Improvements to a queue and delay estimation algorithm utilized in video imaging vehicle detection systems

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Title: Improvements to a queue and delay estimation algorithm utilized in video imaging vehicle detection systems
Author: Cheek, Marshall Tyler
Abstract: 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 .
URI: http : / /hdl .handle .net /1969 .1 /5820
Date: 2007-09-17

Citation

Improvements to a queue and delay estimation algorithm utilized in video imaging vehicle detection systems. Available electronically from http : / /hdl .handle .net /1969 .1 /5820 .

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