Leveraging Big Data Analytics for Intelligent Transportation Systems: Optimize the Internet of Vehicles Data Structure and Modeling

Abstract

Abstract An intelligent transportation system is an efficient and modern system to deal with big data, which is pertinent for a future smart city. It is the inherent challenge of data processing in classic vehicular systems. Therefore, we proposed Big data to optimize location-based operability and safety performances using federated sensor data. The data preprocessing and feature extraction process includes vehicle mobility, multi-source data acquisition, distributed computation, and multi-path data transmission in an analytical model to enhance performance and safety. The proposed approach is capable and scalable to manage large-scale sensor data from its source and line of information flow. The findings revealed the imperfect, complex, and challenging data transformation into actionable, safe, and usable information for the intelligent transformation system modernization and healthy digital ecosystems. Big data analytics for sensor data is complete and informative for the learning process of the physical systems in various situations and localizations. The experiment showed that leveraging big data technology is a systematic approach to enhancing safe and optimal vehicular system applications. It provides access to shared resources through connected devices and reduces costs using the response time and data structuring to the system stability and to accommodate future modifications in sensor technology.