Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review
Abstract
:1. Introduction
- What should be the optimal extent of an LEZ at each moment in time depending on the traffic condition and quality parameters, and what would be its impact?
- What is the penalty generated by an LEZ to the individual mobility of the citizen?
- Should the topology of such an LEZ be considered depending on its impact to mobility?
- Should an LEZ be active in a certain area if the thresholds are not met?
- Can better LEZ topologies be achieved for the quality thresholds raised with less impact to the citizen?
- It provides an in-depth review of existing and current techniques proposed for implementing LEZs, focusing on the transition from static to dynamic systems.
- It categorizes LEZ strategies into static, hybrid, and dynamic systems, highlighting key case studies and enabling technologies.
- It discusses the challenges and future opportunities in LEZ implementation, emphasizing the need for data-driven and adaptive approaches.
- It offers insights and recommendations for policymakers and researchers aiming to enhance urban air quality management through more flexible and efficient LEZ strategies.
2. Dynamic Low-Emission Zones
- Static LEZs: Fixed boundaries and regulations that do not change in response to real-time conditions.
- Hybrid LEZs: A combination of static boundaries with some dynamic elements, such as time-based restrictions.
- Dynamic LEZs: Adaptive zones that adjust their boundaries and regulations based on real-time traffic and emission data.
2.1. Dynamic Low-Emission Zones
2.2. Challenges for Dynamic LEZ Implementations
2.3. LEZ and Green Urban Planning
3. Materials and Methods
3.1. Inclusion and Exclusion Criteria
3.2. Data Extraction
3.3. Study Quality Assessment
4. Results
4.1. Search Results and Study Characteristics
4.2. Pricing Strategies
4.2.1. Electronic Road Pricing System for Low-Emission Zones to Preserve Driver Privacy [18]
- Revocable Anonymity: Honest drivers retain complete anonymity, while fraudulent drivers are identified through revocation mechanisms when they fail authentication or proper payment.
- Tamper-Proof Secure Elements (SEs): Each vehicle is equipped with a secure element to perform sensitive operations, ensuring the integrity of entrance and exit proofs.
- Dynamic Fee Calculation: Prices are calculated based on vehicle category, emission levels, and time spent within the LEZ, promoting equitable and environmentally sensitive tolling.
- Fraud Control Mechanisms: Checkpoints use cryptographic proofs and take photographs only in cases of authentication failure, ensuring fairness and accuracy in fraud detection.
- Privacy Preservation: The system ensures that vehicle itineraries cannot be reconstructed, and each entrance and exit process generates unique encrypted credentials, preventing traceability.
- Scalability and Efficiency: The protocol supports real-time interaction between vehicles and checkpoints, making it practical for deployment in busy urban environments.
4.2.2. Collaborative Road Pricing Strategy for Heterogeneous Vehicles Considering Emission Constraints [27]
- Bi-Level Optimization Model: A bi-level programming framework simultaneously optimizes travel efficiency and emission reduction. The upper-level model minimizes total travel time and emissions, while the lower-level model ensures equilibrium in multi-modal transportation networks.
- Distinct Tolling Mechanisms: CVs pay both congestion and emission tolls, while NEVs only pay congestion tolls, encouraging the adoption of cleaner vehicle technologies.
- Real-World Case Study: Conducted within the Second Ring Road of Beijing, the strategy achieved a 5.04% reduction in total travel time and a 10% reduction in LEZ emissions, with overall network emissions reduced by 2.04%.
- Dynamic Impact Analysis: Explored the effects of varying NEV penetration rates and emission constraints, demonstrating that a balanced NEV penetration rate of 20–30% maximizes network efficiency while meeting emission targets.
- Collaborative Tolling Benefits: Compared to standalone congestion or emission pricing, the collaborative approach significantly improves traffic flow distribution and environmental outcomes, highlighting the synergistic benefits of integrating both pricing strategies.
4.2.3. Pollution and Congestion in Urban Areas: The Effects of Low-Emission Zones [20]
- Impact on Pollution: LEZs are particularly effective in reducing PM2.5 emissions, especially in cities with high initial pollution levels, broad coverage areas, and stringent restrictions on vehicle types. For instance, previous studies indicate average reductions in PM10 emissions of up to 13% in German cities and 33% for PM10 in Rome’s restricted areas.
