Modeling the Interactions Between Smart Urban Logistics and Urban Access Management: A System Dynamics Perspective ^†

Abstract

In response to the challenges of urbanization, digitalization, and the e-commerce surge intensified by the COVID-19 pandemic, Smart Urban Logistics (SUL) has become a key framework for addressing last-mile delivery issues, congestion, and environmental impacts. This study introduces a System Dynamics (SD)-based approach to investigate how urban logistics and access management policies may interact. At the center, there is a Causal Loop Diagram (CLD) that illustrates dynamic interdependencies among fleet composition, access regulations, logistics productivity, and environmental externalities. The CLD is a conceptual basis for future stock-and-flow simulations to support data-driven decision-making. The approach highlights the importance of route optimization, dynamic access control, and smart parking management systems as strategic tools, increasingly enabled by Industry 4.0 technologies, such as IoT, big data analytics, AI, and cyber-physical systems, which support real-time monitoring and adaptive planning. In alignment with the Industry 5.0 paradigm, this technological integration is paired with social and environmental sustainability goals. The study also emphasizes public–private collaboration in designing access policies and promoting alternative fuel vehicle adoption, supported by specific incentives. These coordinated efforts contribute to achieving the objectives of the 2030 Agenda, fostering a cleaner, more efficient, and inclusive urban logistics ecosystem.

1. Introduction

This section contextualizes the research by outlining the challenges of urban logistics, access management strategies, and the integration of Industry 4.0/5.0 technologies. It concludes with the formulation of the problem statement and the study’s significance.

1.1. Background Information

Urban logistics confronts a myriad of challenges in contemporary cities, especially with the emergence of Industry 4.0, enabling technologies that promise to revolutionize transportation systems [1,2]. Severe traffic congestion in densely populated urban centers causes considerable delays in deliveries, contributes to increased air pollution, and negatively affects public health. Furthermore, restricted road accessibility exacerbates logistical difficulties by disrupting optimal delivery routes and raising operational expenses. Safety concerns also arise from the heavy traffic, placing both drivers and pedestrians at risk. Urban logistics significantly contributes to environmental harm due to high levels of greenhouse gas emissions. This situation demands a transition to low-emission vehicle fleets and the implementation of eco-friendly delivery methods to address climate change effectively. The COVID-19 pandemic has further exacerbated these challenges by triggering a surge in online commerce and delivery demand, thereby intensifying congestion and pollution, and exposing the shortcomings of existing logistics infrastructures, underscoring the urgent need for more robust and adaptable systems [3].

1.2. Smart Urban Logistics and Access Management Strategies

In this context, advanced Smart Urban Logistics (SUL) system modeling [4] and sophisticated urban access management strategies [5] play a crucial role in achieving the 2030 Agenda goals [6], thereby promoting sustainable and inclusive urban growth. Crucial in this scenario is the application of emerging Information and Communication Technologies (ICT) for mitigating the negative impacts of last-mile logistics and enhancing its efficacy and sustainability [7].

The academic community has increasingly focused on smart urban logistics, supporting policymakers in developing freight transportation solutions that minimize adverse environmental and social externalities. For example, the European Union has championed sustainable logistics practices [8] while simultaneously orchestrating projects to systematically classify and assess various urban intervention initiatives. Notably, the ReVeAL project, which focuses on regulating urban vehicle access [9], stands out as particularly relevant for the proposed model. This project empowers cities to optimize the use of urban space and transportation networks by implementing innovative packages of policies and technologies. The ReVeAL project delineates three primary mechanisms for enacting Urban Vehicle Access Regulations (UVAR) [10], collectively referred to as “Measure Fields.” Different territorial scales demand tailored approaches for urban logistics interventions, which are typically integrated into Sustainable Urban Logistics Plans (SULPs) [11]. In the European context, SULPs are primarily developed by local and regional authorities in close collaboration with a wide range of stakeholders in the urban logistics sector. These include transport and logistics companies, service providers, representatives of the commercial sector, freight recipients, trade associations, logistics and urban planning experts, as well as citizens and civil society concerned with the impact of logistics on urban quality of life. The collaborative approach is crucial in the drafting of SULPs at the European level, aiming to develop strategies that respond effectively and sustainably to the freight movement needs of urban areas, while minimizing negative impacts on the environment and society. UVAR strategies bring several advantages, such as lowering emissions, enhancing air quality, encouraging eco-friendly mobility practices, and easing traffic congestion. Nonetheless, these measures may also entail certain disadvantages, including possible adverse economic consequences, restrictions on personal mobility, unforeseen repercussions, and difficulties in execution. UVAR schemes may impose financial burdens on enterprises, such as the need to upgrade or replace vehicles to comply with regulations, acquire permits, or pay charges and penalties for infractions [12]. To reduce these adverse effects, incentives for the modernization of vehicle fleets have been implemented. Furthermore, the European Parliament [13] underlines the essential function of urban transport in enhancing citizens’ well-being and fostering economic performance. It advocates for intelligent, inclusive, cost-effective, and well-integrated transport networks. With respect to UVAR initiatives, the resolution underlines the importance of comprehensive impact evaluations and participatory processes. It also emphasizes the need for clear communication and the timely involvement of all relevant actors, especially regarding the economic dimensions of such policies.

Another aspect to consider is the coordination of parking areas dedicated to loading and unloading activities. On-street parking plays a crucial role in influencing urban traffic efficiency in three primary ways. Firstly, parking lanes utilize road space, thereby diminishing the available capacity for traffic flow both on the main road and in adjacent networks. Secondly, the actions of parking and exiting a parking spot temporarily disrupt the traffic stream, resulting in increased delays. Thirdly, certain parking practices, including unauthorized stops or the presence of delivery trucks and buses during loading or unloading, limit roadway availability and lead to disruption and uncertainty for other road users [14]. From this perspective, the proposed model incorporates dynamic parking management measures based on the implementation of policies and technologies aimed at regulating parking spaces according to demand and contextual factors. Dynamic parking management is configured as a flexible tool, capable of adjusting in real time variables such as pricing, availability, and time constraints, to maximize the efficiency of parking space utilization [15]. This approach contributes to reducing urban congestion, improving traffic flow, and enhancing the overall performance of parking systems. Regulatory strategies can be adopted either upstream, through scheduled time restrictions, or in real time via the use of Intelligent Transportation Systems (ITS). Recognized as one of the most significant innovations in the field of urban logistics, dynamic parking management provides multiple benefits. It allows for the rational use of available space, ensuring more efficient utilization of loading and unloading areas by logistics operators. This leads to minimized waiting times, enhanced delivery scheduling accuracy, and, more broadly, increased efficiency in the urban freight distribution process. Furthermore, this type of management promotes a higher turnover of commercial vehicles, increasing access to operational areas for a larger number of logistics providers. However, the implementation of such systems presents challenges that must be addressed. Among these are the high initial costs associated with investments in technological infrastructure, as well as the need for structured cooperation between public and private stakeholders to ensure effective governance and ongoing maintenance of the installed systems. Despite these obstacles, dynamic parking management stands out as a strategic solution to optimize urban logistics processes, enhance the efficiency of the supply chain, and mitigate the environmental and social impacts of urban mobility [16].

In summary, the effective management of last-mile logistics, supported by emerging Information and Communication Technologies (ICT) underpinned by Industry 4.0 and 5.0 paradigms, is confirmed as a cornerstone for achieving a sustainable urban future.

