Analysis method comparison of on-time and on-budget data.
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Analysis method comparison of on-time and on-budget data.

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      New Mexico Department of Transportation (NMDOT) results for On-Time and On-Budget performance measures as reported in (AASHTO/SCoQ) NCHRP 20-24(37) Project – Measuring Performance Among State DOTs (Phase I) are lower than construction personnel know to exist. The presumption by the Department was that the AASHTO/SCoQ Project Phase I applied an analysis model not representative of NMDOT business practices. The Research Bureau was tasked to answer the question: “Are the NMDOT On-Time and On-Budget analysis results as reported in NCHRP 20-24(37) Project Phase I, accurate?”. Review of the AASHTO/SCoQ Project’s Phase I Analysis Model and the Project’s analysis method resulted in the discovery of problematic methods of analysis including the application of “implied” estimates. The Research Bureau analyzed Phase I data using an analysis method more representative of NMDOT business practices and absent of statistical error created by these estimates. NMDOT On-Time performance was revealed to be 170% higher than reported for the strict measure and 220% higher than reported for the lenient measure. NMDOT Fiscal Year 2006 data were analyzed using NMDOT analysis methods and showed results proportionate to NMDOT analyzed Phase I results. Further, AASHTO/SCoQ results for Phase II are closely proportionate to NMDOT calculated results for Phase I and FY06 data. It was concluded that NCHRP 20-24(37) Project Phase I On-Time calculations are not accurate and that the Phase I On-Time analysis model is not representative of NMDOT business practices. Quality problems with Phase I, Phase II and FY06 data called into question the fitness of the data for use in performance benchmarking; it is probable that low quality data contributed to low On-Time results. Two state DOT’s were interviewed for their data quality assurance practices and the information incorporated into data quality improvement recommendations. Recommendations are made for continued research into performance measure modeling, measure comparability, and data quality improvement. Recommendations are made for administrative improvements.
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