Hybrid RFID–Biometric Ticketing with QAOA-Based Fare and Load Optimization for Smart Transit

The current work proposes a hybrid quantum/classical technique for efficient and secure fare management, as well as for distributing the number of passengers on a transit system accordingly, through the use of a combination of Radio Frequency Identification (RFID) identification of each passenger and biometrically-based facial verification at the edge of the network via Mobile Net-Face (running on a Raspberry Pi). This enables frictionless and real-time low-latency and privacy-focused boarding verification. In addition, this work combines a cloud-based transit data logging layer with the Quantum Approximate Optimization Algorithm (QAOA) to provide real-time fare regulation and efficient load distribution among transit routes. This innovative process overcomes the limitations of traditional fixed-fare and static-routing fare regimes by introducing quantum-assisted optimization at all levels depending on fluctuations in transit density/usage and demand. The results of testing indicate that the QAOA enables the most expedient and scalable process for authenticating passengers and producing optimized results, establishing a replicable protocol for future quantum-based Smart Mobility initiatives. The Key Contributions include QAOA-based optimization and cloud-based transit log data with Dynamic Fare RegulationMobile Net-Face-based passenger identification via RFID at Edge of Transit System, Low-Latency Secure Boarding ConfirmationQuantum Hybrid Workflow (Scalability)