- Ineffectiveness on Congestion: LEZs have a minimal impact on congestion mitigation. The renewal of car fleets with cleaner vehicles compliant with LEZ regulations may initially reduce congestion but is counterbalanced by latent demand and car substitution effects in multi-car households.
- Trade-offs in Policy Goals: The study highlights the differing goals of LEZs (curbing pollution) and urban tolls (reducing congestion), showing that while both can address externalities simultaneously, their primary objectives result in varying effectiveness.
- Support for LEZs: LEZs are generally more socially acceptable than tolls, primarily affecting older, more polluting vehicles while leaving compliant drivers unaffected. This acceptability contributes to their widespread adoption across European cities.
4.3. Technological Innovations
4.3.1. Security and Privacy in a Blockchain-Powered Access Control System for Low-Emission Zones [26]
- Decentralization of Access Control: The system replaces centralized entities with a blockchain-based distributed network, eliminating single points of failure and improving transparency in managing vehicle accesses and fee payments.
- Privacy Preservation: Unlike conventional camera-based systems, which capture indiscriminate license plate data, this approach guarantees user anonymity unless fraudulent behavior is detected. Honest drivers retain their privacy throughout the process.
- Smart Contract Implementation: Vehicle entries and payments are processed as blockchain transactions through smart contracts. This ensures automated, tamper-proof fee calculations and payments using digital currencies.
- Secure Fraud Detection: Fraudulent users can be identified and penalized without compromising the privacy of compliant users. The system provides revocable anonymity and integrity for all recorded transactions.
- Performance Validation: Extensive laboratory and real-world low-traffic street-environment testing confirmed the system’s feasibility. The smart contract’s gas consumption was analyzed, demonstrating the approach’s cost-effectiveness.
4.3.2. Time-Based Low-Emission Zones Preserving Drivers’ Privacy [22]
- User-Centric Design: The system protects the privacy of honest drivers while maintaining a robust mechanism for detecting fraud. Only vehicles suspected of fraudulent behavior are photographed at checkpoints, preserving privacy for compliant users.
- Anonymity Revocation for Fraud Detection: Dishonest users lose their anonymity when fraudulent activity is detected. The system utilizes cryptographic techniques to revoke anonymity while safeguarding honest users’ data.
- Advanced Cryptographic Protocols: The ERP system incorporates secure proof generation and verification processes. Vehicles interact with checkpoints using encrypted proofs, ensuring data integrity, non-repudiation, and limited traceability.
- Feasibility and Efficiency: Empirical evaluations demonstrate the system’s practicality, highlighting its ability to operate seamlessly in real-world scenarios with mobile checkpoints and dynamic traffic conditions.
4.3.3. Privacy-Preserving and Secure Decentralized Access-Control System for Low-Emission Zones [33]
- Decentralized Control: The system replaces centralized entities with a blockchain-supported network for managing vehicle accesses and payments, enhancing transparency and reducing the risk of privacy violations.
- Privacy Preservation: User anonymity is preserved using pseudonyms that can be renewed regularly, ensuring the non-traceability of users’ actions unless fraudulent behavior is detected.
- Blockchain and Smart Contracts: Access events are processed as blockchain transactions using smart contracts, enabling automated fee calculation and payment while ensuring data integrity.
- Non-Repudiation and Fraud Control: The system incorporates cryptographic proofs to prevent unauthorized access and ensures that dishonest users can be identified without compromising the privacy of compliant drivers.
- Scalable and Adaptable Design: Integrating onboard units (OBUs) and cryptocurrency mixing services ensures scalability, making the system adaptable for urban environments with varying traffic densities.
4.3.4. Privacy-Preserving Electronic Toll System with Dynamic Pricing for Low-Emission Zones [19]
- Dynamic Pricing: Prices for LEZ stretches are dynamically adjusted based on real-time traffic volume, ensuring efficient flow management and fair pricing for drivers based on their emission category.
- Privacy Preservation: The system guarantees the anonymity of honest drivers using revocable anonymity protocols. Vehicle itineraries are encrypted and cannot be linked to specific users unless fraud is detected.
- Fraud Detection: Fraudulent drivers are identified through digital certificates and on-board unit (OBU) verification at checkpoints. Honest drivers are protected from false accusations by cryptographically signed records.
- Scalability and Feasibility: The system is designed to function in urban environments with dynamic traffic conditions, providing robust fraud detection and privacy preservation without introducing excessive computational or infrastructural costs.
