SCANNER+: Neighborhood-based self-enrichment approach for traffic speed prediction

Traffic speed prediction is vital in fostering sustainable and efficient transportation systems, optimizing urban mobility, and improving road safety. An accurate prediction of traffic speed is challenging, especially for long-term predictions. One critical challenge in this context is the difficulty of capturing complex spatial and temporal interactions within road networks and making them accessible for deep learning models. Whereas recent prediction methods often rely on latent embedded representations of road networks, conventional latent spaces learn time-invariant representations of spatio-temporal snapshots and do not appropriately capture complex spatial and temporal dynamics. Furthermore, traffic data is often enriched by contextual information, such as features extracted from external sources, to enhance prediction accuracy. However, external contextual information sources for specific locations and times are typically limited. This article proposes a novel, neighborhood-based self-enrichment approach for traffic speed prediction to address these limitations. Our approach effectively identifies and explicitly models spatio-temporal correlations and dependencies in the traffic data. Then, our approach leverages these patterns to enrich the data, making such patterns explicitly accessible to the prediction models. We evaluate our method on real-world datasets and demonstrate that our method outperforms the baselines across all considered datasets on average by 4.10% in terms of root mean squared error for traffic speed prediction.