Forecasting Traffic Progression in Terms of Semantically Interpretable States by Exploring Multiple Data Representations

In the rapidly evolving landscape of mobility modelling, the application of deep learning approaches introduces both opportunities and challenges. Such approaches, while powerful, yield opaque models that lack interpretability and adaptability to diverse traffic contexts. Addressing those challenges, a finite set of humanly-interpretable traffic states is exploited here for the purpose of facilitating the annotation of mobility data with meaningful labels such as congestion, free-flow, traffic build-up, etc. Such annotation unlocks a range of opportunities to integrate multiple complementary approaches for modelling state transition behaviour. Concretely, a novel hybrid modelling framework is introduced in this article leveraging multiple data representations (temporal, time-frequency and symbolic) with the aim to forecast traffic progression in terms of humanly-explicable state transitions. Three distinct modelling paradigms are subsequently explored: neural, neural-to-symbolic, and symbolic-to-neural, by demonstrating their potential to capture and forecast traffic dynamics on real-world mobility data. While the fully neural approach is undoubtedly the most accurate one, the two neuro-symbolic approaches offer a better trade-off between accuracy on one side and interpretability, probability calibration, and computational efficiency, on the other. This work illustrates the importance of tailored data representations in understanding and predicting complex mobility behaviour, highlighting the benefits of hybrid approaches in achieving interpretability and efficiency in traffic data analysis.