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A Distributed Big Data Analytics Architecture for Vehicle Sensor Data.

Theodoros Alexakis¹, Nikolaos Peppes¹, Konstantinos Demestichas²

¹School of Electrical and Computer Engineering, National Technical University of Athens, Zografou, 15773 Athens, Greece.

Sensors (Basel, Switzerland)

|January 8, 2023

Summary

This summary is machine-generated.

Intelligent transportation systems (ITS) generate vast data, requiring big data techniques. This study introduces a distributed architecture platform for efficient data acquisition, storage, and analysis in ITS applications.

Keywords:

big data distributed architecture machine learning sensors

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Area of Science:

Computer Science
Transportation Engineering
Data Science

Background:

Intelligent transportation systems (ITS) generate large volumes of fluctuating data from sensors on highways and vehicles.
Increasing data complexity necessitates big data techniques and distributed architectures for scalability in traffic and fleet management.

Purpose of the Study:

To address deficiencies in current ITS data handling by proposing a distributed architecture platform.
To provide a unified solution for continuous data acquisition, storage, and analysis in ITS.

Main Methods:

Leveraging big data frameworks like NoSQL MongoDB and Apache Hadoop.
Integrating analytics tools such as Apache Spark for data processing.
Designing a distributed architecture to support scalability and avoid technical limitations.

Main Results:

A holistic platform enabling continuous data collection, storage, and analysis for ITS.
Overcoming limitations of legacy systems in handling large, rapidly changing datasets.
Providing reliable acquisition, storage, and timely analysis capabilities.

Conclusions:

The proposed distributed architecture offers a comprehensive solution for ITS data management.
The platform supports the entire data lifecycle, enhancing efficiency and reliability.
Enables advanced reporting and analysis for improved transportation system operations.