A digital twin framework for urban parking management and mobility forecasting – Nature

Executive Summary

Rapid urbanization presents significant challenges to achieving Sustainable Development Goal 11 (SDG 11), which aims to create inclusive, safe, resilient, and sustainable cities. Key issues include traffic congestion, inefficient public transportation, and environmental pollution. This report details the development and implementation of a digital twin framework for urban parking management and mobility forecasting, designed to address these challenges. The framework integrates historical and real-time data—including parking transactions, occupancy rates, and environmental conditions—to provide a comprehensive model of urban mobility. By employing descriptive statistics, a Spatial-Temporal Identity model for prediction, and a Conditional Variational Generative Adversarial Network for simulation, the system forecasts parking demand and supports strategic resource allocation. A core feature is the use of Generative Artificial Intelligence to create realistic what-if scenarios, allowing for the virtual testing of mobility strategies before implementation. This capability directly supports evidence-based urban planning and policymaking aligned with SDG 11. Validation using real-world data from Caserta, Italy, confirms the framework’s potential to enhance urban mobility management, improve accessibility, and reduce inefficiencies, thereby contributing to more sustainable urban environments.

Introduction: Addressing Urban Mobility Challenges through Sustainable Innovation

The Imperative of Sustainable Urban Development (SDG 11)

The global trend of rapid urbanization creates complex challenges that directly impact the quality of urban life and environmental sustainability. Cities are central to achieving the 2030 Agenda for Sustainable Development, particularly SDG 11. Issues such as traffic congestion, inadequate public transport, and air pollution are significant barriers to creating sustainable urban communities. These problems necessitate innovative solutions that leverage technology to optimize city systems. Smart city initiatives aim to address these challenges by using digital technologies and data to build more efficient, sustainable, and livable environments. These initiatives prioritize sustainable transport, effective resource management, and pollution mitigation, aligning technological advancement with environmental and social well-being.

Digital Twins as a Tool for Resilient Infrastructure (SDG 9)

A key technology enabling the transition to smart, sustainable cities is the Digital Twin (DT), a virtual replica of a physical system. In line with SDG 9 (Industry, Innovation, and Infrastructure), DTs represent a significant innovation for building resilient urban infrastructure. By integrating real-time data from sensors with advanced analytical models, DTs allow for the monitoring, simulation, and optimization of complex urban systems. In urban mobility, DTs provide a dynamic representation of transportation networks, enabling planners to:

• Optimize traffic flow to reduce congestion and emissions.
• Enhance the efficiency of public transportation.
• Improve parking management to reduce unnecessary vehicle circulation.
• Simulate the impact of infrastructure changes or policy decisions.

This report presents a DT framework that moves beyond conceptual models by integrating predictive and generative capabilities to provide actionable insights for sustainable urban planning.

A Digital Twin Framework for Sustainable Urban Mobility

The proposed framework is structured around three interconnected components designed to support data-driven decision-making for sustainable urban mobility. This integrated approach aligns with SDG 11 by providing tools to manage urban resources more effectively and reduce the environmental footprint of transportation.

1. Data Statistics and Analysis: This component provides a foundational understanding of urban mobility dynamics. It analyzes historical and real-time data from diverse sources to identify key patterns, correlations, and anomalies. This analysis is crucial for developing targeted interventions that promote sustainability.
2. Predictive Analytics: This component focuses on forecasting critical metrics such as parking demand, traffic flow, and revenue. By using advanced machine learning models, the system provides urban planners with insights to anticipate congestion, optimize resource allocation, and improve the efficiency of urban services.
3. Generative Simulation Engine: This innovative component employs Generative Artificial Intelligence (GenAI) to model what-if scenarios. It allows planners to simulate the impact of potential interventions—such as new parking facilities, road closures, or extreme weather events—before real-world implementation. This supports robust, evidence-based planning for resilient and sustainable urban development.

Results: Data-Driven Insights for Sustainable Cities in Caserta

Statistical Analysis of Urban Mobility Patterns

Statistical analysis of data from the city of Caserta provided critical insights into parking resource usage and user behavior, forming a basis for sustainable policy design. Key findings include:

• Temporal and Spatial Variations: Parking demand showed significant variation between weekdays and weekends, as well as between central and peripheral urban zones. Central zones exhibited higher occupancy, highlighting the need for dynamic pricing or targeted enforcement to balance demand and reduce congestion in core areas.
• Impact of External Factors: Weather conditions, local events, and proximity to Points of Interest (POIs) were found to significantly influence parking patterns. For instance, adverse weather increased parking demand near essential services, while city events created predictable demand spikes. Incorporating these factors into planning is essential for building a resilient urban mobility system.
• Enforcement and Compliance: An inverse relationship was observed between the rate of unauthorized parking and the issuance of fines. This data-driven insight supports the optimization of enforcement strategies to improve compliance and ensure equitable access to parking resources, contributing to a more orderly and efficient urban environment.

