06
Oct
The Use Case
A major technology company needed synthetic commute data that looked and behaved like real world travel. They required routes that reflected true movement patterns across a city, along with variations in weather, lighting, and timing. The goal was to train mobility and navigation models without relying on identifiable location data.
The Challenge
Creating realistic commute data revealed several hurdles. The client had only a limited number of real traces to work from. They needed synthetic routes that reflected weekday and weekend patterns, morning and evening peaks, and different parts of the city. Environmental conditions such as rain, fog, snow, sunrise, and nighttime lighting also needed to be represented in a consistent and repeatable way. The team needed fast iteration cycles as their modeling work expanded.
MLtwist’s Solution
MLtwist built a synthetic data pipeline that combined route behavior modeling, environmental conditioning, and automated quality controls. The workflow generated diverse commute routes from limited samples while maintaining privacy. Weather and lighting variations were applied through controlled transformations. Automated checks ensured logical speed patterns, timestamps, and route consistency. The pipeline also supported rapid updates whenever the client requested new route types or areas.
Impact and Benefits
The client received high fidelity synthetic commute data that captured real world complexity without exposing personal information. The improved workflow accelerated model development and reduced time spent fixing data issues. Consistent weather and lighting variations strengthened the robustness of their transportation models and simulations.
The Takeaway
MLtwist provided a fast, reliable, and privacy safe way to generate realistic synthetic mobility data. The project enabled the client to scale their research with confidence in both the quality and performance of the data.