Abstract
Traffic information is critical for pavement design, management, and health monitoring. Numerous in-pavement sensors have been developed and installed to collect the traffic volume and loading amplitude. However, limited attention has been paid to the algorithm of vehicle speed estimation. This research focuses on the estimation of the vehicle speed based on a cross-correlation method. A novel wireless micro-electromechanical sensor (MEMS), Smartrock is used to capture the triaxial acceleration, rotation, and stress data. The cross-correlation algorithms, i.e., normalized cross-correlation (NCC) algorithm, the smoothed coherence transform (SCOT) algorithm, and the phase transform (PHAT) algorithm, are applied to estimate the loading speed of an accelerated pavement test (APT) and the traffic speed in the field. The signal-noise-ratio (SNR) and the mean relative error (MRE) are utilized to evaluate the stability and accuracy of the algorithms. The results show that both the correlated noise and independent noise have significant influence in the field data. The SCOT algorithm is recommended for speed estimation with reasonable accuracy and stability because of a large SNR value and the lowest MRE value among the algorithms. The loading speed investigated in this study was within 50 km/h and further verification is needed for higher speed estimation.
Highlights
Traffic information, including but not limited to traffic volume, vehicle speed, and axle load, is one of the most important input factors for pavement design, management, and health monitoring
In the Superpave mix design, the performance grade (PG) of asphalt is selected based on the traffic loading rate and equivalent single axle loads (ESALs) that is calculated by the traffic volume and axle weight [1]
The results indicated that the cross-correlation based algorithms are more accurate than the other two methods that calculate the sum of absolute differences and the difference in gravity centers
Summary
Traffic information, including but not limited to traffic volume, vehicle speed, and axle load, is one of the most important input factors for pavement design, management, and health monitoring. It has a crucial influence on the performance of asphalt pavement. Many studies have concentrated on obtaining the vehicle classification and the axel load and improving the monitoring accuracy. WIM can provide various traffic information, such as classification of vehicles, total gross weight and axle weight, date, and time [3]. According to the field test results, the multiple classifier system approach improved the classification accuracy
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