A digital twin framework for urban parking management and mobility forecasting.

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 predictiv

SUBMITTER: Piccialli F 

PROVIDER: S-EPMC12549869 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature