A Scalable Hyperparameter-Guided Synchronization Framework for Population-Informed Trajectory Generation

Abstract

The rapid adoption of location-aware devices, such as smartphones, smartwatches, in-vehicle navigation systems, and asset-tracking tags, has resulted in massive volumes of spatio-temporal trajectory data. This data drives a wide range of commercial, transportation, and government applications. However, benchmarking these applications at scale remains difficult: existing network-based trajectory generators are batch-oriented, lack scalability, and cannot produce long-running, continuous trajectory streams that reflect realistic traffic dynamics. To overcome these limitations, we present TraSGen, a distributed, scalable, and continuous trajectory stream generator designed specifically for large-scale benchmarking of trajectory-driven systems. TraSGen produces realistic network-constrained trajectories using different traffic flow models that incorporate real-time road conditions, nearby moving and stationary objects, and other contextual factors. It accepts a GeoJSON-based road network as input, where nodes and edges may contain rich attributes. These attributes, combined with dynamic traffic information, guide the prediction of each subsequent trajectory point. TraSGen is adaptive; it can trade off the volume and synchronization level of traffic-data updates for higher generation speed, enabling users to benchmark systems under diverse load and realism requirements. Built on a shared-nothing architecture, it scales horizontally to support large and continuous trajectory workloads. Extensive experimental evaluation demonstrates TraSGen’s scalability, efficiency, and significant advantages over state-of-the-art trajectory generators.