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 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.