A SUMO-Based Parking Management Framework for Large-Scale Smart Cities Simulations

Abstract

We collectively decided that investing in smart cities, and consequently smart mobility, is the appropriate direction to solve traffic congestion and sustainable growth issues. Among the problems linked to traffic congestion, we find the complexity of efficient multi-modal commuting and the eventual search of a parking spot. Ideally, mobility should be a transparent service for the users and the quest to find parking should not exist in the first place. In order to achieve this goal, we need to study large-scale parking management optimizations. Recently we reached the computational power to simulate and optimize large-scale cities, but problems such as the complexity of the models, the availability of a reliable source of data, and flexible simulation frameworks are still a reality. We present the general-purpose Python Parking Monitoring Library (PyPML) and the mobility simulation framework. We discuss the implementation details, focusing on multi-modal mobility capabilities. We present multiple use-cases to showcase features and highlight why we need large-scale simulations. Finally, we evaluate PyPML performances, and we discuss its evolution.