1.3. Integration of Enabling Technologies

The transformation of SUL is deeply connected to the integration of the nine enabling technology categories identified by the Boston Consulting Group relating to Industry 4.0 [17]. While these technologies have unlocked substantial improvements in automation, connectivity, and operational efficiency within urban logistics, a shift towards Industry 5.0 compels us to address the deeper socio-environmental challenges faced by cities and their freight networks. From an Industry 5.0 perspective, the focus moves beyond pure productivity to embrace human-centricity, sustainability, and systemic resilience, placing worker well-being and planetary boundaries at the heart of innovation. Industry 5.0 is defined by the European Commission [18] as a paradigm that complements Industry 4.0′s digital transformation with research and innovation aimed at creating a more sustainable, human-centric and resilient industrial ecosystem. It emphasizes stakeholder value over shareholder value, promoting technologies that work in harmony with human operators and ecological limits.

These technological advancements are not merely theoretical but have already found practical application in several European urban contexts, where integrated policies and digital infrastructure have significantly improved freight access management. Since August 2007, Stockholm has operated a cordon-based congestion tax for all vehicles—including freight trucks—at 18 entry and exit points equipped with Automatic Number Plate Recognition (ANPR) cameras. Ordinary vehicles are automatically billed, while heavy goods vehicles (≥14 t) pay the same tariff but may obtain special permits to use dedicated lanes during off-peak hours. The system leverages IoT sensors for real-time traffic monitoring and integrates with the city’s ITS platform to adjust charges if needed. Within the first year of full implementation, overall traffic volumes in the tolled zone fell by about 20%, CO₂ emissions dropped by 2–3%, and public transit ridership rose by 2–3% [19]. Beginning in January 2013, Gothenburg introduced a barrier-free congestion charge across 38 ANPR-monitored gates. All vehicles, including delivery vans and heavy trucks, are subject to variable fees based on time of day, with exemptions for emergency services and local deliveries that do not cross more than two gates within 30 min. The municipality also employs IoT-enabled parking sensors to manage loading bays and issues loading permits tied to specific time slots. After one year, central area traffic decreased by 10% and public transport usage increased by 9% [20].

This article delves into the intricate web of interrelationships among key factors shaping SUL and urban access management, with a focus on elucidating a systematic approach through a System Dynamics (SD)-based model. The central query driving this investigation is: what strategies can effectively harmonize the burgeoning demands of urban freight traffic with the imperative of sustainable urban development? Crucially, this inquiry takes into consideration the diverse stakeholders involved and their often contrasting needs. Our findings underscore that optimizing fleet composition, integrating electric or hybrid vehicles and human-powered vehicles (HP) or assisted vehicles (AV) for proximity deliveries, is crucial for balancing urban freight demands with sustainable development. Using an SD approach, we highlighted how altering fleet composition impacts emissions reduction, traffic congestion, and operational efficiency, making it a pivotal strategy for policymakers. Strategies such as access management, incentives for cleaner technologies, and improved urban space management can influence fleet composition. However, not all strategies achieve the desired outcomes; for instance, while switching to electric vehicles reduces emissions, it may not alleviate congestion. This complexity necessitates comprehensive approaches addressing multiple sustainability dimensions, enhancing environmental sustainability and urban resource management efficiency. To enhance the explanatory capabilities of the approach, an extension is proposed through the integration of key enabling technologies derived from the Industry 4.0 and 5.0 paradigms. Among these are advanced systems for route optimization, dynamic urban access regulation, and real-time parking management.

1.4. Problem Statement and Research Significance

While SUL and access regulation policies have been proposed to address the challenges of urban logistics in contemporary cities, existing approaches often lack integrated and systemic evaluation frameworks. There is a gap in understanding how various access policies interact with logistics dynamics, stakeholder behavior, fleet composition, and technological adoption over time. In particular, there is limited use for simulation-based methods to evaluate the long-term effects and interdependencies of these variables on the performance and sustainability of urban freight systems.

Providing the basis for subsequent in-depth analysis based on a Stock and Flow quantitative model, this study aims to improve the understanding of the short- and long-term effects generated on the system by the introduction of certain SUL measures (such as access management interventions, incentives for purchasing alternative vehicles, and dynamic parking management). In this extended framework, enabling technologies act as catalysts for innovation, offering practical tools to enhance the effectiveness of these measures. Practical implications include providing urban planners and policymakers with a framework to implement sustainable logistics solutions and improve overall urban mobility. These solutions, composed of a mix of various elements, aim to not only meet the current demands of urban freight traffic but also align with the long-term objectives of the 2030 Agenda for sustainable development.

In Section 2, through a comprehensive examination of the State of the Art in access management definition, the study navigates through various access management options, offering insights into their efficacy and implications. In Section 3, the presentation of a causal loop diagram (CLD), a visual representation aimed at revealing the intricate interdependencies and feedback loops inherent in SUL systems, is proposed. In Section 4, by illuminating these complex relationships, the approach provides a roadmap to navigate the nuanced terrain of urban logistics and access management, fostering sustainable urban development while mitigating logistical challenges.

2. State of the Art

This section delineates the state of the art in urban logistics, delving into measures, technologies, and emerging paradigms for urban vehicle access regulations. It examines stakeholder perspectives and illustrates the primary simulation approaches employed within the field.

2.1. Urban Logistics and Smart Urban Vehicle Access Regulations: Measures, Technologies, and Emerging Paradigms

Urban logistics plays an increasingly important role in modern cities, where efficient transportation management is essential to ensure residents’ well-being, support economic development, and mitigate the negative impacts of transportation [4]. One of the main challenges is minimizing the adverse effects of transportation, including traffic congestion, air and noise pollution, as well as environmental impact from greenhouse gas emissions. Addressing these challenges requires the adoption of innovative policies and strategies, such as promoting sustainable transportation modes, optimizing delivery routes, and introducing low-emission vehicles. These efforts align directly with the principles of Industry 5.0, particularly its emphasis on sustainability and fostering resilient urban systems. Various classifications of measures to improve urban logistics have been proposed. Merchan and Blanco [21] proposed categorizing best practices into four areas: Urban Logistics Spaces for Multi-Tier Last-Mile Distribution, Emerging Vehicles for Last-Mile Distribution, Complementary Last-Mile Distribution Strategies, and Additional Technologies. The first category encompasses Urban Consolidation/Transfer Centers, Micro-deconsolidation Platforms, Micro-consolidation Platforms, Delivery Bays, and Automatic Parcel Terminals. In the second category, solutions involving the use of alternative vehicles are included, such as Cargo-cycles, Electric Trucks, Mobile Warehouses, and Autonomous and Semi-autonomous Vehicles. The third category comprises complementary measures, including Off-hour Deliveries, On-demand (Crowd-sourced) Last-mile Services, and Last-Mile Delivery using the Bus Rapid Transit/Subway System. Finally, the last category includes technological solutions such as GPS Sensors and Data for Logistics. De Marco et al. [22] divided the measures of City logistics into: Infrastructure, Regulation, and Technology. Research has not only classified various measures for improving urban logistics but has also focused on analyzing how institutional entities can best combine interventions as they identify and implement them within their strategic plans [11]. Within the classification of ‘Measure Fields’, the ReVeAL project further identified a series of Building Blocks [11], which represent the operational components through which measures are implemented in urban contexts. These include:

• Regulatory measures: establishing legal frameworks to control vehicle entry into specific areas, including Zero Emission Zones, Low Emission Zones, Limited Traffic Zones (LTZ), as well as regulations based on vehicle characteristics, trip purposes, or permitting systems. A Zero-emission Zone (ZEZ) is a designated area where only vehicles that produce zero emissions are allowed to enter. An LTZ is a defined spatial area characterized by low traffic volume, typically resulting from access regulations implemented by the local municipality. While a Low-Emission-Zone (LEZ) is an area where vehicular access is limited to vehicles that meet certain emissions characteristics;
• Spatial interventions: redesigning urban road layouts to prioritize sustainable mobility and restrict vehicular traffic, such as repurposing roads and parking areas for alternative modes of transport;
• Pricing aspects: introducing economic measures to incentivize sustainable travel choices, such as congestion-based tolls, distance-dependent charges, or fees determined by emission standards.

