ADOT Department Estimate Price Comparison Analysis
Highway construction projects often face budget surprises because estimated costs for common work like removing old curbs, sidewalks, or pavement can vary wildly between jobs. This project analyzed Arizona Department of Transportation (ADOT) cost estimates from 23 highway projects in 2024-2025 to understand why prices fluctuate so much and how to make budgeting more predictable. The study is based on real ADOT bid documents and uses simple statistical analysis—this is an academic data analysis project, not an official ADOT report.
The approach was straightforward: extract Department Estimate prices for seven common items (like concrete curb removal, sidewalk removal, asphalt pavement removal, and roadway excavation) from 74 bid records across Interstate, US Highway, and State Route projects. Basic stats like averages, ranges, and variation percentages were calculated, plus tests to check if highway type or bid year made a difference in pricing. Data cleaning confirmed all numbers were realistic construction costs with no missing information.
Key findings showed extreme price swings—for example, removing concrete curbs ranged from $38 to $19,226 per linear foot (average $2,484, variation 178%), while asphalt pavement removal went from $7 to $163,550 per square yard (variation 448%). Statistical tests found no clear link between highway type (Interstate vs State Route) or year (2024 vs 2025) and prices—project-specific factors like site conditions or unusual circumstances drove most variation. Items like demolition work showed more consistent pricing (20-40% variation) compared to excavation.
These results suggest ADOT estimates follow a systematic approach but need wider “risk bands” (like mean ± standard deviation) for high-variation items to avoid budget shocks. Limitations include small sample sizes for some stats and focus only on Department Estimates (not actual contractor bids), plus just two years of data can’t capture long-term trends. Future work could add contractor bids, regional factors, or machine learning to pinpoint what really drives outliers and build better forecasting tools.
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