Multi-Agent Multimodal Large Language Model Framework for Automated Interpretation of Fuel Efficiency Analytics in Public Transportation

TL;DR

This paper introduces a multi-agent framework utilizing multimodal large language models to automate and improve the interpretation and reporting of fuel efficiency data in public transportation, demonstrated through a real-world case study.

Contribution

It presents a novel multi-agent system that coordinates specialized LLMs for automated, coherent, and stakeholder-oriented energy analytics reporting in public transit.

Findings

Achieved 97.3% narrative accuracy with GPT-4.1 mini and Chain-of-Thought prompting.

Enhanced factual precision, coherence, and scalability in LLM-based energy reporting.

Validated framework effectiveness through real-world public bus transportation data.

Abstract

Enhancing fuel efficiency in public transportation requires the integration of complex multimodal data into interpretable, decision-relevant insights. However, traditional analytics and visualization methods often yield fragmented outputs that demand extensive human interpretation, limiting scalability and consistency. This study presents a multi-agent framework that leverages multimodal large language models (LLMs) to automate data narration and energy insight generation. The framework coordinates three specialized agents, including a data narration agent, an LLM-as-a-judge agent, and an optional human-in-the-loop evaluator, to iteratively transform analytical artifacts into coherent, stakeholder-oriented reports. The system is validated through a real-world case study on public bus transportation in Northern Jutland, Denmark, where fuel efficiency data from 4006 trips are analyzed using Gaussian Mixture Model clustering. Comparative experiments across five state-of-the-art LLMs and three prompting paradigms identify GPT-4.1 mini with Chain-of-Thought prompting as the optimal configuration, achieving 97.3% narrative accuracy while balancing interpretability and computational cost. The findings demonstrate that multi-agent orchestration significantly enhances factual precision, coherence, and scalability in LLM-based reporting. The proposed framework establishes a replicable and domain-adaptive methodology for AI-driven narrative generation and decision support in energy informatics.