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Description:
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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 researcherâ  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 . |