- Integration of Security Mechanisms: It incorporates tamper-proof modules and secure vehicle elements to ensure data integrity and authenticity throughout the tolling process.
4.3.5. Advancing Sustainability Impact Assessment: A Comprehensive Tool for Low-Emission Zone Management [30]
- Real-Time Monitoring and Data Integration: Utilizes IoT sensors, cameras, and communication networks to collect hyperlocal data on vehicle emissions, traffic patterns, and compliance levels within LEZs.
- Advanced Air Quality Modeling: Incorporates CHIMERE-WRF chemical models and bilinear interpolation techniques to predict pollutant dispersion at a street-level resolution, enabling targeted interventions.
- Sustainability Impact Assessment: Provides key performance indicators (KPIs) and visualization tools to evaluate the environmental and traffic impacts of LEZ scenarios in real time.
- Validated Emission Reductions: Achieved up to 86% CO2 equivalent reductions in Helsinki under optimal scenarios, demonstrating the efficacy of adaptive LEZ management strategies.
- Interactive User Interface: Includes simulation tools, heatmaps, and dashboards for defining and monitoring LEZ boundaries and optimizing policies based on contextual intelligence and real-time data.
4.4. Data and Evidence Mapping
4.4.1. Decentralized Privacy-Preserving Access for Low-Emission Zones [23]
- Decentralized Architecture: Utilizes blockchain to eliminate centralized third-party control over LEZ access and payment processes, transferring trust to a decentralized network of nodes.
- Privacy Preservation through Group Signatures: Ensures anonymity and unlinkability for honest users by leveraging group signatures, where access data are signed on behalf of vehicle emission category groups without disclosing individual identities.
- Revocable Anonymity for Fraud Control: Honest users retain complete anonymity, but fraudulent users who violate protocol can have their identities disclosed through group signature revocation.
- Integration with Smart Contracts: Automates payment and verification processes using Ethereum smart contracts, which calculate and transfer access fees in LEZ tokens, ensuring a transparent and tamper-proof system.
- Efficiency and Scalability: Designed to operate efficiently with minimal computational overhead, leveraging lightweight cryptographic operations suitable for real-time LEZ scenarios.
4.4.2. A Data-Driven Method of Traffic Emissions Mapping with Land Use Random Forest Models [25]
- High-Resolution Emission Mapping: Leveraging 272 predictors, including road features, population density, and land use variables, the model achieves a spatial generalization accuracy for traffic volume and speed simulations.
- Dynamic Temporal and Spatial Analysis: Evaluated hourly and daily CO, HC, NOx, PM2.5, and CO2 emission patterns, revealing significant variations during peak and off-peak traffic periods.
- Drivers of Spatial Heterogeneity: Nonlinear relationships were identified between emissions and urban features, such as population density and proximity to logistical centers. For instance, freight activity predominantly influences NOx emissions, while CO2 emissions are higher near Central Business Districts (CBDs).
- Scenario Evaluation for LEZ Policies: It conducted fine-grained assessments of emission reductions under two scenarios: (i) traffic demand management, exemplified by COVID-19 restrictions, achieving up to 66% CO2 reductions; and (ii) fleet electrification, resulting in a 31% CO2 reduction and significant reductions in NOx and PM2.5 emissions.
- Scalability and Policy Applications:The LURF model’s computational efficiency and adaptability make it suitable for real-time traffic data integration, supporting LEZ strategies in global megacities.
4.5. Optimization and Simulation
4.5.1. An Optimization-Based Approach to Designing Urban Low-Emission Zones [29]
- Integrated Optimization Framework: The model simultaneously determines optimal LEZ boundaries and parking facility locations for modal shifts, balancing user costs and traffic network performance.
- Hexagonal Zoning System: The city is divided into small, identical hexagonal zones, enabling precise configuration and evaluation of potential LEZ layouts.
- Multi-Criteria Objective Function: It optimizes generalized user travel costs and link saturation while ensuring accessibility for all Origin–Destination (OD) pairs.
- Case Study Validation: Tested on a simplified urban network with four feasible configurations, the model demonstrated significant potential for balancing LEZ area expansion with minimal travel cost increases and reduced network congestion.
- Efficient Computational Performance: The MILP approach solved each scenario in under 20 s, showcasing its practicality for real-world applications.
