Estimation Theory Approach to Monitoring and Updating Average Daily Traffic: Final Report

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Estimation Theory Approach to Monitoring and Updating Average Daily Traffic: Final Report

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1997-01

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Minnesota Department of Transportation

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Report

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This report describes the application of Bayesian statistical methods to several related problems arising in the estimation of mean daily traffic for roadway locations lacking permanent automatic traffic recorders. A lognormal regression model is fit to daily count data obtained from automatic traffic recorders, and this model is then used to develop (1) a heuristic algorithm for developing traffic sampling plans which minimize the likelihood of assigning a site to an incorrect factor group, (2) an empirical Bayes method for assigning a short-count site to a factor group using the information in a sample of traffic counts, and (3) an empirical Bayes estimator of mean daily traffic which allows for uncertainty concerning the appropriate factors to be used in adjusting a sample count. An evaluation of these methods confirmed results reported in other work, in which a sample consisting of two, 1-week counts was found to be adequate for overcoming prior uncertainty concerning the correct adjustments for a site. The empirical Bayes method produced sample-based estimates of mean daily traffic that on the average differed by 5%-6% from estimates based on daily counts for an entire year. The report concludes with suggestions for agencies wishing to implement these methods.

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Minnesota Department of Transportation

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Davis, Gary A.. (1997). Estimation Theory Approach to Monitoring and Updating Average Daily Traffic: Final Report. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/155100.

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