A Trajectory Probability Network for City-Scale Road Volume Prediction
Keywords: Data Mining, Traffic Volume Prediction, Learning on Graph
TL;DR: A deep-learning model for city-scale road volume estimation with ultral incomplete data, fast and accurate.
Abstract: City-scale road volume prediction is a fundamental task in traffic management. However, the observation data are often incomplete and biased, posting a challenge for accurate prediction. Existing methods address this issue through interpolation techniques or manual priors, but they typically provide only a deterministic restoration, overlooking the influence of other potential scenarios. To overcome these limitations, we propose a novel neural network-based probabilistic model, the Trajectory Probability Network (TraPNet), which predicts traffic volume through the aggregation of the joint distribution of potential trajectories. TraPNet makes full use of current observations, historical data, and road network information to offer a comprehensive inference of road volumes. Unlike autoregressive methods, TraPNet makes predictions in a single step, substantially reducing computational time while maintaining high predictive accuracy. Experiments on real-world road networks demonstrate that TraPNet outperforms state-of-the-art methods, and can keep the advantage with only 20\% observation ratio. The code will be made publicly available.
Supplementary Material: zip
Primary Area: learning on graphs and other geometries & topologies
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Submission Number: 10916
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