According to the ReVeAL project, the various building blocks within this regulating measure field can be combined to form a comprehensive UVAR scheme. These components include:

• Regulations by emissions: this aspect involves implementing regulations based on vehicle emissions, ensuring that only vehicles meeting specific emission standards are allowed to enter designated areas (e.g., Brussels, Belgium) [12];
• Regulations by vehicle type and dimension (vehicle type, weight, or length): in Paris, France, measures are enforced to control vehicle entry based on their types, restricting access for specific categories of vehicles [13]. In Utrecht, the Netherlands, regulations distinguish between Heavy Duty Vehicles (HDV) and Light Duty Vehicles (LDV) by imposing limits based on their dimensions [14];
• Regulations by trip purpose (delivery; residents and specific users): in Strasbourg, France, regulatory measures are tailored to the purpose of the trip, particularly targeting delivery vehicles and regulating their access to specific zones [9];
• Scheme timescale: these schemes may rely on programmed time windows, which refer to fixed and pre-established intervals during which regulations are applied; reactive operations, which allow for dynamic adjustments based on real-time conditions or unexpected events; or phasing, which involve the gradual introduction or withdrawal of measures to ensure a smoother adaptation process for users and stakeholders. Madrid, Spain [9], implements regulatory schemes within designated time windows, controlling vehicle access during specific periods of the day; similarly, Stockholm, Sweden, applies time-based restrictions, with a particular focus on nighttime regulations that may restrict vehicle access during late hours [15];
• Regulations by permit: this regulatory category includes several forms of access control based on formal authorization. For example the “permit to travel” refers to an authorization required for accessing specific urban zones with a vehicle, often conditioned by vehicle category, time of day, or user profile; the “car park or ownership permit” regulates the right to own or park a vehicle within restricted areas, typically granted to residents or specific business operators: the “permit to build car park space” concerns urban planning permissions necessary to construct new parking facilities, serving as a strategic tool to manage parking supply and promote sustainable mobility. Siena, Italy, requires a permit to travel for vehicles wishing to access certain areas, regulating entry based on permits issued by the authorities [16]; London, UK, implements regulations based on planning permits, particularly controlling vehicle access to specific areas [17];
• Regulations by other factors: additional considerations such as load factors, vehicle safety features, company size, or the reallocation of road space are also integrated into the UVAR framework.

While this research typically adopts a more general approach, there are numerous studies centered on specific measures, aiming to optimize their implementation or test their outcomes, such as those focused on Zero Emission Zone [23] or Low Emission Zone [24]. Simulation is relevant in the field of logistics, enabling analysis, optimization, and planning of logistics strategies and operations more effectively.

The evolution of Urban Vehicle Access Regulations (UVAR) has been increasingly supported by enabling technologies from the Industry 4.0 paradigm. These technologies provide the necessary infrastructure and analytical capabilities to design, implement, and monitor UVAR schemes effectively, especially in complex and dynamic urban logistics contexts. The following categories highlight key applications within this framework:

• Advanced manufacturing solutions: in urban logistics, advanced manufacturing enables the on-demand production of components for electric vehicles or cargo bikes used for urban deliveries, supporting modularity and customization of the vehicles operating in access-regulated zones;
• Additive manufacturing: this technology can be used to produce tailored infrastructure for urban logistics, such as temporary micro-depots or modular lockers near UVAR zones, reducing the setup time and cost of last-mile delivery systems;
• Augmented Reality (AR): AR can support delivery route planning and operator training, for example, by providing real-time digital overlays to aid navigation in restricted or dynamically regulated urban areas, improving operational accuracy; it may also serve to route parcel carriers to their final drop destination, enhancing efficiency in the last-mile delivery process;
• Simulation: dynamic or agent-based simulations are essential for evaluating the impact of UVAR on freight flows, delivery times, emissions, and public space saturation. These models allow optimization of regulatory parameters before real-world implementation; this may apply to analyze specific sectors, e.g., urban transport of construction materials and other time-sensitive or logistically complex deliveries;
• Horizontal and vertical integration: horizontal integration among urban logistics operators supports shared use of micro-hubs and low-impact vehicles, reducing redundant delivery routes. In this context, urban freight consolidation centers represent a strategic infrastructure, facilitating operator integration and the efficient use of low-emission vehicles. Vertical integration between suppliers, carriers, and public authorities enables real-time coordinated management of permits and access through interoperable digital systems;
• Internet and Internet of Things (IoT): IoT sensors on vehicles and infrastructure allow real-time monitoring of logistics flows and compliance with UVAR, enhancing efficiency and traceability. These devices can also trigger automatic corrective actions in cases of congestion or rule violations;
• Cloud computing: cloud platforms centralize data on access permits, delivery time slots, logistics flows, and environmental reporting, facilitating interaction among stakeholders (authorities, operators, citizens) and enabling flexible, data-driven access policies;
• Cybersecurity and Business Continuity: securing the digital infrastructure that governs access to urban centers is crucial to ensure operational continuity and protection in automated authorization and control systems, preventing disruptions and misuse in urban delivery systems;
• Big Data and Analytics: analysis of big data from GPS, IoT sensors, digital permits, and environmental records helps assess the effectiveness of UVAR, identify congestion patterns, and plan more efficient, adaptive, and sustainable access policies over time.

Building upon the foundations laid by Industry 4.0, the transition towards Industry 5.0 introduces a more human-centric, sustainable, and resilient approach to Smart Urban Logistics (SUL). This paradigm shift emphasizes the synergy between advanced technology and human agency. In SUL, this evolution manifests, for instance, through the following:

• Floating Car Data (FCD) that significantly contributes to the resilience of urban logistics systems enabling adaptive management and rapid response to disruptions or changing traffic conditions [25];
• Digital twins for real-time simulation of delivery corridors and management of loading zones [26], enabling planners to evaluate UVAR scenarios before deployment;
• Collaborative AI systems that dynamically allocate curb-side slots and optimize last-mile routing in response to live traffic and demand data;
• Human–machine interfaces (e.g., wearable AR devices) that enhance courier ergonomics, safety, and decision support during complex urban deliveries;
• Nature-inspired materials and bio-inspired designs for sustainable packaging and adaptive infrastructure elements that reduce environmental impact and support circular-economy goals.

2.2. Stakeholders’ Perspectives and Interactions

The success of access management strategies and sustainable urban logistics policies hinges on the alignment and collaboration of multiple stakeholders, each characterized by distinct roles, interests, and levels of influence. These stakeholders can be considered from three overarching perspectives—Demand, Supply, and Urban Governance—with further distinctions that help clarify the complexity of their interactions [27].

Customers represent the Demand side of the system. Their decisions are primarily driven by convenience, speed, and cost, typically preferring rapid, reliable, and low-cost or free delivery services. Their consumption habits, especially the growing expectation for next-day or same-day delivery, exert pressure on logistics operators to increase delivery frequency and fragmentation, often at the expense of environmental and social sustainability. Customers might express dissatisfaction if restrictions lead to delays or additional charges, unless adequately informed about environmental benefits or offered alternative delivery options that preserve convenience.

Courier, Express, and Parcel (CEP) companies, falling under the logistics operators category, are key actors on the Supply side. Their primary objective is to minimize delivery costs while maximizing operational efficiency. This frequently results in high vehicle turnover, suboptimal load factors, and a prioritization of route efficiency over environmental impact. These firms are also subject to municipal regulations and may resist measures that increase delivery times or operating costs. In response to restrictive access management policies, logistics operators might initially resist, perceiving them as cost-increasing constraints that hinder delivery efficiency. To mitigate such opposition, compensatory measures, such as micro-consolidation centers or special access permits for low-emission vehicles, may be necessary.

