Heterogeneous fixed sensor data fusion based on interpolation and deep learning for freeway traffic state estimation

Real-time and accurate traffic state estimation is crucial for intelligent freeway management. To fully utilize existing resources and achieve high-resolution estimation, this study proposes a multi-source heterogeneous fixed sensor data fusion framework. First, interpolation is adopted to unify reporting frequency to one minute, improving time alignment and data continuity. Then, four deep learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Stacked Auto-Encoders (SAE), and Transformer, are employed for traffic state estimation. To achieve optimal performance, twelve input step sizes and five interpolation methods are considered. The framework is comprehensively evaluated using real-world freeway data. Results show that each model has a distinct optimal configuration, and both interpolation and data fusion contribute to improving estimation performance. Multi-dimensional analysis further confirms the model’s robustness under varying traffic states, freeway section types, weather conditions, sensor failures, and cross-segment transfer test, enabling effective support for timely traffic management decisions.