Relying solely on synthetic trajectory data for analyses and services poses several potential risks. One major risk is that synthetic data may not accurately represent real-world behaviors and patterns present in actual trajectory datasets. This discrepancy between synthetic and real data could lead to biased analyses, incorrect conclusions, or ineffective decision-making based on the generated information. Additionally, if there are flaws in the generative model used to create synthetic trajectories, these inaccuracies could propagate throughout any downstream applications relying on this data, potentially leading to significant errors or misinterpretations of results.
Furthermore, using only synthetic trajectory data may overlook important nuances and intricacies present in real datasets that could impact service quality or analysis outcomes. Without a comprehensive understanding of how well synthetic data mirrors reality and addresses all relevant factors, there is a risk of making decisions based on incomplete or misleading information.