  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.
