A large city is trying to plan its road construction so that its roads are appropriately-sized for when cars are all self-driving (autonomous). Research has suggested that adding autonomous vehicles to the current system could significantly reduce traffic by reducing the variance in speed of both the autonomous and human driving vehicles. The city would like to use analytics to help determine how many lanes its highways and major streets will need to have in the future.

GIVEN the fraction of autonomous cars in the city over the past, Use exponential smoothing to estimate the fraction of autonomous cars in the city in the next 10 years. Then, GIVEN that estimate and an expert estimate of how many of those cars will be going from each part of the city to each other part during rush hour, Use linear regression To predict the number of lanes required on each other part during 10 years from now. Finally, Given the number of cars going between parts of the city during rush hour and the number of lanes on each major street and highway Use optimization TO find the quickest route for each car to use.

a.Given the small fraction of autonomous vehicles in the city in the past 5 years, exponential smoothing cannot capture the possibility of a major increase in the next 10 years.

b.Optimization cannot find the quickest route for a car to use

c.Exponential smoothing is not an appropriate model to predict 10 data points in the future based on just 5 data points.

d.Linear regression cannot predict the number of lanes required based only on the number of cars going between parts of the city.