A Big Data Architecture for Near Real-time Traffic Analytics

2017 ◽  
Author(s):  
Yikai Gong ◽  
Paul Rimba ◽  
Richard Sinnott
Keyword(s):  
Big Data ◽  
Real Time ◽  
Real Time Traffic ◽  
Data Architecture

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

2021 ◽  
Author(s):  
B. Mounica ◽  
K. Lavanya
Keyword(s):  
Social Media ◽  
Big Data ◽  
Real Time ◽  
Traffic Management ◽  
Traffic Analysis ◽  
Traffic Prediction ◽  
Text Data ◽  
Real Time Traffic ◽  
Data Architecture ◽  
Social Media Text

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.

scholarly journals Big Data-Driven Based Real-Time Traffic Flow State Identification and Prediction

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Hua-pu Lu ◽  
Zhi-yuan Sun ◽  
Wen-cong Qu
Keyword(s):  
Big Data ◽  
Real Time ◽  
Traffic Flow ◽  
Optimization Model ◽  
Bilevel Optimization ◽  
Data Driven ◽  
Stage Model ◽  
Flow State ◽  
Real Time Traffic ◽  
State Identification

With the rapid development of urban informatization, the era of big data is coming. To satisfy the demand of traffic congestion early warning, this paper studies the method of real-time traffic flow state identification and prediction based on big data-driven theory. Traffic big data holds several characteristics, such as temporal correlation, spatial correlation, historical correlation, and multistate. Traffic flow state quantification, the basis of traffic flow state identification, is achieved by a SAGA-FCM (simulated annealing genetic algorithm based fuzzyc-means) based traffic clustering model. Considering simple calculation and predictive accuracy, a bilevel optimization model for regional traffic flow correlation analysis is established to predict traffic flow parameters based on temporal-spatial-historical correlation. A two-stage model for correction coefficients optimization is put forward to simplify the bilevel optimization model. The first stage model is built to calculate the number of temporal-spatial-historical correlation variables. The second stage model is present to calculate basic model formulation of regional traffic flow correlation. A case study based on a real-world road network in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods.

scholarly journals Analytic architecture to overcome real time traffic control as an intelligent transportation system using big data

2018 ◽  
Vol 7 (2.18) ◽  
pp. 7 ◽  
Author(s):  
Venkata Ramana N ◽  
Seravana Kumar P. V. M ◽  
Puvvada Nagesh
Keyword(s):  
Big Data ◽  
Real Time ◽  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Control Logic ◽  
Systematic Analysis ◽  
Real Time Traffic

Big data is a term that describes the large volume of data – both structured and unstructuredthat includes a business on a day-to-day basis including Intelligent Transportation Systems (ITS). The emerging connected technologies created around ubiquitous digital devices have opened unique opportunities to enhance the performance of the ITS. However, magnitude and heterogeneity of the Big Data are beyond the capabilities of the existing approaches in ITS. Therefore, there is a crucial need to develop new tools and systems to keep pace with the Big Data proliferation. In this paper, we propose a comprehensive and flexible architecture based on distributed computing platform for real-time traffic control. The architecture is based on systematic analysis of the requirements of the existing traffic control systems. In it, the Big Data analytics engine informs the control logic. We have partly realized the architecture in a prototype platform that employs Kafka, a state-of-the-art Big Data tool for building data pipelines and stream processing. We demonstrate our approach on a case study of controlling the opening and closing of a freeway hard shoulder lane in microscopic traffic simulation. 

scholarly journals Towards of a Real-time Big Data Architecture to Intensive Care

2017 ◽  
Vol 113 ◽  
pp. 585-590 ◽  
Author(s):  
André Gonçalves ◽  
Filipe Portela ◽  
Manuel Filipe Santos ◽  
Fernando Rua
Keyword(s):  
Big Data ◽  
Intensive Care ◽  
Real Time ◽  
Data Architecture
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