4.5.2. Optimized Design of Low-Emission Zones in SUMO: A Dual Focus on Emissions Reduction and Travel Time Improvement [24]
- Dynamic Configuration: Introduces an optimization model for LEZs that adjusts boundaries dynamically based on real-time data, including emission levels and traffic congestion.
- Genetic Algorithm for Optimization: Employs genetic algorithms to select optimal LEZ edges by minimizing emission and travel times, ensuring topological connectivity, and maintaining geographical constraints.
- Experimental Validation: Simulations were conducted in Guadalajara, Spain, to validate the approach, demonstrating a 35% reduction in CO2 emissions and a 15% improvement in travel times compared to traditional static LEZs.
- Comprehensive Data Collection: Utilizes SUMO’s traffic simulation capabilities to collect and process extensive datasets, enabling precise modeling of traffic and emission dynamics.
- Policy Implications: Highlights the feasibility of integrating optimized LEZ designs into urban planning to achieve sustainable mobility goals.
4.6. Access-Control Systems
4.6.1. Secure and Privacy-Preserving Lightweight Access-Control System for Low-Emission Zones [32]
- Privacy Preservation: The system ensures that honest drivers maintain their anonymity. Cryptographic protocols guarantee that only non-compliant drivers are identified, addressing privacy concerns raised by current intrusive LEZ systems.
- Low-Cost Infrastructure: Using lightweight cryptographic protocols, the system operates effectively on single-board computers, reducing implementation and maintenance costs.
- Smartphone Integration: Drivers interact with the system using their smartphones, which serve as both the identification tool and access validator, removing the need for dedicated vehicle hardware.
- Real-Time Data Collection: The system collects metadata on vehicle entries and exits, which can be analyzed to optimize LEZ management and policies without compromising user privacy.
- Feasibility Validation: Laboratory and field tests confirmed the system’s effectiveness under real-world conditions, demonstrating its ability to handle varying traffic speeds and environmental constraints.
4.6.2. On DICE-Free Smart Cities, Particulate Matter, and Feedback-Enabled Access Control [31]
- PM Emissions from Non-Exhaust Sources: The study highlights that tire-related PM emissions in cities like Dublin might already exceed levels deemed safe by the World Health Organization (WHO), even without internal combustion engine (ICE) vehicles.
- Feedback-Based Regulation: It introduces an adaptive control mechanism to regulate vehicle access based on real-time PM emission levels, ensuring emissions remain below safe thresholds while maximizing city access for vehicles and passengers.
- Ride-Sharing Scheme: It encourages carpooling by integrating a probabilistic selection method for vehicles based on occupancy, prioritizing fully occupied cars for city access.
- Digital Token Compliance System: A digital token system is used as a compliance mechanism to ensure adherence to ride-sharing agreements. Tokens are forfeited for non-compliance, such as failing to pick up passengers or promoting cooperative behavior.
- Simulation Results: It demonstrated through simulations that the system can maintain PM levels within safe boundaries while ensuring fair access for drivers and passengers over extended periods.
4.7. Policy Impact and Integration
Can Low-Emission Zones Be Managed More Dynamically and Effectively? [34]
- Dynamic Pricing Based on Real Emissions: Charges are calculated using a differentiated pricing model for pollutants such as NOx, PM, HC, and CO. The system accounts for spatial and temporal variations in environmental sensitivity, allowing for adaptive tolling during periods of high pollution.
- Incentives for Behavioral Change: By implementing real-time data collection and feedback mechanisms, the GAZ model encourages carriers to adopt eco-friendly practices, such as optimizing routes, increasing load factors, and adopting cleaner vehicles.
- Fairness and Transparency: The system adheres to the Polluter Pays Principle, ensuring that charges are proportionate to the environmental impact of each vehicle. Stakeholders can also document their environmental performance, creating incentives for sustainable competition.
- Stakeholder Engagement and Acceptability: Surveys conducted among retailers, wholesalers, and carriers highlight variations in acceptance levels. Wholesalers are more willing to adopt the GAZ framework than carriers, who express concerns over cost implications and limited perceived effectiveness.
- Technological Feasibility: The study emphasizes the readiness of current technologies, such as onboard sensors and real-time monitoring systems, to support the implementation of GAZ.