Beyond CEP operators, construction companies and their suppliers are crucial Supply side stakeholders. These actors manage significant transport flows with heavy vehicles, generating notable impacts on urban infrastructure and the environment. Their need to access sites in densely populated areas makes dialog and collaboration with municipal authorities essential for integrating their needs into sustainable urban logistics strategies [28].

Shippers and e-commerce platforms (retailers) act as strategic intermediaries within the Supply side, facilitating the connection between customer demand and the physical provision of services. Their competitive strategies revolve around satisfying individualized consumer preferences, ensuring rapid fulfillment, and maintaining customer loyalty. In this context, they often delegate logistics operations to CEP companies while retaining strategic control over service standards and pricing. Their influence is central in shaping logistics models, particularly through the definition of delivery conditions, return policies, and warehouse location strategies. Shippers and retailers might respond to restrictions by adapting their distribution networks, for instance, by shifting to local pickup points or increasing reliance on urban warehouses, but they may also pressure logistics providers to absorb new costs, thereby transferring tensions down the supply chain.

Local governments and urban authorities, identified here as municipalities, are responsible for regulating urban logistics activities in the broader community’s interest, exercising Urban Governance. Their objectives include improving air quality, reducing traffic congestion, enhancing road safety, and promoting equitable access to public space. From their perspective, access management policies serve as instruments to rebalance the utilization of urban space and minimize negative externalities. However, they face the challenge of balancing these objectives with the economic viability of freight activities and the expectations of other stakeholders. Municipalities are tasked with ensuring these policies are equitable, enforceable, and accompanied by clear communication strategies. Pilot projects and stakeholder engagement can be effective in facilitating their implementation.

Residents and local communities, while often overlooked, are directly impacted by the consequences of freight activities and also represent a crucial social aspect of Urban Governance. They are concerned with noise, air pollution, safety, and the quality of life in their neighborhoods. In some cases, residents may also be e-customers, positioning them as both beneficiaries and affected parties of the same logistics flows, depending on context and time of day. Their support is crucial for the social legitimacy of logistics-related urban policies. Depending on their level of awareness and involvement, residents may support these restrictions when environmental and social benefits become visible and tangible, particularly in terms of reduced traffic, noise, and pollution [29].

The recent literature on urban access control policies highlights several challenges related to their effectiveness and sustainability. In particular, Ma and Mészáros [30] identify five “paradoxes” that can arise during policy implementation: the emission reduction paradox, driven by the overall increase in vehicle numbers; the spillover effect, which shifts traffic to peripheral areas raising equity concerns; the agglomeration effect, caused by insufficient public transport capacity; socioeconomic disparities in the impact of restrictions; and the temporal and spatial limitations of regulations. These findings emphasize the need for integrated policy design and multi-stakeholder engagement to avoid unintended consequences and enhance social acceptance of urban access regulations.

2.3. Simulation Approaches in Urban Logistics

Several simulation approaches exist in the literature [27]; among them, three main categories can be identified. There are three primary approaches to simulations used in this domain: discrete event simulation, agent-based simulation, and SD. Discrete event simulation models the logistics system as a series of discrete events that occur over time. Each event represents a change in the system’s state, such as the arrival of a vehicle, the loading or unloading of goods, or the completion of a delivery. This type of simulation is particularly useful for analyzing complex systems where events occur non-continuously and for studying the interactions between different logistics components, such as warehouses [29], unloading infrastructure [26], vehicles, and distribution centers, also integrating material elements equipped with sensors and immaterial elements, as in the case of digital twins [31]. It helps in identifying bottlenecks, optimizing resource allocation, and improving process efficiency. This optimization and efficiency improvement directly supports the Industry 5.0 pillar of resilience, enabling urban logistics systems to better withstand disruptions and adapt to changing conditions. Agent-based simulation models the system through the interaction of autonomous agents, each with its own behaviors and objectives. Agents can represent various elements of the logistics chain, such as vehicles, operators, customers, or even infrastructure. This approach is useful for studying emergent behavior resulting from the interaction of multiple agents and for evaluating the impact of logistics management policies on both local and global scales. For example, it can be used to simulate the behavior of truck drivers in response to different traffic policies, toll charges or decision processes of the different nodes in a supply chain [32]. SD is a methodology used to study and manage complex interactions characterized by feedback loops and time delays, commonly found in business and social contexts. It relies on computer simulation to explore how system structure influences behavior over time [33,34]. In the context of logistics, SD simulation models the dynamic interactions among system components using stock-and-flow diagrams, helping to reveal how feedback mechanisms and delays shape overall system behavior. This type of simulation is particularly valuable for strategic planning and policy analysis, as it helps in identifying leverage points and predicting the long-term impact of different logistics strategies, but also for companies in order to improve activities, like as supply chain management [35]. SD is ideal for examining how changes in one part of the system can ripple through and affect the whole, making it a powerful tool for addressing systemic issues in logistics. It is especially useful for understanding the broader implications of policy decisions and for developing sustainable logistics practices. The long-term impact assessments and development of sustainable logistics practices through SD directly contribute to the sustainability pillar of Industry 5.0, promoting environmentally conscious urban freight management. Studying transportation and urban logistics with an SD approach is crucial for a comprehensive understanding of complex urban logistics systems. It allows researchers to model various factors like vehicle fleets, traffic flow, and access management, revealing dynamic system behaviors over time. Moreover, this approach enables long-term impact assessments of policy interventions and logistical strategies, aiding decision-makers in formulating effective urban logistics policies. Additionally, these models help identify synergies and trade-offs between policy objectives, enabling policymakers to make informed decisions that balance competing priorities. Furthermore, delving into causal loops within the SD models provides valuable insights into the interconnectedness of variables, aiding in understanding the underlying causes of system behaviors and identifying leverage points for policy interventions [36]. An example of this is the SD model that examines the intricate relationship between logistics strategies and freight transport [37]. This paper uses an SD approach to model the interactions between urban logistics and access policies through a Causal Loop Diagram. This framework captures feedback loops among fleet composition, regulations, logistics efficiency, and environmental effects, providing a basis for future simulations. Our specific contribution lies in highlighting how dynamic policy adjustments and technological integrations can optimize urban freight flows while balancing environmental and operational goals. This focus on balancing environmental and operational goals, along with enabling data-driven decision-making through integrated real-time data and advanced technologies for sustainable and adaptive urban freight management, clearly aligns with the human-centric and resilient dimensions of Industry 5.0, aiming for a better quality of life for citizens and more adaptable urban systems. This approach supports data-driven decision-making by integrating real-time data and advanced technologies for sustainable and adaptive urban freight management.

3. Materials and Methods

This section outlines the methodology employed for the study, beginning with the definition of the conceptual framework and system boundaries. It proceeds with the presentation and analysis of the causal loop diagram (CLD), detailing the classification and validation of its links.

3.1. Conceptual Framework: Defining System Boundaries and Variable Classes for Dynamic Analysis

Based on insights drawn from the existing body of literature, a model boundary chart was created to define the key parameters under consideration [34]. A total of 51 parameters were identified as relevant to the exploration of SUL and urban access management. These parameters were subsequently categorized into four groups: endogenous variables, which both influence and are influenced by other variables within the system; exogenous variables, which affect endogenous variables without being affected in return; excluded variables, which are not currently integrated into the system dynamics model; and 2030 Agenda outcomes, which represent long-term sustainability objectives aligned with the Sustainable Development Goals (SDGs), as summarized in

Table 1. Model boundary chart.

