A digital twin framework for urban parking management and mobility forecasting

Francesco Piccialli (), Sara Amitrano, Donato Cerciello, Anna Borrelli, Edoardo Prezioso and Marzia Canzaniello ()
Additional contact information
Francesco Piccialli: University of Naples Federico II
Sara Amitrano: University of Naples Federico II
Donato Cerciello: University of Naples Federico II
Anna Borrelli: University of Naples Federico II
Edoardo Prezioso: University of Naples Federico II
Marzia Canzaniello: University of Naples Federico II

Nature Communications, 2025, vol. 16, issue 1, 1-20

Abstract: Abstract Rapid urbanization and population growth have created significant challenges in urban mobility management, such as traffic congestion, inefficient public transportation, and environmental pollution. Here we present the development and implementation of a digital framework for urban parking management and mobility forecasting. The framework integrates a wide range of historical and real-time data, including parking meter transactions, revenue records, street occupancy rates, parking violations, and sensor-based parking slot utilization. Additionally, the data encompass weather conditions, temporal patterns (such as weekdays and peak hours), and agent shift schedules. Descriptive statistics are used to identify key patterns, while the Spatial-Temporal Identity model is used for the predictive phase, and the Conditional Variational Generative Adversarial Network is used for the generative phase of the digital twin. The algorithm allows forecasting parking demand and mapping data for spatial planning and resource allocation. Moreover, the integration of the Generative Artificial Intelligence model generates realistic what-if scenarios for virtual testing of mobility strategies before real-world implementation. The results highlight the framework’s potential to enhance urban mobility management, especially by improving parking meter placement and reducing inefficiencies and improving accessibility. Validation on real-world data from the city of Caserta, confirms the robustness and adaptivity of the proposed framework, although expanding the dataset and refining specific components are necessary for fully realizing its potential and ensuring sustainable urban planning.

Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-65306-w Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-65306-w

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-65306-w

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().