Automated Scheduling for Optimized Enforcement

To enhance operational efficiency and support SDG 11’s goal of safe and inclusive cities, an automated system for scheduling traffic warden shifts was developed. The algorithm uses Key Performance Indicators (KPIs) and data on peak occupancy and violation rates to generate equitable and effective weekly schedules. This ensures that enforcement resources are deployed dynamically where they are most needed, maximizing coverage during critical periods and improving overall compliance with parking regulations.

Predictive Analysis for Efficient Resource Allocation

The framework’s predictive models forecast parking meter transactions, revenue, and street occupancy rates with high accuracy. By integrating a wide range of data, including POIs and local events, the models provide actionable intelligence for sustainable mobility management. These forecasts enable urban planners to:

• Optimize parking meter rates to redistribute demand and reduce congestion.
• Design targeted mobility policies, such as reserved or dynamic parking systems.
• Plan infrastructure interventions based on projected needs.

This data-driven approach supports the efficient use of existing infrastructure, a key principle of sustainable urban development.

Simulating ‘What-If’ Scenarios for Resilient Urban Planning

The generative simulation engine allows for the virtual assessment of strategic interventions, reducing the risks associated with urban planning decisions. This capability is crucial for building resilient cities that can adapt to changing conditions. Three key scenarios were simulated:

1. Temporary Road/Zone Closures: Simulations predicted how closures would redistribute traffic, allowing planners to optimize diversion routes and mitigate congestion in adjacent areas. This supports proactive management of both planned events and unplanned disruptions.
2. Construction of a Multistory Parking Facility: The model evaluated the long-term impact of new parking infrastructure, showing how it could alleviate congestion in high-demand zones and lead to a more balanced utilization of parking resources across the city.
3. Impact of Adverse Weather: Simulating consecutive rainy days revealed nuanced shifts in parking demand related to the spatial distribution of POIs. This provides valuable insights for managing mobility during extreme weather events, enhancing the city’s resilience in line with SDG 11.

Discussion: Advancing Sustainable Urban Governance

Contributions to SDG 11: Sustainable Cities and Communities

The developed digital twin framework provides a powerful decision-support tool for advancing the goals of SDG 11. By integrating comprehensive data analysis, predictive analytics, and generative simulations, it enables a holistic approach to urban mobility management. The statistical analysis provides a clear picture of current urban dynamics, while the predictive models allow for proactive resource allocation. The simulation engine supports long-term strategic planning for sustainable and resilient urban infrastructure. This integrated system helps municipalities reduce traffic congestion, lower vehicle emissions, improve air quality, and enhance the overall efficiency of urban operations, directly contributing to making cities more inclusive, safe, resilient, and sustainable.

Framework Scalability and Future Directions

While validated in the city of Caserta, the framework’s modular architecture is designed for scalability and transferability to other urban contexts. Future work will focus on expanding the framework to create a more comprehensive smart city ecosystem. Promising avenues for expansion include:

• Integration with Other Urban Systems: Linking the framework with digital twins for public transportation, traffic flow, and energy usage to enable coordinated, multi-domain decision-making.
• Enhanced Simulation Methods: Incorporating agent-based modeling to gain deeper insights into the complex interactions between socio-economic, environmental, and infrastructural factors.
• Real-Time Data Integration: Increasing the system’s capacity to incorporate real-time data streams from various sensors to enable dynamic resource allocation and greater responsiveness to evolving urban conditions.

These advancements will further enhance the framework’s capacity to support the development of smarter, more sustainable urban environments globally.

SDGs Addressed in the Article

SDG 11: Sustainable Cities and Communities

• The article directly mentions its alignment with SDG 11. The entire focus is on addressing urban challenges like traffic congestion, inefficient public transportation, and environmental pollution through a digital framework for urban mobility management. The goal is to create “smarter, more sustainable urban environments” by improving urban planning, resource allocation, and accessibility.

