Closed-Loop Neural Operator-Based Observer of Traffic Density
This work addresses traffic management challenges for urban planners by providing an incremental improvement in data-driven observers for density estimation.
The paper tackled traffic density estimation using sparse sensor data by developing a closed-loop observer that combines a Fourier neural operator for open-loop prediction with a correction operator that integrates real-time measurements. Simulations in SUMO showed the closed-loop observer achieved robustness to noise and bounded error, outperforming open-loop methods.
We consider the problem of traffic density estimation with sparse measurements from stationary roadside sensors. Our approach uses Fourier neural operators to learn macroscopic traffic flow dynamics from high-fidelity data. During inference, the operator functions as an open-loop predictor of traffic evolution. To close the loop, we couple the open-loop operator with a correction operator that combines the predicted density with sparse measurements from the sensors. Simulations with the SUMO software indicate that, compared to open-loop observers, the proposed closed-loop observer exhibits classical closed-loop properties such as robustness to noise and ultimate boundedness of the error. This shows the advantages of combining learned physics with real-time corrections, and opens avenues for accurate, efficient, and interpretable data-driven observers.