5. Discussion
Assessment of Risk of Bias
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
MDPI | Multidisciplinary Digital Publishing Institute |
DOAJ | Directory of Open Access Journals |
TLA | Three-letter acronym |
LD | Linear dichroism |
LEZ | Low-Emission Zone |
EPZ | Environmental Protection Zone |
EEA | European Environment Agency |
PM | Particulate Matter |
CO2 | Carbon Dioxide |
NOx | Nitrogen Oxides |
WHO | World Health Organization |
ERP | Electronic Road Pricing |
OBU | On-Board Unit |
CBD | Central Business District |
IoT | Internet of Things |
CHIMERE-WRF | Chemical transport models used in the study |
MILP | Mixed Integer Linear Programming |
Appendix A
First Author (Year) | 1. Sample Frame Appropriate? | 2. Sample Size Adequate? | 3. Study Setting Described in Detail? | 4. Data Analysis Coverage? | 5. Valid Methods for Emissions? | 6. Emissions Measured Reliably? | 7. Statistical Analysis Appropriate? | Overall Quality Rating |
---|---|---|---|---|---|---|---|---|
Roger Jardí-Cedó (2014) [18] | No | No | Yes | Yes | Unclear | Unclear | Yes | Moderate |
Roger Jardí-Cedó (2015) [19] | No | No | Yes | Yes | Unclear | Unclear | Yes | Moderate |
Bernardo et al. (2021) [20] | Yes | Yes | Yes | Yes | Yes | Yes | Yes | High |
Roger Jardí-Cedó (2018) [22] | No | No | Yes | Yes | Unclear | Unclear | Yes | Moderate |
Anglès-Tafalla (2019) [23] | No | No | Yes | Yes | Unclear | No | Yes | Moderate |
Paricio-Garcia (2024) [24] | Yes | Yes | Yes | Yes | Yes | Yes | Yes | High |
Wen et al. (2022) [25] | Yes | Yes | Yes | Yes | Yes | Yes | Yes | High |
Anglès-Tafalla (2023) [26] | No | No | Yes | Yes | Unclear | No | Yes | Moderate |
Rui Feng (2023) [27] | Yes | Yes | Yes | Yes | Yes | Yes | Yes | High |
Jardí-Cedó (2016) [28] | No | No | Yes | Yes | Unclear | Unclear | Yes | Moderate |
Pili et al. (2023) [29] | Yes | Yes | Yes | Yes | Yes | Yes | Yes | High |
Fernández et al. (2023) [30] | Yes | Yes | Yes | Yes | Yes | Yes | Yes | High |
Katsikouli et al. (2020) [31] | No | No | Yes | Yes | Unclear | No | Yes | Moderate |
Anglestafalla (2018) [32] | Yes | Yes | Yes | Yes | Unclear | Unclear | Yes | Moderate |
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Title | The title of the study. |
Authors | The authors who conducted the study. |
Identified Problems or needs | The specific issues or challenges related to LEZs. |
System Proposal | The proposed system or framework introduced by the study to tackle the identified problems. |
Proposed Measures | The strategies or measures suggested for implementing the proposed system, such as variable pricing, emission-based access controls, or integration with public transport. |
Key Results | The main findings and outcomes of the study include reductions in emissions (e.g., NOx, CO2, PM), improvements in traffic flow, and improvements in levels of public acceptance. |
Year of Publication | The year the study was published. |
Dataset | The dataset used for case studies and practical experiments. Where available, the city, country, traffic volumes, time bands, and the status of real or synthetic data are shown. |
Title | Authors | Identified Problems | System Proposal | Proposed Measures | Key Results | Year | Dataset |
---|---|---|---|---|---|---|---|
Electronic Road Pricing System for Low Emission Zones to Preserve Driver Privacy [18] | Roger Jardí-Cedó et al. | Privacy concerns in LEZs due to tracking and potential misuse of driver data and inadequate fraud detection mechanisms in existing systems | An ERP system incorporating cryptographic proofs and revocable anonymity, ensuring honest driver privacy while enabling identification of fraudulent drivers | Use of tamper-proof secure elements (SEs), dynamic fee calculation based on emissions and time, and cryptographic checkpoints for authentication and fraud detection | A robust privacy-preserving system that dynamically adjusts fees ensures fairness and identifies dishonest drivers without tracking itineraries | 2014 | No experimental data used |
Privacy-Preserving Electronic Toll System with Dynamic Pricing for Low Emission Zones [19] | Roger Jardí-Cedó et al. | Privacy issues and fraud detection inefficiencies in traditional ERP systems for LEZs | A dynamic pricing ERP system leveraging cryptographic techniques and prepayment mechanisms to enhance privacy and fraud detection | Dynamic pricing based on real-time traffic, revocable anonymity protocols, secure tamper-proof modules, and cryptographically signed records | Robust fraud control, ensured privacy for honest drivers, scalability for urban environments, and operational feasibility | 2015 | Theoretical. No experimental data used |
