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 digital approach to dealing with massive data, which is pertinent for a future smart city. It is the inherent challenge of data processing in classic vehicular systems. Therefore, I proposed Big Data Analytics (BDA) to optimize the location and safety-based data analytics and performance measures for complete and informative learning processes in various circumstances. The data preprocessing and feature extraction process includes vehicle mobility, multi-source data acquisition, distributed computation, and multi-path data communication in an analytical model to enhance performance and safety. The proposed approach is capable and scalable to manage large-scale sensor data in various contexts. The findings revealed the possible trajectory gained from the online transaction and information flow on digital and safe ITS applications. The experiment showed that leveraging big data technology enhances 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.