Category	Variables
Endogenous variables	Portion of alternative vehicles, Use of alternative vehicles, Use of traditional trucks, Number of deliveries, Delivery pressure, Traffic and congestion, Road infrastructure utilization, Road capacity, Number of accesses, Time to circulate, Time to park, Level of consumption of parking areas, Optimization of urban spaces, Road safety, Air pollution, Fossil fuel consumption, Noise pollution, Quality of urban life, Total shipments, Number of alternative/traditional shipments, Number of kilometers traveled by traditional/alternative vehicles, Alternative/Traditional transport costs, Recharge cost, Refueling cost.
Exogenous variables	UVAR, Incentives for the use of electric vehicles, Incentives for the use of human-powered or assisted vehicles, Restrictions on parking time, Dynamic access closing/opening, Dynamic management of parking areas, Dynamic closing/opening of parking areas, Level of route optimization.
Excluded variables	Logistic concept, Outsourcing, Delocalization, Delivery failure rate, Difference between online and in-store orders, E-commerce trends, Consumer behavior, Labor regulations, Fuel policies, Technological innovation rate, Logistical service quality, Weather conditions.
2030 Agenda Outcomes	SDG 7: Affordable and Clean Energy, SDG 9: Industry, Innovation and Infrastructure, SDG 11: Sustainable Cities and Communities, SDG 15: Life on Land.

. The parameters classification was carried out through a comprehensive literature review and expert consultation, constituting the initial step of the classification process, while further validation steps are planned for future work.

Once the model boundaries were established, the causal loop diagram (CLD) was developed. The CLD highlights the primary feedback loops and provides a simplified visualization of the system’s structure. The initial phase of the analysis involved mapping the interactions within the system using a systems dynamics perspective and building the corresponding causal loop diagram. The following section will explore the CLD in greater depth, moving from a general overview to a more detailed examination. The CLD (Figure 1) is structured into three distinct areas, each delineating crucial aspects of the study’s focus. The variables highlighted in red in Figure 1 represent enabling technologies that play a crucial role in shaping the dynamics of urban logistics and access management. Their inclusion emphasizes the impact of technological advancements on the system’s behavior, facilitating more adaptive and efficient logistics processes.

At the center lies the box titled “Company and urban interactions” which serves as the nexus for understanding the dynamics between corporate entities and the urban environment. This main area encapsulates the intricate interplay between businesses, logistics operations, and the broader urban landscape, shedding light on how these interactions influence the flow of goods and services within the city. Moving to the upper section, we encounter the box labeled “Access management measures”, where a comprehensive array of access management measures is explored, including both restrictive and supportive interventions. Among the restrictive measures are UVAR, restriction on time for parking and Dynamic management of parking areas designed to regulate and optimize traffic flow and parking within urban areas. Complementing these restrictive measures are supportive interventions such as “Incentives for the use of electric vehicles” and “Incentives for the use of HP or AV” (i.e., cargo bike). These supportive measures aim to incentivize the adoption of environmentally friendly transportation modes, contributing to a more sustainable urban mobility landscape. In several points of the diagram, for simplicity, reference will be made to both electric vehicles and HP or AV, defining them as “alternative vehicles”. The third box “2030 Agenda SDGs” relates different types of Access Management measures, through interactions with the urban and corporate system, to some of the 2030 Agenda Goals, particularly to “Ensure access to affordable, reliable, sustainable and modern energy for all” (SDG 7—Clean and Affordable Energy), “Build resilient infrastructure, promote inclusive and sustainable industrialization, and foster innovation” (SDG 9 —Industry, Innovation, and Infrastructure), “Make cities and human settlements inclusive, safe, resilient, and sustainable” (SDG 11—Sustainable Cities and Communities), and “Protect, restore, and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, halt and reverse land degradation, and halt biodiversity loss” (SDG 15—Life on Land).

3.2. Methodology for Causal Link Classification and Future Validation

The causal links depicted in the CLD (Figure 1) represent the hypothesized influence of one variable on another. The classification of these links as either positive (+) or negative (−) is grounded in a combination of established theoretical frameworks, empirical evidence from the existing literature on urban logistics and transport engineering, and logical deduction derived from system behavior. For instance, a positive link, such as ‘Increased number of traditional vehicles → Increased traffic congestion’, signifies that an increase in the former leads to an increase in the latter, a widely acknowledged phenomenon in transportation systems. Conversely, a negative link, like ‘Increased UVAR enforcement → Decreased unauthorized vehicle access’, indicates an inverse relationship. To ensure the robustness and validity of these causal assignments, the structure and polarities of the CLD will be rigorously reviewed and refined through a systematic review of and a series of expert elicitation workshops with urban logistics managers and transportation planners.

4. Results and Discussion

The proposed model, which explores the interrelationships among key factors of Smart Urban Logistics and urban access management, aims to offer a useful basis for reflection on potential implications and policy actions. While still at an early stage, it contributes an analytical framework to support understanding of how urban access strategies may affect fleet composition and the broader dynamics of urban logistics in an integrated perspective. Entering into the detail of the CLD, a crucial variable in the model is the Portion of alternative vehicles, defined as the percentage ratio between the capacity of alternative vehicles used (such as electric vehicles and HP vehicles) compared to the total capacity of the fleet vehicles. The Portion of alternative vehicles is directly influenced by three out of four measures studied (UVAR and incentives for alternative vehicles) and is fundamental for analyzing modal shift phenomena. The availability of vehicles and their potential capacities also affects the Portion of alternative vehicles, influenced by both market factors and company constraints. Understanding how these factors interact is essential for optimizing the Portion of alternative vehicles and promoting the adoption of alternative and sustainable transportation options. The Portion of alternative vehicles is linked to the cost of the alternative vehicles and to the level of use of traditional vehicles. Additionally, it can be observed that the availability of vehicles with higher capacity in the market may accelerate modal shift, leading to a further increase in the presence of alternative vehicles compared to the total, but over time, this will result in a slowdown in the Portion of alternative vehicles according to a limit which is the total fleet.

The presence of UVAR measures affects the fleet composition, and so the Portion of alternative vehicles, by reducing the use of traditional vehicles, thereby encouraging the use of alternative vehicles that could be acquired, outsourced for delivery services, or replaced with alternative vehicles. The literature identifies several types of UVAR, including emission-based regulations, vehicle type and dimension regulations, trip purpose regulations, scheme timescales, permit regulations, and other regulations. However, these various measures can be broadly categorized into two main perspectives: spatial and temporal. Spatial regulations limit access to specific areas, while temporal regulations restrict access during certain time periods, or a combination of both. In either case, the overall fleet capacity (the capacity of traditional vehicles plus the capacity of alternative vehicles) is impacted. Restrictions on spatial access diminish the capacity available for conventional vehicles, making it necessary to increase the use of alternative modes to sustain the same overall logistical performance. On the other hand, temporal access limitations reduce capacity to zero during the restricted periods. The presence of incentives for acquiring alternative vehicles is positively correlated with Portion of alternative vehicles, potentially acting as a “multiplier” for the capacity of alternative vehicles. This is because, thanks to the incentives, a new capacity can be acquired at a lower cost, or even halved, depending on the value of the incentives themselves. It is advisable to keep the two types of incentives separate because the distinct types of vehicles (whether electric or HP) impact the variables of interest in different ways. For example, electric vehicles have an impact on emissions but not on traffic.

4.1. Loop 1 Energy Loop

Another important part of the CLD is the Loop 1—“Energy Loop” (Figure 2) illustrates a crucial negative feedback dynamic in the adoption of alternative vehicles (Balancing Loop). An initial increase in the “Portion of alternative vehicles” triggers a proportional growth in the “Use of alternative vehicles” and the subsequent “N. of alternative shipments” leading to an increase in the “N. of km traveled by alternative vehicles”. This rise in utilization directly translates into higher “Recharge cost,” elevating the overall “Alternative transport costs”. Significantly, the increase in these costs acts as a balancing force, exerting a negative pressure on the “Portion of alternative vehicles”. This self-regulating cycle suggests that, in the absence of external interventions, a massive adoption of alternative vehicles could self-limit due to the increased operating costs associated with recharging and, potentially, the “Level of route optimization”. Inefficient route optimization could, in fact, exacerbate the kilometers traveled and, consequently, the recharging costs, further strengthening the balancing dynamic of the loop.

