AI-Powered Generative Data Pipelines for Intelligent Transportation Systems

Abstract

Intelligent Transportation Systems (ITS) increasingly rely on vast, high-quality datasets to optimize traffic flow, enhance road safety, and support emerging mobility services. However, real-world data collection is often hindered by privacy concerns, sensor limitations, and high acquisition costs—especially in capturing rare or hazardous traffic conditions. To alleviate these challenges, we propose an AI-powered generative data pipeline that synthesizes realistic, diverse, and context-rich transportation datasets at scale. The pipeline integrates procedural scenario modeling, generative adversarial networks (GANs), and multi-modal data fusion (including video, LiDAR, and telemetry) to create synthetic traffic scenes under varying environmental conditions, incident events, and sensor modalities.
Our framework operates in three stages: scenario orchestration, generative augmentation, and validation & deployment. First, scenario templates define key parameters such as vehicle density, weather, road configuration, and event triggers (e.g., accidents, congestion, pedestrian infractions). Next, GAN-based models generate high-fidelity sensor outputs and dynamic traffic behaviors conditioned on scenario inputs. Finally, a validation module ensures physical plausibility and statistical realism, enabling the curated data to support ITS subsystems including traffic prediction models, signal control algorithms, and anomaly detectors.
Evaluation against baseline real-world datasets—drawn from municipal traffic cameras and loop detectors—demonstrates that synthetic data enhances model performance in traffic flow forecasting by up to 15% and incident detection by 10%. Additionally, the cost per scene generation is reduced by approximately 70% compared to deploying roadside data collection infrastructure. The generative pipeline’s flexibility allows rapid adaptation to new urban layouts, sensor configurations, or event types, offering substantial advantages in scalability, privacy preservation, and scenario coverage.
This AI-powered generative pipeline paves the way toward resilient, cost-effective, and adaptable data infrastructure for intelligent transportation systems, facilitating safer, smarter urban mobility.