Evaluating the performance of AI chatbots in estimating traffic restriction directions

Abstract

In digital twin technology, it is essential not only to accurately replicate physical spaces in a digital environment, but also to ensure the ability to verify data integrity, even in the presence of erroneous or inconsistent information. To achieve this, redundant mechanisms that allow for cross-verification and supplementation of data through diverse methods are desirable. Prior research has explored the feasibility of such verification by integrating publicly available datasets with geo-tagged photographic data. This study builds upon previous work that estimated large vehicle restriction zones using geo-tagged photos. Specifically, we investigate whether it is possible to estimate the traffic regulation directions indicated by road signs in images that were previously considered difficult to interpret. To this end, we evaluated the performance of multiple AI chatbots: Claude Opus 4, Gemini 2.5 Pro, and ChatGPT (o3/o4-mini-high). By inputting both the images used in prior studies and corresponding Google Street View captures from the same locations, we found that ChatGPT was able to successfully estimate all traffic regulation directions.