a) Create a list of hypotheses Uber could use to predict a rider's pickup location with information such as the rider's previous trips and current destination, as well as historical patterns related to the pickup location. Support the case information with your personal Uber experiences to suggest potential hypotheses.

b) Create a quantitative pickup quality metric using attributes derived from the passive, active, and third-party signals available to Uber. Discuss why selected attributes represent a robust pickup quality metric. What weights would you assign to the features you chose for your pickup model?

c) Based on your pickup quality metric, what actions can Uber operators take to improve the pickup experience?

d) How would you improve the pickup experience at venues such as sporting events and concerts, which typically see temporary surges in demand for Uber rides, as well as temporary parking restrictions and traffic congestion?