Nuhuo: An Effective Estimation Model for Traffic Speed Histogram Imputation on A Road Network

Traffic speed histograms show the distribution of traffic speeds over a certain period. Traffic speed might not be recorded continuously, leading to missing histograms for some links on a road network. However, accurate imputation of missing histograms is a critical yet challenging task. This paper introduces a novel framework to address four previously unexplored dimensions crucial for precise traffic speed histogram estimation: regionality, proximity, sparsity, and volatility. First, to address the challenge of regionality and proximity, we employ a global partition graph that captures both regional and proximal correlations within the road network. Next, in response to the challenge of sparsity, the framework features a disentangled feature encoding pipeline, comprising a global encoder and a localized spatio-temporal encoder. This design allows for the effective handling of entangled spatio-temporal dimensions, thereby mitigating the issues related to input sparsity. In particular, the framework leverages graph neural networks and recurrent neural networks to capture spatial and temporal correlations. In addition, to encompass the complexities of spatio-temporal correlations both on global and local scales, we employ a two-layer fusion module with an attention-based mechanism for representation integration. Lastly, to mitigate the challenge of volatility due to missing values, we incorporate a self-supervised learning task using an auto-encoder framework, enhancing the stability and robustness of the encoding models. Extensive evaluations on two real-world datasets confirm that our method significantly outperforms state-of-the-art solutions in terms of both accuracy and robustness.