PathGen-LLM: A Large Language Model for Dynamic Path Generation in Complex Transportation Networks

Xun Li, Kai Xian, Huimin Wen (), Shengguang Bai, Han Xu and Yun Yu
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Xun Li: School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
Kai Xian: Beijing Transport Institute, Beijing 100161, China
Huimin Wen: Beijing Transport Institute, Beijing 100161, China
Shengguang Bai: Bowers College of Computing and Information Science, Cornell University, Ithaca, NY 14850, USA
Han Xu: Beijing Transport Institute, Beijing 100161, China
Yun Yu: Beijing Transport Institute, Beijing 100161, China

Mathematics, 2025, vol. 13, issue 19, 1-22

Abstract: Dynamic path generation in complex transportation networks is essential for intelligent transportation systems. Traditional methods, such as shortest path algorithms or heuristic-based models, often fail to capture real-world travel behaviors due to their reliance on simplified assumptions and limited ability to handle long-range dependencies or non-linear patterns. To address these limitations, we propose PathGen-LLM, a large language model (LLM) designed to learn spatial–temporal patterns from historical paths without requiring handcrafted features or graph-specific architectures. Exploiting the structural similarity between path sequences and natural language, PathGen-LLM converts spatiotemporal trajectories into text-formatted token sequences by encoding node IDs and timestamps. This enables the model to learn global dependencies and semantic relationships through self-supervised pretraining. The model integrates a hierarchical Transformer architecture with dynamic constraint decoding, which synchronizes spatial node transitions with temporal timestamps to ensure physically valid paths in large-scale road networks. Experimental results on real-world urban datasets demonstrate that PathGen-LLM outperforms baseline methods, particularly in long-distance path generation. By bridging sequence modeling and complex network analysis, PathGen-LLM offers a novel framework for intelligent transportation systems, highlighting the potential of LLMs to address challenges in large-scale, real-time network tasks.

Keywords: complex networks; deep learning; data-driven complex system modeling; path generation; spatiotemporal data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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