4.2. Loop 2 Transport Emissions

The Loop 2 “Transport emissions” (Figure 3) outlines a negative feedback mechanism aimed at controlling emissions from traditional transportation (Balancing Loop).

If the Portion of alternative vehicles is low, the use of traditional vehicles increases, leading to a rise in the number of shipments conducted by conventional means. This results in a greater number of kilometers traveled by such vehicles, causing an increase in fossil fuel consumption and, consequently, in transport emissions. The rise in emissions can negatively affect traditional transport costs (because emissions are internalized), making it less competitive compared to more sustainable solutions. Over time, this effect may encourage the gradual adoption of alternative vehicles, closing the balancing loop. It describes a self-regulating dynamic, where the initial prevalence of traditional vehicles triggers economic and environmental effects that, in the medium to long term, push the logistics system toward rebalancing through the integration of more sustainable technologies. It is important to note that the overall level of fossil fuel consumption also depends on the efficiency of the vehicles used, while the capacity of traditional vehicles influences the total number of shipments required. These elements significantly impact the environmental footprint of the system and should be considered when analyzing urban access policies and sustainable logistics strategies.

4.3. Loop 3 Fuel Cost

The Loop 3 “Fuel cost” (Figure 4) highlights a negative feedback mechanism driven by the operating costs of traditional transportation (Balancing Loop). An increase in the use of traditional trucks raises the “N. of traditional shipments “ and the “N. of kilometers traveled by traditional vehicles” leading to greater “Fossil consumption” and, consequently, an increase in the “Refueling cost”. This rise in refueling costs translates into higher “Traditional transport costs” which, with a time delay, can incentivize a larger “Portion of alternative vehicles” by making lower-fuel-consumption or alternative-powered options more economically advantageous. However, the strength of this balancing loop is closely tied to the “Cost of fuel” and the “Efficiency of traditional vehicles”. High fuel prices amplify the impact of increased consumption on costs, potentially accelerating the transition to alternatives. Conversely, improvements in the efficiency of traditional vehicles can weaken this loop by reducing the sensitivity of operating costs to increased utilization. Furthermore, the “Capacity of traditional vehicles” plays a crucial role, as greater capacity can reduce the number of trips required, mitigating the increase in fuel consumption and total costs. Therefore, the effectiveness of this loop in promoting the adoption of alternative vehicles depends on a complex interplay between economic, technological, and logistical factors.

The analysis of CLD highlights the complex interrelationships influencing air and noise pollution stemming from transportation, and the quality of urban life. Transport emissions, directly correlated with the fossil fuel consumption of traditional vehicles, represent a primary source of air pollution.

A key strategy to mitigate these negative impacts lies in increasing the proportion of alternative vehicles, supported by UVAR, and incentives, which can significantly reduce the use of internal combustion engine vehicles and, consequently, air emissions. As explicitly indicated in the diagram, the number of kilometers traveled by traditional vehicles directly contributes to noise pollution due to the inherently higher noise levels of combustion engines. Although noise pollution is also influenced by traffic and congestion, the reduction in the use of traditional vehicles and the promotion of quieter alternatives represent key strategies to mitigate its impact.

Ultimately, the quality of urban life emerges as a synthetic variable, negatively influenced by both air and noise pollution, but potentially improvable through policies that promote sustainable mobility and the optimization of urban spaces.

4.4. Loop 4 Congestion and Loop 5 Demand Trigger

The Loop 4 “Congestion brake” (Figure 5) is a balancing loop that activates in response to an increase in the variable “Traffic and congestion”. When the number of vehicles attempting to access a specific urban area exceeds the optimal capacity, congestion inevitably increases. Recognizing this situation, the “Dynamic access closing” system intervenes to reduce the vehicular entry capacity. This dynamic management can manifest through various concrete strategies, such as temporarily reducing the duration of green lights at inbound traffic signals, temporarily closing certain access lanes, or even creating temporary LTZs that restrict access to certain categories of vehicles or all unauthorized vehicles. The direct consequence of this dynamic closure is a decrease in the variable “Number of accesses” which is the number of vehicles that can effectively access the congested area per unit of time. A lower inflow of vehicles leads to a reduction in the “Road infrastructure utilization” at that specific point. With fewer vehicles in motion, the result is a decrease in the variable “Traffic and congestion” completing the negative feedback loop that tends to bring the system back towards an equilibrium with less congestion.

The Loop 5 “Demand trigger” is a reinforcing loop triggered in the presence of low levels of “Traffic and congestion”. In these fluid conditions, the “Dynamic access opening” system can react by easing entry restrictions. This can occur by increasing the duration of green lights at inbound traffic signals, reopening previously closed lanes, or suspending access limitations such as LTZs. The increased access capacity leads to an improved “Perceived ease of access/travel time” by road users. This positive perception incentivizes an increase in the “Demand for access” to the area: more people are willing to use their vehicles to reach the destination, knowing that traffic is smooth and access is facilitated. The increase in demand translates into a greater “Number of accesses” of vehicles entering. A larger number of accessing vehicles leads to increased “Road infrastructure utilization”. If this utilization approaches or exceeds the optimal capacity of the infrastructure, an increase in the variable “Traffic and congestion” occurs, closing the positive feedback loop that amplifies the initial trend, in this case potentially towards greater congestion in the long term.

The synergistic yet opposing interaction of these two loops is fundamental to urban traffic dynamics. The effectiveness of dynamic access management critically depends on the precise calibration of activation thresholds for closing and opening measures, the intensity with which these measures are implemented, and the intrinsic delays within the system. Poorly defined thresholds, disproportionate responses, and significant delays can compromise the goal of optimizing traffic flow. Prudent management aims to leverage the stabilizing capacity of Loop 4 to contain congestion, while preventing Loop 5 from triggering an uncontrolled increase in demand and traffic, seeking a dynamic equilibrium that promotes sustainable urban mobility.

The analysis of the conceptual model reveals two primary dynamics influencing urban logistics efficiency: a negative reinforcing loop, the Escalation of congestion from restrictions (Loop 6), and a balancing loop, the Balancing of dynamic access (Loop 7) (Figure 6).

4.5. Loop 6 Escalation of Congestion and Loop 7 Balancing of Dynamic Access

The Loop 6 Escalation of Congestion from Restrictions explores the consequences of temporal parking restrictions, highlighting how a reduction in available delivery time can trigger a negative spiral. The increasing pressure to maximize the number of deliveries within a limited period can lead, in the absence of adequate business efficiency improvements, to an increase in circulation time and congestion, further increasing the time spent parking and reducing the effective delivery time. However, it is crucial to consider that this pressure induced by restrictions can also push companies to adopt more efficient operational strategies (route optimization, faster loading/unloading), potentially mitigating the negative impacts on traffic.

In contrast, the Loop 7 Balancing of dynamic access describes a balancing mechanism centered on the implementation of dynamic access measures to parking areas. Effective dynamic management aims to reduce the time spent parking, thereby increasing the effective time available for delivery operations. This increase in operational time can potentially translate into a greater number of deliveries and, in the long term, a reduction in the overall circulation time of commercial vehicles and urban congestion. The loop self-regulates as increased congestion can stimulate the adoption of further dynamic parking management measures, creating positive feedback towards a more efficient and less congested logistics system. The interaction between these two loops suggests that the system’s response to restrictions is complex and depends on the ability of companies to innovate their operations, while targeted interventions in parking management offer significant potential to improve urban fluidity.

