Melbourne’s Parking Dynamics and Traffic Flow: A Dataset for Microscopic Agent-based Simulation

Private vehicles park 95% of the time, prompting urban planners and policymakers to implement policies to manage parking supply and demand in city centres efficiently. Understanding the parking dynamics and its correlation with traffic flow is essential for effective urban infrastructure planning and policy formulation. This paper introduces a comprehensive dataset that includes real-world data on the road network and parking spaces, with traffic flow generated based on demographic information from the City of Melbourne. The dataset enables the investigation of the correlation between parking dynamics and traffic flow across city zones with heterogeneous parking demand and traffic flow characteristics. Designed for use with the Simulation of Urban MObility (SUMO) application, this dataset serves as a baseline to simulate and analyze parking dynamics and traffic flow at a microscopic level. Additionally, the paper provides the code necessary to generate similar datasets for other geographical locations or to adjust the parking dynamics and traffic flow parameters. Our dataset follows the FAIR principles (Findable, Accessible, Interoperable, and Reusable), ensuring that it is easy to locate, access, integrate with other data, and apply in various research contexts. This resource can significantly benefit researchers in transport and urban planning by facilitating the setup of real-world simulations of parking dynamics and traffic flow, thereby enabling a deeper understanding and more informed decision-making in urban infrastructure management.