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Big data analysis and cloud computing for smart transportation system integration

  • 1231: IoT-driven Computer Vision Technology for Smart Transportation Applications
  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Big data and cloud computing are becoming more critical in transportation systems as these technologies develop. Transportation companies can recognize and forecast potential traffic problems and offer appropriate responses. To avoid hindering mobility, one might use predictive analytics to assess the effect of various development initiatives and suggest a viable alternative. Due to automobiles’ flexibility and rapid changes in their environment, creating an effective communication system for vehicular networks is tough. An intelligent transportation system with big data analytics and cloud computing (STS-BCC) is the goal of this research work. Data mining is used to anticipate traffic conditions using a machine learning method. The cloud platform provides a secure storage service and processing unit to aid traffic forecasting. The experimental analysis finds the prediction accuracy of 97.45% and proves the efficient integration of big data analytics and cloud computing technologies.

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Data availability

Available on request.

Code availability

Not applicable.

Abbreviations

STS-BCC:

Intelligent Transportation system with big data analytics and cloud computing

ITS:

Intelligent Transport system

ML:

Machine Learning

PSO:

Particle swarm optimization

ANN:

Artificial neural network

CNN-LSTM:

Convolutional neural network-long short-term memory

ICT:

Information and Communication Technology

CPS:

Cyber physical system

AID:

Automatic Incident Detection

RSU:

Road side unit

OBU:

Onboard unit

ECU:

Electronic control unit

ECM:

Engine control unit

BP:

Backpropagation

RF:

Radio frequency

AI:

Artificial Intelligence

BPNN:

Backpropagation neural networks

SaaS:

Software as a Service

PaaS:

Platform as a Service

IaaS:

Information as a Service

CaaS:

Communication as a Service

QPSO:

Quantum particle swarm optimization

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Author contribution

Mohammed Hasan Ali, Mustafa Musa Jaber, Sura Khalil Abd, is responsible for designing the framework, analyzing the performance, validating the results, and writing the article. Ahmed Alkhayyat, Mustafa Fahem Albaghdadi is responsible for collecting the information required for the framework, provision of software, critical review, and administering the process.

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Authors did not receive any funding.

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Authors and Affiliations

  1. Computer Techniques Engineering Department, Faculty of Information Technology, Imam Ja’afar Al-Sadiq University, Najaf, 10023, Iraq

    Mohammed Hasan Ali

  2. Department of Computer Science, Dijlah University College, Baghdad, 10021, Iraq

    Mustafa Musa Jaber

  3. Department of Computer Science, Al-turath University College, Baghdad, Iraq

    Mustafa Musa Jaber & Sura Khalil Abd

  4. College of technical engineering, The Islamic University, Najaf, Iraq

    Ahmed Alkhayyat

  5. Optics Techniques Department, Al- Mustaqbal University College, Hilla, 51001, Iraq

    Mustafa Fahem Albaghdadi

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  1. Mohammed Hasan Ali
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  2. Mustafa Musa Jaber
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Correspondence to Sura Khalil Abd.

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Ali, M.H., Jaber, M.M., Abd, S.K. et al. Big data analysis and cloud computing for smart transportation system integration. Multimed Tools Appl (2022). https://doi.org/10.1007/s11042-022-13700-7

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  • DOI: https://doi.org/10.1007/s11042-022-13700-7

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