Enhanced Path Travel Time Prediction via Guided Fusion of Heterogeneous Sensors Using Continuous-Time Dynamics

Accurate path travel time prediction is often hindered by sparse and heterogeneous traffic data. This paper proposes FusionODE-TT, a novel model designed to address these challenges by modeling traffic as a continuous-time process. The model features a Recurrent Neural Network encoder that processes multi-source time-series data to initialize a latent state vector, which then evolves over the prediction horizon using a Neural Ordinary Differential Equation (NODE). The core innovation is a guided fusion mechanism that leverages sparse but high-fidelity Automatic Vehicle Identification (AVI) data to apply strong, event-based corrections to the model's continuous latent state, mitigating error accumulation in the prediction process. Experiments were conducted on a real-world dataset comprising AVI, GPS, and point sensor data from a major urban expressway. The experimental results demonstrate that the proposed model achieves superior accuracy, outperforming a suite of baseline models in terms of prediction accuracy and robustness. Furthermore, a comprehensive ablation study was performed to validate the efficacy of our design. The study quantitatively confirms that both the continuous-time dynamics modeled by the NODE and the guided fusion mechanism are essential components, each providing a significant and independent contribution to the model's overall performance.