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Teaching Tip: NYC’s Potholes and Regression Analysis

June 26, 2014

potholeNew York is famous for many things, but one it does not like to be known for is its large and numerous potholes. David Letterman used to joke: “There is a pothole so big on 8th Avenue, it has its own Starbucks in it.” When it comes to potholes, some years seem to be worse than others. This winter was an exceptionally bad year. City workers filled a record 300,000 potholes during the first 4 months of 2014. That’s an astounding accomplishment.

But potholes are to some extent a measure of municipal competence–and are costly in many ways. NYC’s poor streets cost the average motorist an estimated $800 per year in repair work and new tires. There has been a steady and dramatic increase in potholes from around 70,000- 80,000 in the 1990s to the devastatingly high 200,000- 300,000 range in the most recent years. One theory is that bad weather causes the potholes, writes OR/MS Today (June, 2014). Using inches of snowfall as a measure of the severity of the winter, the graph on the left shows a plot of the number of potholes vs. the inches of snow each winter. (R-squared = .32).Pothole-analytics

Research showed that the city would need to resurface at least 1,000 miles of roads per year just to stay even with road deterioration. Any amount below that would contribute to a “gap” or backlog of streets needing repair. The right-side graph shows the plot of potholes vs. the gap. With an R-squared of .81, there is a very strong relationship between the increase in the “gap” and the number of potholes. It is obvious that the real reason for the steady and substantial increase in the number of potholes is due to the increasing gap in road resurfacing.Pothole-analytics2

A third model performs a regression analysis using the resurfacing gap and inches of snow as 2 independent variables and number of potholes as the dependent variable. That regression model’s R squared is .91.

Potholes = 7801.5 + 80.6 * Resurfacing Gap + 930.1 * Inches of Snow

We are always looking for real-world, down-to-earth examples of forecasting models to share with classes. This may do the job!

 

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