A machine learning and simulation-based dynamic parking choice model for airports

Abstract

Parking choice models have been developed in previous works with certain limitations, such as fixed demand, lack of real-time knowledge of parking availability at the parking facilities, and fixed parking duration, to name a few. Furthermore, such models when developed using mathematical optimization approaches, suffer from computational complexity that limits their practical applicability. This paper proposes a machine learning and simulation-based dynamic parking choice model for airport parking. It considers real-time variable parking demand, which influences access time and parking cost to various parking lots. Drivers ultimately decide to seek a parking lot depending on: (1) their value of time; (2) parking duration, and (3) availability of a parking lot. The model uses a Random Forest classifier to predict a driver's choice of a parking lot. The results, based on some empirical data, show that parking demand is highly correlated with a driver's decision to choose a parking lot, followed by parking duration and the driver's value of time. The model's accuracy in predicting a driver's parking choice is found to be 99.6%. The model provides real-time parking occupancy and can be very useful for managing airport parking. The model can be used for seeking a parking space by connected vehicles enabled with real-time information on parking availability at various parking locations (or garages). Future works may include extending the method for autonomous vehicles parking allocation and building a user-friendly dashboard with real-time traffic information for automated ground vehicle systems.