Vehicle Speed Estimation from Roadside Noise Using Physics-Guided Doppler Ridge and Linear Calibration

Abstract Accurate measurement of vehicle speeds is central to traffic safety, enforcement, and intelligent transportation systems, yet many established methods depend on intrusive pavement sensors, costly radar or light detection and ranging (LiDAR), or vision systems sensitive to environmental conditions. This study evaluates a low-cost, nonintrusive alternative that estimates vehicle speed from roadside pass-by noise using a Class 1 sound-level meter with a free-field microphone. A total of 2,607 pass-by events were analyzed using a physics-guided Doppler ridge framework, refined through calibration and machine learning. On the test subset, calibrated acoustic features achieved mean absolute errors (MAE) of 8.29 km / h , root-mean squared error (RMSE) of 11.39 km / h , and R 2 as 0.47, and trucks exhibited the best performance with MAE of 5.44 km / h with 87.50% of cases within ± 10 km / h of actual speed. A portion of the error can be attributed to vehicle speed being measured when it was at some upstream location. Vehicles can accelerate or decelerate before reaching the closest distance from the microphone. These results demonstrate that acoustic Doppler-based features enable reliable, scalable, and nonintrusive speed estimation, offering a practical dual use for existing noise monitoring infrastructure. This approach provides primary low-cost speed screening in parallel with noise pollution assessment. More sophisticated state-of-the-art instruments may be triggered in real-time using this information for confirmatory measurement.