Preparing Urban Curbside for Increasing Mobility-on-Demand Using Data-Driven Agent-Based Simulation: Case Study of City of Gainesville, Florida

Abstract

Cities in many countries are witnessing an era of transformative innovations in vehicular technologies and mobility-on-demand (MoD) services in the context of global initiatives of smart and connected cities. However, advances in the built environment where vehicles operate have not maintained the same pace. The new MoD especially burdens curb environments in urban cores due to competition for spaces for pick-ups and drop-offs (PUDO). These uncoordinated and diverse uses without data-driven management have led to increased safety and sustainability issues. This research intends to address the increasing curbside uses of PUDO activities due to MoD services and proposes a data-driven agent-based simulation approach to plan designated PUDO zones in limited public curbside spaces. A case study was conducted for five street blocks in urban cores of the City of Gainesville, Florida, United States, based on longitudinal parking transaction and violation records, place visitation data, and geospatial data of parking assets. The results show that temporary PUDO zones should be designated at all investigated blocks during peak hours even when the MoD market penetration rate is low (i.e., 10%), which helps mitigate the occurrences of competing use events, while permanent PUDO zones should be designated for the two busiest blocks when the MoD market penetration rates increase to 30% or 50%. Sensitivity analyses suggest that the designated PUDO zones can mitigate curbside stresses more effectively when regulating MoD users’ PUDO dwell time (e.g., within one minute). This research aims to contribute to data-driven public asset managerial decision-making and strategies in smart cities and benefits more accessible and sustainable living environments in urban cores.