Embracing Large Language Models in Traffic Flow Forecasting (2412.12201v1)

Abstract: Traffic flow forecasting aims to predict future traffic flows based on the historical traffic conditions and the road network. It is an important problem in intelligent transportation systems, with a plethora of methods been proposed. Existing efforts mainly focus on capturing and utilizing spatio-temporal dependencies to predict future traffic flows. Though promising, they fall short in adapting to test-time environmental changes of traffic conditions. To tackle this challenge, we propose to introduce LLMs to help traffic flow forecasting and design a novel method named LLM Enhanced Traffic Flow Predictor (LEAF). LEAF adopts two branches, capturing different spatio-temporal relations using graph and hypergraph structures respectively. The two branches are first pre-trained individually, and during test-time, they yield different predictions. Based on these predictions, a LLM is used to select the most likely result. Then, a ranking loss is applied as the learning objective to enhance the prediction ability of the two branches. Extensive experiments on several datasets demonstrate the effectiveness of the proposed LEAF.

Embracing LLMs in Traffic Flow Forecasting

The paper "Embracing LLMs in Traffic Flow Forecasting" introduces an innovative approach to improving traffic flow forecasting through the integration of LLMs. It proposes a novel framework termed LLM Enhanced Traffic Flow Predictor (LEAF), which aims to address prevailing challenges faced by traditional forecasting methods in adapting to test-time environmental changes and adequately capturing complex spatio-temporal dependencies within traffic data.

Overview and Methodology

Traffic flow forecasting is a critical aspect of intelligent transportation systems, involving the prediction of future traffic patterns based on historical data and road networks. Historically, efforts in this domain have predominantly focused on leveraging spatio-temporal dependencies via techniques such as graph neural networks, recurrent neural networks, and transformers. However, these approaches often assume static test-time conditions, thereby limiting their adaptability to dynamic real-world scenarios.

To mitigate these limitations, the LEAF framework merges the strengths of traditional methods with the evolving capabilities of LLMs. The approach consists of a two-pronged strategy:

1. Dual-branch Predictor: The predictor is composed of two branches designed to capture distinct aspects of spatio-temporal relations:
   • A graph neural network branch that models pairwise relations typically found in spatial graphs, suitable for capturing localized traffic interactions.
   • A hypergraph neural network branch that captures non-pairwise relations, accounting for more intricate multi-node interactions in the traffic network.
2. LLM-based Selector: This module uses a LLM to select from various predictive outcomes produced by the dual-branch predictor during test time. The selector evaluates different predicted scenarios and their respective transformations (e.g., smoothing trends, overestimation) to determine the most plausible traffic forecast. A ranking loss informs the adaptation process based on these selections, facilitating dynamic learning and improved accuracy over time.

Strong Results and Implications

Through rigorous experimentation on benchmark datasets such as PEMS03, PEMS04, and PEMS08, the proposed LEAF framework consistently outperformed existing methods. Notably, the framework achieved lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values, highlighting its effectiveness in predicting traffic flow more accurately than both conventional graph and hypergraph methods as well as initiatives that use LLMs for direct generative forecasting.

This work presents several implications for the future developments in AI-driven traffic prediction:

• Adaptability: By integrating LLMs in the prediction process, models can better adapt to shifting traffic patterns influenced by variables such as weather, special events, and urban development.
• Flexibility in Forecasting Models: The dual-branch approach affords a comprehensive view of traffic data, accommodating both localized and extended relational dynamics within traffic networks.
• Potential of LLMs in Non-Textual Domains: This paper evidences the potential of LLMs beyond textual data handling, indicating their utility in complex numerical and relational forecasting tasks.

Future Directions

The integration of LLMs in traffic flow forecasting opens new avenues for exploration. Future research may focus on optimizing LLM-based selectors further, investigating the generalizability of such models across different geographical and infrastructural contexts, and extending the framework to incorporate additional data types such as real-time sensor inputs and vehicle trajectories.

In summary, this paper illustrates a potent application of LLMs in refining traffic flow forecasting, offering a sophisticated tool to tackle dynamic urban transportation challenges. The LEAF framework lays foundational work that could inspire further research and practical applications within the AI and smart city domains.