Video Imaging Vehicle Detection Systems (VIVDS) are steadily becoming the dominant method for the detection of vehicles at a signalized traffic approach. This research is intended to investigate the improvement of a queue and delay estimation algorithm (QDA), specifically the queue detection of vehicles during the red phase of a signal cycle. A previous version of the QDA used a weighted average technique that weighted previous estimates of queue length along with current measurements of queue length to produce a current estimate of queue length. The implementation of this method required some effort to calibrate, and produced a bias that inherently estimated queue lengths lower than baseline (actual) queue lengths. It was the researcher’s goal to produce a method of queue estimation during the red phase that minimized this bias, that required less calibration, yet produced an accurate estimate of queue length. This estimate of queue length was essential as many other calculations used by the QDA were dependent upon queue growth and length trends during red. The results of this research show that a linear regression method using previous queue measurements to establish a queue growth rate, plus the application of a Kalman Filter for minimizing error and controlling queue growth produced the most accurate queue estimates from the new methods attempted. This method was shown to outperform the weighted average technique used by the previous QDA during the calibration tests. During the validation tests, the linear regression technique was again shown to outperform the weighted average technique. This conclusion was supported by a statistical analysis of data and utilization of predicted vs. actual queue plots that produced desirable results supporting the accuracy of the linear regression method. A predicted vs. actual queue plot indicated that the linear regression method and Kalman Filter was capable of describing 85 percent of the variance in observed queue length data. The researcher would recommend the implementation of the linear regression method with a Kalman Filter, because this method requires little calibration, while also producing an adaptive queue estimation method that has proven to be accurate.