This paper describes the development of a hybrid reidentification algorithm to estimate travel times entirely on the basis of data that are readily available from single loop detectors (60-Hz vehicle occupancy data) without the need for additional hardware in the field. The key concept is data fusion, which combines crude and potentially inaccurate spot-point estimates with a software-based signature-matching algorithm. The method was applied to real data from an urban freeway site with four locations and six segments, with Bluetooth-matched travel time as the ground truth. The method performed satisfactorily, with a relative error of 9% for a segment 1.54 mi long, among others. It removed bias and improved the baseline spot-speed method significantly. The approach requires minimum calibration with no additional hardware. The method, therefore, proves suitable for widespread deployment and provides a clear path for agencies to leverage their existing loop and controller infrastructure for accurate travel time estimation through reidentification.
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