Big data analysis and cloud computing for smart transportation system integration

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 t...

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Bibliographic Details
Published in:Multimedia tools and applications Vol. 84; no. 29; pp. 35073 - 35090
Main Authors: Ali, Mohammed Hasan, Jaber, Mustafa Musa, Abd, Sura Khalil, Alkhayyat, Ahmed, Albaghdadi, Mustafa Fahem
Format: Journal Article
Language:English
Published: New York Springer US 01.09.2025
Springer Nature B.V
Subjects:
1231: IoT-driven Computer Vision Technology for Smart Transportation Applications
Accuracy
Algorithms
Big Data
Cloud computing
Communications systems
Computer Communication Networks
Computer Science
Data analysis
Data mining
Data processing
Data Structures and Information Theory
Efficiency
Intelligent transportation systems
Machine learning
Multimedia Information Systems
Roads & highways
Sensors
Smart cities
Special Purpose and Application-Based Systems
Traffic
Traffic control
Traffic flow
Transportation planning
Vehicles
Cloud computing
Big data analytics
Traffic prediction
Smart transportation
Machine learning
ISSN:1573-7721, 1380-7501, 1573-7721
Online Access:Get full text
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Summary: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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ISSN:1573-7721
1380-7501
1573-7721
DOI:10.1007/s11042-022-13700-7