Year: 2025
Type: article
Abstract: We introduce the first natural language interface for complex urban analytics, leveraging Large Language Models (LLMs) and Spatio-Temporal Transactional Networks (STTNs). By combining intuitive natural language querying with structured data analytics, our framework simplifies complex urban analyses, such as identifying commuter patterns, detecting anomalies, and exploring mobility networks. We propose a comprehensive evaluation dataset that demonstrates that minor architectural improvements can significantly improve analytical accuracy. Our approach bridges the gap between non-expert users and sophisticated urban insights, paving the way for accessible, reliable, and scalable urban data analytics.
Source: Computational Urban Science
Authors: Yuri Bogomolov, Daniel Bretsko, Swam Pyae Paing, Stanislav Sobolevsky
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: diamond
Funders: New York University Abu Dhabi, Yuhan
Award: G8833531855
OpenAlex ID: W4417537754