SDG 9: Industry, Innovation, and Infrastructure

• The article discusses the development and implementation of an innovative “digital framework” that leverages advanced technologies like Digital Twins (DT), Artificial Intelligence (AI), IoT sensors, and Machine Learning (ML). This framework represents an upgrade to urban infrastructure management, making it more resilient, efficient, and sustainable, which directly relates to building resilient infrastructure and fostering innovation.

SDG 13: Climate Action

• The article highlights that a key benefit of optimizing urban mobility and parking is the potential to “decrease pollutant emissions and enhance air quality.” By reducing unnecessary vehicle circulation and traffic congestion, the framework contributes to mitigating the environmental impact of urban transport, which is a crucial aspect of climate action. The integration of air quality data further supports this goal.

Specific SDG Targets Identified

Targets under SDG 11: Sustainable Cities and Communities

1. Target 11.2: Provide access to safe, affordable, accessible and sustainable transport systems for all.
   • The framework aims to tackle “inefficient public transportation” and improve “accessibility.” By forecasting parking demand and optimizing parking management, it reduces the time drivers spend searching for parking, thereby making the overall transport system more efficient and sustainable.
2. Target 11.3: Enhance inclusive and sustainable urbanization and capacity for integrated and sustainable human settlement planning and management.
   • The article’s digital twin is presented as a tool for “urban planning,” “spatial planning,” and “sustainable urban planning.” The ability to generate “what-if scenarios” allows planners to virtually test mobility strategies and infrastructure changes (like road closures or new parking facilities) before real-world implementation, enhancing planning capacity.
3. Target 11.6: Reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality.
   • The framework explicitly supports efforts to “decrease pollutant emissions and enhance air quality.” It achieves this by reducing traffic congestion and integrating environmental data, including “air quality indicators (PM2.5, PM10, NO2, O3),” to understand and mitigate the environmental impact of urban mobility.

Targets under SDG 9: Industry, Innovation, and Infrastructure

1. Target 9.1: Develop quality, reliable, sustainable and resilient infrastructure.
   • The digital framework is a technological infrastructure designed to make the physical infrastructure of urban mobility (parking systems, roads) more resilient and sustainable. It enables proactive responses to disruptions through scenario simulation, such as “temporary road or area closures” or “extraordinary weather conditions.”
2. Target 9.4: Upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency.
   • The article describes a technological upgrade for managing urban infrastructure. The framework’s objective is to “optimize city systems,” “improve resource allocation,” and “reduce inefficiencies,” which directly aligns with increasing resource-use efficiency in urban management.

Targets under SDG 13: Climate Action

1. Target 13.2: Integrate climate change measures into national policies, strategies and planning.
   • The digital twin serves as a decision-support tool that allows urban planners to integrate climate change mitigation measures into their planning. By simulating the impact of different mobility strategies on traffic flow and congestion, planners can choose options that “minimize unnecessary vehicle circulation” and thus “decrease pollutant emissions.”

Indicators for Measuring Progress

Indicators for SDG 11 Targets

• For Target 11.2 (Sustainable Transport): The article implies indicators related to transport efficiency and accessibility, such as:
  • Levels of “traffic congestion.”
  • “Street occupancy rates” and “parking occupancy rates,” which measure the utilization and availability of parking infrastructure.
  • Forecasts of “parking demand,” which help in planning for accessibility.
• For Target 11.6 (Air Quality): The article explicitly mentions indicators used to measure the environmental impact of cities:
  • “Air quality indicators (PM2.5, PM10, NO2, O3).”
  • The “European Air Quality Index (AQI).”
  • Correlation analysis between “parking meter transactions and air pollution levels.”

Indicators for SDG 9 Targets

• For Target 9.1/9.4 (Sustainable Infrastructure/Resource Efficiency): The article implies indicators that measure the efficiency of the urban mobility infrastructure:
  • “Parking meter transactions” and “revenue records” to measure economic efficiency.
  • Rates of “parking violations” and “unauthorized parking” to assess the effectiveness of enforcement and regulation.
  • Metrics on “resource allocation” and reduction in “operational costs for municipalities.”

Indicators for SDG 13 Targets

• For Target 13.2 (Climate Action Integration): The primary implied indicator is the reduction of harmful emissions:
  • The amount of “pollutant emissions” decreased as a result of reduced vehicle circulation and congestion. The framework’s simulation capability allows for the estimation of these reductions under different policy scenarios.

SDGs, Targets, and Indicators Analysis

Source: nature.com