Data is the cornerstone of modern technology, but what happens when actual data is unavailable due to privacy or other practical concerns? This is where simulated and synthetic data shine and that is the case with TRACY’s simulator (TSIM) component. 

TSIM simulates human and vehicle activity, as they live and move around a city, while they make use of communication, road, parking, or other services. Generated events provide a realistic event stream for such activities. 

Simulated data models real-world processes mathematically, while synthetic data replicates the statistical characteristics of real data without using sensitive information. Both types provide practical alternatives, enabling innovation even in data-scarce environments. 

Synthetic data is crucial in fields like security, healthcare and finance, were privacy regulations limit data availability. It ensures compliance while offering reliable datasets for AI model training. Businesses can prototype, train, and refine systems without the lengthy process of data collection. Additionally, model robustness gets enhanced by introducing diverse scenarios into datasets.  

However, quality matters and should be carefully addressed because poorly generated data may lead to biased results. This emphasizes the importance of validation against real-world benchmarks as well as simulation quality metrics. 

Figure-Simulated Athens Experiment Overview