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Pothole PredictorA tool for forecasting New York City's pothole problem

Dyfrig Mon

Potholes are a Problem

Source: New York Times, Joshua Jamerson 30, 2015

Target user – Department of Transportation NYC

Motivation – liability ($140 million)

Problem – Number of potholes varies drastically by month to month

Analysis

Data● NYC non-emergency call data (311)● NOAA historic weather data

Features● ~20 features● Engineered another ~50 features ● Month of year● Weather in previous months● Rolling Mean● Moving Average● Autoregressive

Model● Linear regression● Recursive Feature Elimination

Forecast

TrainTrain Test

Forecast

TrainTrain

Forecast

Train

Forecast

Linear RegressionR^2= 0.70MAE= 1729 potholes

Model Comparison

Linear RegressionR^2= 0.70MAE= 1729 potholes

ARIMAR^2= 0.24MAE= 2317 potholes

Detailed Model Comparison

Most Important Feature

Take Away

About Me

Dyfrig MonSwansea University (UK)PhD - Applied PhysicsSolar Cells, X-rays and Neutron scattering

Backup Slides

Features

Snow fall in previous months ---> proxy for other things (gritting, plowing etc.)

Top 20 Coefficients

Residuals

ARMA(2,2) Model

Last Month Model

All Features

● Snow fall 1 month ago

● Snow fall 2 months ago



● Snow fall change between 1 and 2 months ago




● Snow fall 1 month ago ^2

● Snow fall 2 months ago ^2



All Features

● Snow fall change between 1 and 2 months ago ^2




● Snow fall 1 month ago ^3








All Features

● Mean Temperature 1 month ago

● Mean Temperature 2 months ago



● Mean Temperature change between 1 and 2 months ago

● Mean Temperature change between 2 and 3 months ago

● Mean Temperature change between 3 and 4 months Ago

● Mean Temperature change between 4 and 5 months Ago

● Precipitation 1 month ago

● Precipitation 2 months ago


All Features


● Precipitation change between 1 and 2 months ago



● Precipitation change between 4 abd 5 months ago

● Rolling Mean last 2 months

● Rolling Mean last 3 months

● Autoregressive(2)

● Autoregressive(3)

● Value_Dif

● Linear_Approx

All Features

● Rolling Mean Expanding Window

● Moving Average(1)



● Autoregressive(2)(12)

● Population Mean

● Moving Average(1)(12)

● Constant

All Features

● Month of January

● Month of Febuary

● Month of March

● Month of April

● Month of May

● Month of June

● Month of July

● Month of August

● Month of September

● Month of October

● Month of November

● Month of December

Staffing Formula

num. teams required to fix potholes = round(num. Potholes / (daily pothole fixing rate * num. Work days per month) + 0.5)

staf required =num. teams required to fix potholes * Num. people in team

Solution

Accurate pothole forecast could be Accurate pothole forecast could be used to estimate resource used to estimate resource requirementsrequirements

(staffing, monetary etc.)(staffing, monetary etc.)

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Data & Analytics