4.6. Loop 8 Dynamic Openings, Loop 9 Dynamic Closing and Loop 10 Urban Regeneration

The analysis of dynamic parking management cycles (Figure 7) reveals contrasting dynamics. Loop 8 “Dynamic openings” is configured as a balancing loop. In this mechanism, an increase in the parking occupancy level (or the “Level of consumption of parking areas,” as defined in the context of space optimization, indicating increased pressure on the parking resource) triggers a dynamic opening of new spaces to meet demand. This mechanism aims to bring occupancy back to a desired level, acting as a regulator. Its balancing action is crucial for preventing saturation and maintaining availability. Conversely, the Loop 9 Dynamic closing operates as a negative reinforcing loop, where a low occupancy level leads to the reduction in parking spaces, potentially increasing the utilization rate of the remaining ones but risking a spiral of supply contraction if demand does not adjust. Crucially, the Loop 10 Urban Regeneration represents a long-term reinforcing loop that pushes towards the optimization of underutilized urban spaces, including parking lots, converting them for more efficient uses. While Loop 10 “Urban Regeneration” strongly propels a continuous optimization of urban spaces through the strategic reallocation of parking areas to alternative functions, such as pedestrian zones or green spaces, it is crucial to recognize that even such a reinforcing process cannot proceed indefinitely. Its positive momentum inevitably encounters concrete limitations. The availability of suitable and truly redundant parking spaces for conversion is finite, and an excessive, unbalanced reduction could compromise essential mobility and accessibility for residents and businesses, creating problems rather than solving them. Furthermore, the conversion of these spaces into green or pedestrian areas (with associated urban furniture, drainage, lighting, etc.) involves significant design, construction, and subsequent maintenance costs, imposing economic constraints. Lastly, and perhaps most critically, the removal of parking often faces considerable social and political resistance from those who depend on it, effectively limiting the scope and speed of such transformations. All these factors act as balancing forces that, once they emerge, tend to slow down or even redefine the boundaries of the regeneration process, pushing it towards a new and more complex equilibrium between spatial efficiency and socio-economic needs.

Effective dynamic management requires a thorough analysis of parking demand, aiming for an optimal occupancy level. The introduction of dynamic pricing can function as a mechanism for regulating demand flow, incentivizing the use of less saturated parking and discouraging concentration in specific areas. Simultaneously, dynamic opening and closing decisions should be informed not only by current occupancy levels but also by demand forecasts and long-term urban space optimization goals. From this perspective, the closure of low-demand parking areas should not be seen as a simple reduction in supply but as a strategic opportunity for the temporary or permanent reallocation of space for more priority uses, contributing to a broader vision of urban efficiency and liveability.

4.7. Access Management, Urban Logistics, and Sustainable Development Goals

Considering the interconnectedness of access management strategies and urban logistics, several variables align with the objectives of the United Nations’ 2030 Agenda (

Table 2. Relationships between key variables of UVAR and SUL and selected SDGs.

SDG Objective	Connection with UVAR and SUL
SDG 7 Affordable and Clean Energy	The Portion of alternative vehicles (shaped by UVAR, Incentives for the use of electric vehicles or HP or AV, and Availability of alternative vehicles) leads to lower Fossil consumption and fewer Transport emissions, supporting cleaner energy use.
SDG 9 Industry, Innovation and Infrastructure	Road infrastructure utilization (affected by Road capacity) and Road safety are improved through Dynamic access closing/opening and route optimization, fostering efficient and innovative urban logistics systems.
SDG 11 Sustainable Cities and Communities	Quality of urban life improves through lower Air pollution and Noise pollution due to the increased use of Alternative vehicles. Road safety also improves as a result of the Optimization of urban spaces enabled by better Parking area management and Space allocation.
SDG 15 Life on Land	Reduced Air pollution and fewer Kilometers traveled by traditional vehicles mitigate environmental degradation, supporting the protection of urban and natural ecosystems.

). The uptake of alternative vehicles, for example, directly supports SDG 7, advancing efforts towards accessible, safe, sustainable, and affordable energy. The quality of urban life, road safety, and the optimization of urban spaces are critical factors relevant to SDG 11. Access management interventions that foster more liveable, secure, and inclusive urban settings can bolster social cohesion, equity, and the long-term viability of urban communities. Air pollution, frequently stemming from city vehicle traffic, has a clear connection to SDG 15. Mitigating air pollution through access management can aid in preserving air quality and safeguarding urban and natural ecosystems, thereby contributing to the sustainable stewardship of land resources and biodiversity conservation. Lastly, road safety and urban space optimization can be linked to SDG 9. Implementing access management policies aimed at enhancing road safety and improving infrastructure utilization can promote the sustainability and resilience of cities, as well as encourage innovation within the transportation and urban mobility sectors. A fundamental aspect is therefore fleet composition, indicated by the proportion of alternative vehicles in relation to overall fleet capacity. This proportion is directly shaped by UVAR measures and incentives for adopting cleaner vehicles, and it is key to understanding shifts in transportation modes. Vehicle availability also influences this proportion, affected by market dynamics and company limitations. Grasping these interactions is vital for refining fleet composition and encouraging the adoption of alternative and sustainable transportation options.

Furthermore, this viewpoint emphasizes the interdependencies among various elements, such as vehicle carrying capacity, and their influence on logistical efficiency and the urban environment. This deeper comprehension of SDGs empowers decision-makers to evaluate the efficacy of public policies more precisely and to pinpoint potential synergies or trade-offs between diverse objectives, like diminishing air pollution, improving road safety, and streamlining urban space. Ultimately, this approach can serve as a valuable instrument for informing policy formulation and supporting the development of more impactful and enduring public policies within the realm of urban logistics and access management. As an advantage, this framework enables the analysis of combined measures in a mutually reinforcing and effective manner. This integrative capacity could allow decision-makers to craft more holistic and consistent strategies to enhance urban logistics and urban access management. For example, UVAR policies can be combined with incentives for transitioning to alternative vehicles to gauge impacts on emissions, while smart city management and vehicle technologies, utilizing real-time data, connectivity, and automation, can be coordinated with the introduction of electric vehicles to assess effects on traffic flow or road safety. Moreover, the constructive collaboration of various measures can boost the overall effectiveness of urban logistics and contribute to achieving goals related to sustainability and urban quality of life.

A forward-thinking approach to urban planning and mobility, centered on human well-being, necessitates leveraging smart city management and vehicle technologies to foster sustainable urban regeneration—economically, ecologically, and socially. This involves a change in basic assumptions towards prioritizing people and the environment in urban design and policy. Integrating real-time data from connected vehicles and infrastructure allows for dynamic adaptation of urban spaces and mobility systems, optimizing traffic flow, reducing emissions, and enhancing accessibility for all citizens. This includes the intelligent management of parking, as discussed previously, but extends to the broader allocation of urban resources, prioritizing green spaces, pedestrian and cycling infrastructure, and efficient public transport. The goal is to create cities where logistics and transportation systems seamlessly integrate with the needs of residents, businesses, and the environment, fostering economic vitality through efficient goods movement while simultaneously improving air quality, reducing noise pollution, and enhancing social equity through inclusive mobility options.

4.8. Limitations and Future Perspectives

While this analytical lens offers significant insights into the complexities of SUL and urban access management, acknowledging its limitations and areas for future refinement is important. One constraint lies in the inherent complexity of urban settings, involving numerous interacting factors that this perspective may not fully encompass. For instance, socio-economic factors, cultural nuances, and local regulations could affect the success of policies and strategies concerning fleet makeup and access management. Furthermore, this framework might not capture all potential feedback loops and nonlinear relationships within urban systems, potentially leading to some simplification of complex dynamics, particularly given the lack of distinction between deliveries to businesses and deliveries to end consumers (last-mile logistics). Additionally, its predictive power may be constrained by uncertainties in data availability and future trends, especially in rapidly evolving urban areas. For future advancement, efforts could focus on enhancing the accuracy and predictive capabilities of this approach by integrating more granular data. This could involve conducting empirical studies to validate its underlying assumptions and calibrate its parameters based on real-world observations. Moreover, a quantitative stock and flow model will be developed to assess the impacts of diverse measures. This model will be specifically engineered to overcome the limitations of the current analytical lens by providing a more rigorous and predictive tool for assessing the dynamic impacts of Smart Urban Logistics (SUL) and access management strategies.

