120/ijca2021921640,
author = {Semil Jain and Riya Singh},
title = {Real Time Traffic Detection using Semantic Analysis},
journal = {International Journal of Computer Applications},
issue_date = {September 2021},
volume = {183},
number = {25},
month = {Sep},
year = {2021},
issn = {0975-8887},
pages = {1-5},
numpages = {5},
url = {http://www.ijcaonline.org/archives/volume183/number25/32080-2021921640},
doi = {10.5120/ijca2021921640},
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
Abstract
Social networks have recently come across as a great source of information for detecting congestion, accidents, as well as crowding due to numerous festivals. Twitter is one of the most popular sites because it expresses knowledgeable information in minimum words. Since the tweets have limited words, processing it becomes easy. Hence, This project uses Twitter as a source of information. This project focuses on classifying whether a tweet is a traffic related tweet or not using Semantic technologies. In this project, the system fetches the tweets using the Twitter Api and then pre-process it, converting into tokens and cleaning all noise. These tokens are then semantically processed and classification is performed using Na¨ıve Bayes multinomial classifier. Different types of tweets data are used for prediction, including tweets from selected road-traffic Twitter accounts, tweets that contain road-trafficrelated keywords and geo-tagged tweets. These classified results are then plotted as a colored path on the android application. Such information can help the traveler to make a better travel plan.
References
- Eleonora D’Andrea, Pietro Ducange, Beatrice Lazzerini, Member, IEEE, and Francesco Marcelloni, Member, IEEE, “Real- Time Detection of Traffic From Twitter Stream Analysis”, https://ieeexplore.ieee.org/document/7057672
- Fatma Amin Elsafoury Enschede, The Netherlands, “Monitoring Urban Traffic Status Using Twitter Messages ” , https://ieeexplore.ieee.org/elsafoury 28376
- Freddy L´ecu´e, Robert Tucker, Veli Bicer, Pierpaolo Tommasi, Simone TalleviDiotallevi, Marco Sbodio, “Predicting Severity of Road Traffic Congestion using SemanticWeb Technologies”, https://ieeexplore.ieee.org/document/paper 205
- Farman Ali, Daehan Kwak, Pervez Khan, S. M. Riazul Islam, Kye Hyun Kim, K.S. Kwak, “Fuzzy Ontology-based Sentiment Analysis of Transportation and City Feature Reviews for Safe Traveling”, https://ieeexplore.ieee.org/document/1701.05334
- Fellbaum, Christiane, ‘WordNet — A Lexical Database for English’, 2005. [Online]. Available: https://wordnet.princeton.edu/. [Accessed: 21- Oct- 2018].
Keywords
Semantic Analysis, geo-tagged tweets, Na¨ıve Bayes multinomial classifier, Tokenization, Stop-word filtering, Stemming, Stem filtering, Feature representation