Preparing Urban Curbside for Increasing Mobility-on-Demand Using Data-Driven Agent-Based Simulation: Case Study of City of Gainesville, Florida
Publication: Journal of Management in Engineering
Volume 38, Issue 3
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.
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Data Availability Statement
Parking transaction data, violation data, and POI visitation frequency data used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments. Matlab and Python code generated or used during the study are available from the corresponding author upon reasonable request.
Acknowledgments
This material is based upon work supported by the National Science Foundation under Grant No. 2124858, the University of Florida’s faculty start-up funds, and the Graduate School Fellowship Award. The authors are grateful for the parking data shared by the City of Gainesville and the POIs’ visitation data shared by SafeGraph data. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation and the University of Florida.
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Received: Apr 13, 2021
Accepted: Nov 23, 2021
Published online: Jan 28, 2022
Published in print: May 1, 2022
Discussion open until: Jun 28, 2022
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