Large Language Model in Suburban Transport Data Management
N. G. Kuftinova, А. В. Остроух, O. I. Maksimychev, A. A. Podberezkin, A. M. Volkov
Abstract
This article explores the new reality of using Large Language Models (LLM) to manage digital data of transport infrastructure based on the concept of artificial intelligence and deep learning of the transport network model as a digital twin of agglomeration. Innovations in the field of artificial intelligence based on data make it possible to make reliable predictions and make optimal decisions to solve scientific problems in this study, but it faces a number of critical problems, including high system complexity, large search space, incomplete knowledge and small amounts of data, and all this requires new strategies to effectively solve these problems. In combination with Artificial General Intelligence (AGI), vehicles can use this data to make more complex decisions, for example, choosing the optimal route depending on the current traffic situation and predicting its changes. LLM's artificial intelligence technology can also be used to create safer vehicles. For example, the system can automatically respond to changes in the traffic situation, preventing traffic accidents and minimizing risks to passengers and others.