Smart parking pricing: A machine learning approach

Abstract

Crowded streets are a major problem in large cities. A large part of the problem stems from drivers seeking on-street parking. Cities such as San Francisco, Los Angeles and Seattle have tackled this problem with smart parking systems that aim to maintain the on-street parking occupancy rates around a target level, thus ensuring that empty spots are spread across the city rather than clustered in a single area. In this study, we use the San Francisco's SFpark system as a case study. Specifically, in each given parking area, the SFpark uses occupancy rate data from the previous month to adjust the price in the current month. Instead, we propose a machine learning approach that predicts the occupancy rate of a parking area based on past occupancy rates and prices from an entire neighborhood (which covers many parking areas). We further formulate an optimization problem for the prices in each parking area that minimize the root mean squared error (RMSE) between the predicted occupancy rates of all areas in the neighborhood and the target occupancy rates. This approach is novel in that 1) it responds to a predicted level of occupancy rate rather than past data and 2) it find prices that optimize the total occupancy rate of all neighborhoods, taking under account that prices in one area can impact the demand in adjacent areas. We conduct a numerical study, using data collected from the SFpark study, that shows that the prices obtained from our optimization lead to occupancy rates that are very close to the desired target level.