Scalable Generative AI Pipelines for Smart City Traffic Simulation with ERP Synergy and Bright Diagnostics
DOI:
https://doi.org/10.15662/IJARCST.2024.0704003Keywords:
generative AI, traffic simulation, smart cities, ERP integration, bright diagnostics, synthetic data, reinforcement learning, predictive analytics, anomaly detection, scalable pipelines, urban mobilityAbstract
Smart cities rely on advanced simulation systems to design, evaluate, and optimize traffic flow, safety, and sustainability. Traditional traffic simulation models often face limitations in adaptability, scalability, and integration with enterprise systems. This paper introduces a framework for scalable generative AI pipelines that leverage synthetic data generation, multimodal modeling, and reinforcement learning to simulate complex traffic dynamics in real time. The proposed system integrates with Enterprise Resource Planning (ERP) platforms to achieve cross-domain synergy, linking transportation data with logistics, supply chain, and urban infrastructure management. Additionally, bright diagnostics powered by predictive analytics and anomaly detection enable continuous monitoring of traffic health, early identification of bottlenecks, and adaptive decision support for urban planners. The cloud-native design ensures elasticity and fault tolerance, while generative AI enhances realism by capturing emergent traffic patterns and human-like driving behaviors. The framework demonstrates how generative AI, ERP synergy, and intelligent diagnostics can collectively transform urban mobility, enabling policymakers and stakeholders to create safer, more efficient, and sustainable smart city ecosystems.
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