Large Language Models (LLMs) for Roadway Safety and Mobility Enhancement

Abstract

Roadway safety and mobility are major concerns in rapidly urbanizing regions where traffic congestion, accidents, and inefficient transport systems are becoming serious problems. Recent developments in Artificial Intelligence have introduced Large Language Models (LLMs) as powerful tools capable of understanding, analyzing and generating human-like text from massive datasets. While LLMs are widely used in natural language processing applications, their potential role in transportation engineering, especially in roadway safety and mobility enhancement, is still emerging. This paper presents a comprehensive review of how LLMs can be applied in traffic accident analysis, traffic management, traveler information systems, infrastructure maintenance, and policy decision support. The paper also discusses integration of LLMs with IoT sensors, traffic cameras, GIS data, and intelligent transportation systems. Challenges such as data privacy, model bias, reliability and computational cost are also addressed. The study shows that LLMs can significantly support decision making, automate data interpretation, and improve safety strategies when combined with traditional traffic engineering tools.

KEYWORDS: Large Language Models, Roadway Safety, Mobility Enhancement, Traffic Management, Intelligent Transportation Systems, AI in Transportation, Accident Analysis, Smart Mobility