A method to promote safe cycling powered by large language models and AI agents

• Type: Article in International Scientific Journal
• Date: 2024

Authors

Costa, DG (Author) FEUP; Silva, I (Author) Other; Medeiros, M (Author) Other; Bittencourt, JCN (Author) Other; Andrade, M (Author) Other

Journal

The Journal is awaiting validation by the Administrative Services.

Title: METHODSX

Vol. 13

ISSN: 2215-0161

Indexing

ISI Web of Knowledge - 0 Citations

Scopus - 0 Citations

Other information

Authenticus ID: P-012-PBT

DOI: 10.1016/j.mex.2024.102880

Abstract (EN): This paper presents a novel information generation methodology to support safer cycling patterns in urban environments, leveraging for that Large Language Models (LLMs), AI-based agents, and open geospatial data. By processing multiple files containing previously computed urban risk levels and existing mobility infrastructure, which are generated by exploiting open data sources, our method exploits multi-layer data preprocessing procedures and prompt engineering to create easy-to-use, user-friendly assistive systems that are able to provide useful information concerning cycling safety. Through a well-defined processing pipeline based on Data Ingestion and Preparation, Agents Orchestration, and Decision Execution methodological steps, our method shows how to integrate open-source tools and datasets, ensuring reproducibility and accessibility for urban planners and cyclists. Moreover, an AI agent is also provided, which fully implements our method and acts as a proof-of-concept implementation. This paper demonstrates the effectiveness of our method in enhancing cycling safety and urban mobility planning. center dot A novel method that combines LLMs and AI agents is defined to enhance the processing of multi-domain open geospatial data, potentially promoting cycling safety. center dot It integrates urban risk data and cycling infrastructure for a more comprehensive understanding of cycling resources, which become accessible by textual or audio prompts.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7