Study on Structural Expansion of an Integrated Mobility Optimization System — Information Recognition & Prediction × Parallel/Stage Optimization × Immersive Presentation/Direct Control × Precursory Triggers × Failure-Learning Resilience —

This paper examines the structural expansion of an integrated mobility optimization system that combines real-time information recognition, predictive modeling, parallel and staged optimization, immersive VR/AR presentation, direct control interfaces such as BMI, precursor trigger detection, and failure-learning resilience. The framework integrates AI, distributed optimization, and quantum annealing to generate adaptive mobility plans under dynamic conditions involving traffic, weather, regulations, and demand. A multi-objective mathematical model is presented to evaluate time, cost, risk, environmental impact, and safety. Example calculations demonstrate potential improvements in reoptimization speed, delay reduction, risk mitigation, and overall system efficiency.