A Pretrained Probabilistic Transformer for City-Scale Traffic Volume Prediction

• URL: http://arxiv.org/abs/2506.02654v1
• Date: Tue, 03 Jun 2025 09:07:29 GMT
• Title: A Pretrained Probabilistic Transformer for City-Scale Traffic Volume Prediction
• Authors: Shiyu Shen, Bin Pan, Guirong Xue,
• Abstract summary: City-scale traffic volume prediction plays a pivotal role in intelligent transportation systems.<n>Current models are typically trained in a city-specific manner, which hinders their generalizability.<n>We introduce TrafficPPT, a Pretrained Probabilistic Transformer designed to model traffic volume as a distributional aggregation of trajectories.
• Score: 8.384075654211685
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: City-scale traffic volume prediction plays a pivotal role in intelligent transportation systems, yet remains a challenge due to the inherent incompleteness and bias in observational data. Although deep learning-based methods have shown considerable promise, most existing approaches produce deterministic point estimates, thereby neglecting the uncertainty arising from unobserved traffic flows. Furthermore, current models are typically trained in a city-specific manner, which hinders their generalizability and limits scalability across diverse urban contexts. To overcome these limitations, we introduce TrafficPPT, a Pretrained Probabilistic Transformer designed to model traffic volume as a distributional aggregation of trajectories. Our framework fuses heterogeneous data sources-including real-time observations, historical trajectory data, and road network topology-enabling robust and uncertainty-aware traffic inference. TrafficPPT is initially pretrained on large-scale simulated data spanning multiple urban scenarios, and later fine-tuned on target cities to ensure effective domain adaptation. Experiments on real-world datasets show that TrafficPPT consistently surpasses state-of-the-art baselines, particularly under conditions of extreme data sparsity. Code will be open.

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