This research delves into the construction and utilization of synthetic
datasets, specifically within the telematics sphere, leveraging OpenAI's
powerful language model, ChatGPT. Synthetic datasets present an effective
solution to challenges pertaining to data privacy, scarcity, and control over
variables - characteristics that make them particularly valuable for research
pursuits. The utility of these datasets, however, largely depends on their
quality, measured through the lenses of diversity, relevance, and coherence. To
illustrate this data creation process, a hands-on case study is conducted,
focusing on the generation of a synthetic telematics dataset. The experiment
involved an iterative guidance of ChatGPT, progressively refining prompts and
culminating in the creation of a comprehensive dataset for a hypothetical urban
planning scenario in Columbus, Ohio. Upon generation, the synthetic dataset was
subjected to an evaluation, focusing on the previously identified quality
parameters and employing descriptive statistics and visualization techniques
for a thorough analysis. Despite synthetic datasets not serving as perfect
replacements for actual world data, their potential in specific use-cases, when
executed with precision, is significant. This research underscores the
potential of AI models like ChatGPT in enhancing data availability for complex
sectors like telematics, thus paving the way for a myriad of new research
opportunities.

本研究以 ChatGPT 为基础，探讨了合成数据集的构建和利用，特别地，针对遥测监测领域。合成数据集具有对数据保密、不足以及控制变量等问题的解决方案，但其效用主要取决于其多样性、相关性和一致性。通过案例研究，本文对合成遥测数据集的生成过程进行了阐述，并对其进行了评估。尽管合成数据集不能完美地替代实际世界的数据，但在特定的应用场景中，其潜力是巨大的。