The proposed quantitative model will systematically translate the intricate interdependencies identified in the CLD into a robust system of quantifiable stocks and flows, structured around several interconnected sub-models. Initially, the model’s development could strategically concentrate on a specific segment of the CLD, particularly the sub-model focused on optimizing fleet composition between traditional and alternative vehicles. This targeted approach would allow for an in-depth analysis of how restrictive measures and incentives can be effectively balanced to achieve desired fleet transformation.

For fleet dynamics, the model will integrate stocks representing the number of traditional and alternative vehicles, with corresponding flows capturing vehicle acquisition, retirement, and conversion rates. These flows will be dynamically influenced by factors such as incentives for the adoption of electric, human-powered, or assisted vehicles. The essential data required for this sub-model includes the number and type of both traditional and alternative vehicles, their average fuel or energy consumption, carrying capacity, acquisition and maintenance costs, and operational ranges.

The delivery operations sub-model will encompass stocks such as the number of deliveries and total shipments, modulated by flows representing delivery rates. These rates will be dynamically influenced by delivery pressure and effective delivery time, with explicit linkages to the utilization of alternative and traditional vehicle types. Data acquisition for this component will necessitate daily delivery counts, average delivery times, vehicle load factors, typical route lengths, and the frequency of access to specific urban zones. Future iterations of this sub-model will aim to differentiate between Business-to-Business (B2B) and Business-to-Consumer (B2C) last-mile deliveries for a more granular analysis.

In terms of urban congestion and access management, the model will incorporate key stocks such as traffic and congestion levels and the consumption rate of parking areas. Flows within this sub-model will represent traffic generation, vehicle circulation, and parking utilization, directly impacted by Urban Vehicle Access Regulations (UVARs), time restrictions for parking, dynamic management of parking areas (including dynamic opening and closing mechanisms), and dynamic access opening and closing. The crucial variable of “Time to circulate” will be included to explicitly link congestion back to overall delivery efficiency. Data requirements for this segment will cover traffic volumes, average speeds, congestion levels stratified by time and location, parking occupancy rates, and enforcement data pertinent to UVARs.

Finally, the environmental and societal impact sub-model will quantify outcomes such as transport emissions, air pollution levels, noise pollution, and the broader quality of urban life. Flows within this section will be calculated based on fossil fuel consumption, the number of kilometers traveled by traditional vehicles, and the volume of alternative shipments, directly assessing the attainment of relevant 2030 Agenda Sustainable Development Goals (SDGs), specifically SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). The essential data for this part includes local air quality indices (e.g., PM2.5, NOx), observed noise levels, and Greenhouse Gas (GHG) emission factors for various vehicle types. Furthermore, this framework could be broadened to incorporate wider socio-economic ramifications—such as job creation, economic growth, and social equity—in order to provide a more thorough understanding of the implications of urban logistics policies and access management. Additionally, future work could integrate this viewpoint with other types of interventions affecting city logistics, such as the establishment of consolidation centers.

5. Conclusions

In summary, the proposed approach offers a comprehensive perspective for understanding the complex interactions between Smart Urban Logistics (SUL) and access management, providing fundamental insights for informed policymaking and the development of effective strategies. By dissecting the relationships between elements such as UVAR policies, fleet composition, and logistics productivity, this perspective equips decision-makers to refine urban logistics strategies with greater accuracy. Its inherent integrative capacity enables the identification of beneficial synergies among diverse measures, thereby improving the overall efficiency of urban freight movement and advancing sustainability objectives. While acknowledging certain inherent limitations, including potential simplifications of intricate urban dynamics and dependencies on data availability, the positive contribution of this methodology is significant. Looking ahead, potential enhancements include the integration of real-time data, the examination of wider socio-economic impacts, and the incorporation of additional indicators related to urban logistics. These developments could significantly improve the model’s accuracy and real-world relevance. This study provides a conceptual basis for advancing toward a more refined simulation framework, supporting the design of sustainable and effective urban logistics and access management strategies. It highlights how simulation-based approaches can help address the complex challenges of urban freight, offering valuable guidance for policymakers aiming to implement well-informed and efficient solutions. Firstly, the crucial role of optimized fleet composition is highlighted, where the integration of traditional vehicles with alternative fuel options, including hybrid and autonomous vehicles, is paramount for reducing emissions, alleviating congestion, and enhancing overall operational efficiency within urban logistics. Secondly, a synergistic improvement in urban space management and dynamic parking solutions is crucial; this involves strategically pairing restrictive measures and incentives with initiatives aimed at optimizing urban space utilization. The dynamic parking solutions offer a pathway to enhance delivery efficiency and optimize the utilization of parking areas by reducing congestion, which can, in turn, liberate valuable urban space for alternative uses that enhance the quality of life, such as green areas and pedestrian zones, contributing to a more people-centric urban environment. The integration of Internet of Things (IoT) sensors into road infrastructure is also key, facilitating real-time data collection and monitoring that offers critical insights into traffic flow, emissions, and infrastructure usage. A data-driven approach to urban logistics enables predictive planning, more accurate assessments of policy impacts, and responsive adaptation to dynamic urban conditions. Crucially, technologies such as route optimization algorithms, dynamic access management platforms, and smart parking management systems serve as vital instruments for enhancing operational responsiveness and efficiency. These tools operate within the Industry 4.0 framework, enabled by foundational technologies including IoT, big data analytics, cloud computing, cyber-physical systems, artificial intelligence, and advanced human–machine interfaces. Integrating them into urban freight transport fosters the creation of intelligent and adaptive logistics models, capable of real-time adjustment to urban conditions and user needs. Equally important is the engagement with private logistics operators and technology providers through public–private collaboration frameworks. Incentivizing the adoption of IoT devices and intelligent routing systems on commercial vehicles not only improves fleet tracking and performance monitoring but also fosters data sharing that can inform collective decision-making and support the development of intelligent logistics networks. Finally, aligning with the principles of Industry 5.0, this approach moves beyond automation and efficiency to embrace a more human-centric, resilient, and sustainable vision of urban logistics. This highlights the critical need to align technological innovation with social and environmental objectives, ensuring digital transformation serves the greater public good. Fostering robust partnerships between public authorities and private stakeholders remains essential to ensure coordinated actions, shared responsibility, and the successful implementation of effective and inclusive urban logistics policies.

Future research endeavors should aim to strengthen and expand upon this study by gathering empirical data from case studies or pilot projects. Such efforts would be crucial for validating and finely tuning the model’s assumptions and parameters. Moreover, the creation of more elaborate quantitative stock and flow models would facilitate a more precise evaluation of different policies’ long-term impacts. Additional research should consider incorporating variables related to logistical service quality, fuel policies, and weather conditions, which can significantly influence urban logistics performance and resilience. Furthermore, integrating socio-economic factors like employment effects, economic development, and social equity is essential for a more thorough comprehension of urban policy outcomes. Further research could explore the integration of access management strategies with other urban logistics solutions, such as consolidation centers and innovations in last-mile deliveries, thus fostering a more holistic approach to urban logistics transformation. Finally, studying the role of emerging technologies—such as connected and autonomous vehicles, real-time data analytics, and smart infrastructure—is essential to effectively guide future sustainable mobility policies.