Real Time Traffic Prediction Based On Social Media Text Data Using Deep Learning

Authors

DOI:

https://doi.org/10.13052/jmm1550-4646.18211

Keywords:

Traffic prediction, Spark, Big data architecture ,Real time traffic analysis

Abstract

Due to urbanization Traffic management is one of the major issues in contemporary civic management, considering this circumstance traffic analysis is turning into the need of the present world. Text data generated by Twitter, Facebook and other social media platforms can be used for traffic management. Big data helps in traffic prediction and traffic analysis of advancing metropolitan zones. Constant traffic investigation requires preparing of information streams that are produced persistently to increase fast experiences. To measures stream information at a fast rate advancements on high figuring limit is required. Social media text data can be processed by using batch processing and stream processing with big data architecture through Spark and Hadoop framework. In this paper big data architecture is proposed for real time traffic text data analysis. In architecture Spark and Kafka are used in combination. Kafka helps in pipelines text data used in conjunction with spark stream processing engine. Big data architecture using Spark, Kafka with ability for processing and preparing huge measure of information, have settled the serious issue of handling and putting away constantly streaming data. The traffic information from Twitter API is streamed. In The proposed model pointed toward ensemble neural network model to reduce the variance in results for better prediction foreseeing traffic stream text data by incorporating Spark and Kafka that will be of an extraordinary incentive to the public authority for traffic management and analysis.

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Author Biographies

B. Mounica, School of Computer Science and Engineering, VIT University, Vellore, India

B. Mounica is currently working as an Assistant Professor in New Horizon College of Engineering, Bangalore and Research scholar from School of Computer Science and Engineering (SCOPE) in VIT University, Vellore. She completed M.Tech in Computer Science and Engineering from JNTU University, in the year 2012. She also received B.Tech degree in Information Technology from the Anna University, India, in 2005. Her current research interests include Data analytics, Data Mining and Warehousing, Machine Learning, Big data.

K. Lavanya, School of Computer Science and Engineering (SCOPE ),VIT University ,Vellore ,India

K. Lavanya is currently working as an Associate Professor in the School of Computer Science and Engineering(SCOPE) in VIT University, Vellore. She received her Ph.D. degree in Computer Science and Engineering from VIT University, Vellore, on August 2015 [July 2011–August 2015]. She completed ME in Computer Science and Engineering from VIT University, Vellore, in the year 2011. She also received BE degree in Computer Science and Engineering from the Anna University, India, in 2005 Her current research interests includes Computational Intelligence, Data Science, NoSQL databases, Data Mining and Warehousing, Machine Learning.

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https://www.sciencedirect.com/topics/computer-science/apache-spark

Published

2021-11-16

Issue

Section

Enabling AI Technologies Towards Multimedia Data Analytics for Smart Healthcare