# Large Language Models and Their Applications in Roadway Safety and Mobility Enhancement: A Comprehensive Review

arXiv: https://arxiv.org/abs/2506.06301 · HTML: https://mlsanity.com/paper/2506.06301/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 118 of its 220 references that are in the map.
- First posted on arXiv 19 May 2025.

## Builds on (most cited first, 30 of 118)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://mlsanity.com/paper/2306.02858/) (2023)
- [Gemma: Open Models Based on Gemini Research and Technology](https://mlsanity.com/paper/2403.08295/) (2024)
- [Time-LLM: Time Series Forecasting by Reprogramming Large Language Models](https://mlsanity.com/paper/2310.01728/) (2023)
- [CogVLM: Visual Expert for Pretrained Language Models](https://mlsanity.com/paper/2311.03079/) (2023)
- [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://mlsanity.com/paper/2305.03111/) (2023)
- [Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey](https://mlsanity.com/paper/2403.14608/) (2024)
- [Textbooks Are All You Need II: phi-1.5 technical report](https://mlsanity.com/paper/2309.05463/) (2023)
- [YOLOv11: An Overview of the Key Architectural Enhancements](https://mlsanity.com/paper/2410.17725/) (2024)
- [GPT-Driver: Learning to Drive with GPT](https://mlsanity.com/paper/2310.01415/) (2023)
- [InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Composition](https://mlsanity.com/paper/2309.15112/) (2023)
- [UL2: Unifying Language Learning Paradigms](https://mlsanity.com/paper/2205.05131/) (2022)
- [A Survey on Multimodal Large Language Models for Autonomous Driving](https://mlsanity.com/paper/2311.12320/) (2023)

## Built on by (most cited first, 9 of 9)

- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
- [SafePLUG: Empowering Multimodal LLMs with Pixel-Level Insight and Temporal Grounding for Traffic Accident Understanding](https://mlsanity.com/paper/2508.06763/) (2025)
- [Large Foundation Models for Trajectory Prediction in Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2509.10570/) (2025)
- [Improving Crash Data Quality with Large Language Models: Evidence from Secondary Crash Narratives in Kentucky](https://mlsanity.com/paper/2508.04399/) (2025)
- [Benchmarking Frontier Large Language Models Against Official Crash Database Coding Using Police Crash Narratives](https://mlsanity.com/paper/2607.29064/) (2026)
- [Jailbreaking Large Vision Language Models in Intelligent Transportation Systems](https://mlsanity.com/paper/2511.13892/) (2025)
- [A Vision-and-Knowledge Enhanced Large Language Model for Generalizable Pedestrian Crossing Behavior Inference](https://mlsanity.com/paper/2601.00694/) (2026)
- [Mind the Gap: Evaluating LLM Understanding of Human-Taught Road Safety Principles](https://mlsanity.com/paper/2511.13909/) (2025)
- [Truthful Online Preference Aggregation for LLM Fine-Tuning in Mobile Crowdsourcing](https://mlsanity.com/paper/2605.24052/) (2026)

## Abstract (arXiv)

Roadway safety and mobility remain critical challenges for modern transportation systems, demanding innovative analytical frameworks capable of addressing complex, dynamic, and heterogeneous environments. While traditional engineering methods have made progress, the complexity and dynamism of real-world traffic necessitate more advanced analytical frameworks. Large Language Models (LLMs), with their unprecedented capabilities in natural language understanding, knowledge integration, and reasoning, represent a promising paradigm shift. This paper comprehensively reviews the application and customization of LLMs for enhancing roadway safety and mobility. A key focus is how LLMs are adapted -- via architectural, training, prompting, and multimodal strategies -- to bridge the "modality gap" with transportation's unique spatio-temporal and physical data. The review systematically analyzes diverse LLM applications in mobility (e.g., traffic flow prediction, signal control) and safety (e.g., crash analysis, driver behavior assessment,). Enabling technologies such as V2X integration, domain-specific foundation models, explainability frameworks, and edge computing are also examined. Despite significant potential, challenges persist regarding inherent LLM limitations (hallucinations, reasoning deficits), data governance (privacy, bias), deployment complexities (sim-to-real, latency), and rigorous safety assurance. Promising future research directions are highlighted, including advanced multimodal fusion, enhanced spatio-temporal reasoning, human-AI collaboration, continuous learning, and the development of efficient, verifiable systems. This review provides a structured roadmap of current capabilities, limitations, and opportunities, underscoring LLMs' transformative potential while emphasizing the need for responsible innovation to realize safer, more intelligent transportation systems.