Urban Mobility Assessment Using LLMs

TL;DR

This paper presents an innovative AI-based method using large language models to synthesize urban mobility data, reducing reliance on traditional surveys and addressing privacy concerns, with promising results across multiple U.S. metropolitan areas.

Contribution

It introduces a novel approach leveraging LLMs to generate synthetic mobility data, demonstrating effectiveness in replicating real survey patterns at various data granularity levels.

Findings

Open-source Llama-2 models can generate realistic mobility data after fine-tuning.

Synthetic data closely matches actual survey data across different metrics.

The approach offers a privacy-preserving alternative for urban mobility analysis.

Abstract

Understanding urban mobility patterns and analyzing how people move around cities helps improve the overall quality of life and supports the development of more livable, efficient, and sustainable urban areas. A challenging aspect of this work is the collection of mobility data by means of user tracking or travel surveys, given the associated privacy concerns, noncompliance, and high cost. This work proposes an innovative AI-based approach for synthesizing travel surveys by prompting large language models (LLMs), aiming to leverage their vast amount of relevant background knowledge and text generation capabilities. Our study evaluates the effectiveness of this approach across various U.S. metropolitan areas by comparing the results against existing survey data at different granularity levels. These levels include (i) pattern level, which compares aggregated metrics like the average number of locations traveled and travel time, (ii) trip level, which focuses on comparing trips as whole units using transition probabilities, and (iii) activity chain level, which examines the sequence of locations visited by individuals. Our work covers several proprietary and open-source LLMs, revealing that open-source base models like Llama-2, when fine-tuned on even a limited amount of actual data, can generate synthetic data that closely mimics the actual travel survey data, and as such provides an argument for using such data in mobility studies.