This paper introduces an online methodology for real-time traffic density estimation using Physics-Informed Neural Networks (PINNs) with probe vehicle measurements. The proposed framework demonstrates robustness to model errors and noisy data, outperforming classical open-loop observers in scenarios with model mismatch and successfully reproducing traffic characteristics in high-fidelity simulations.