Harnessing LLMs for Cross-City OD Flow Prediction

TL;DR

This paper presents a novel approach using Large Language Models to predict cross-city Origin-Destination flows, overcoming traditional models' limitations by leveraging semantic understanding and transferability across diverse urban environments.

Contribution

The paper introduces a new LLM-based framework for cross-city OD flow prediction, including a novel loss function and semantic feature extraction, enabling better transferability across cities.

Findings

Outperforms state-of-the-art methods in cross-city OD prediction

Effectively captures spatial and functional relationships in urban spaces

Demonstrates robustness across diverse city datasets

Abstract

Understanding and predicting Origin-Destination (OD) flows is crucial for urban planning and transportation management. Traditional OD prediction models, while effective within single cities, often face limitations when applied across different cities due to varied traffic conditions, urban layouts, and socio-economic factors. In this paper, by employing Large Language Models (LLMs), we introduce a new method for cross-city OD flow prediction. Our approach leverages the advanced semantic understanding and contextual learning capabilities of LLMs to bridge the gap between cities with different characteristics, providing a robust and adaptable solution for accurate OD flow prediction that can be transferred from one city to another. Our novel framework involves four major components: collecting OD training datasets from a source city, instruction-tuning the LLMs, predicting destination POIs in a target city, and identifying the locations that best match the predicted destination POIs. We introduce a new loss function that integrates POI semantics and trip distance during training. By extracting high-quality semantic features from human mobility and POI data, the model understands spatial and functional relationships within urban spaces and captures interactions between individuals and various POIs. Extensive experimental results demonstrate the superiority of our approach over the state-of-the-art learning-based methods in cross-city OD flow prediction.