The Secure GPS Tracking Data for transportation in Distributed Environments

Efficient and secure tracking of the public transport has become a critical issue in smart cities, especially in urban areas like Karachi which has high population. Current systems are usually characterized by slowness, unreliability in data transmission and susceptibility to security risks including services by unauthorized users and information alteration. In an attempt to solve these problems, this paper introduces a Secure Transporting Tracking Method one of the (SSTM) which is a new AI-based system of real-time GPS tracking of the public transport in the distributed environment. The SSTM incorporates a secure transmit of GPS data, AIs, location prediction, and efficient vehicle-passenger matching to make it more accurate, less time-consuming, and data integrity-insured. The model has been applied to and tested in several places all around Karachi, such as Gulberg, Nipa, Gulshan-e-Iqbal and Sachal. The outcomes of simulations prove that SSTM is faster than the current approaches, such as the traditional GPS and chatbot-enhanced ones, in processing, encryption/decryption, and tracking. The research brings in a safe, large scale, and smart transport tracking platform specific to smart cities with the possible use in real-time fleet management and secure passenger safety systems.