Pollution and congestion in urban areas: The effects of low emission zones [20] | Bernardo et al. | Ineffectiveness of LEZs in mitigating congestion and trade-offs between the objectives of pollution reduction and congestion alleviation | Analyzes the impact of LEZs on pollution and congestion in European cities | Use of panel data to evaluate PM2.5 pollution and congestion effects and a comparison of LEZs’ effectiveness with urban tolls | LEZs effectively reduce PM2.5 emissions, with a reduction of up to 33% in areas such as Rome; minimal impact on congestion reduction due to latent demand and substitution of vehicles | 2021 | EU CLARS (Charging, Low Emission Zones, other Access Regulation Schemes) covering 33 cities from 5 countries [21] |
Time-based low emission zones preserving drivers’ privacy [22] | Roger Jardí-Cedó et al. | Privacy concerns in LEZs due to vehicle tracking, inadequate fraud detection, and trade-offs between fraud control and driver privacy | A user-centric ERP system for LEZs, prioritizing privacy for honest drivers while maintaining fraud detection efficiency | Implementation of cryptographic techniques for anonymity, revocation for fraud detection, and dynamic proof generation to prevent traceability | Successfully balances privacy and fraud detection, enabling practical deployment with mobile checkpoints and dynamic conditions | 2018 | Theoretical. No experimental data used |
Decentralized Privacy-preserving Access for Low Emission Zones [23] | Carles Anglès-Tafalla et al. | Privacy concerns and reliance on centralized entities in LEZ systems, leading to potential misuse of user data and lack of transparency | A decentralized access-control system using blockchain and group signatures to ensure privacy and fraud detection | Blockchain integration for access and payment processes, group signature schemes for anonymity and unlinkability, and revocable anonymity for fraud control | Achieved efficient and privacy-preserving access control with reduced reliance on centralized entities, demonstrating scalability and lightweight implementation | 2019 | Theoretical. No experimental data used |
Optimized Design of Low Emission Zones in SUMO: A Dual Focus on Emissions Reduction and Travel Time Improvement [24] | Paricio-Garcia et al. | Inefficiencies in static LEZ configurations that fail to adapt to real-time traffic and emission data | An optimization model for LEZs using the SUMO framework and genetic algorithms to adjust boundaries based on real-time traffic conditions dynamically | Use of genetic algorithms to select optimal LEZ boundaries, integration of real-time data, and validation of the approach through simulations in Guadalajara, Spain | Achieved a 35% reduction in CO2 emissions and a 15% improvement in travel times compared to traditional static LEZs | 2024 | Synthetic dataset from Guadalajara, Spain |
A data-driven method of traffic emission mapping with land use random forest models [25] | Wen et al. | Lack of precision in estimating traffic emissions and the inability of traditional LEZ models to integrate real-time, link-level emission data | Introduction of the land use random forest (LURF) approach to dynamically map real-time vehicle emissions using high-density traffic and land use data | Integration of 272 predictors, dynamic temporal and spatial analyses of emissions, scenario evaluation for LEZ policies, and a focus on scalability for real-time applications | Achieved accuracy in traffic volume and speed simulations, identified significant emission reductions under traffic demand management (up to 66% CO2 reduction) and fleet electrification (31% CO2 reduction) | 2022 | Chengdu, China |
Security and Privacy in a Blockchain-Powered Access Control System for Low Emission Zones [26] | Carles Anglés-Tafalla et al. | Privacy threats in centralized Low-Emission Zone (LEZ) management systems due to single points of failure and indiscriminate user data collection | A decentralized LEZ access-control system using blockchain and smart contracts for privacy preservation and enhanced security | Integration of blockchain transactions for vehicle access, implementation of smart contracts for automatic fee calculation, and revocable anonymity for fraud detection | Demonstrated feasibility in real-world scenarios, ensuring driver privacy and scalable fraud detection, validated through extensive experiments | 2023 | No experimental data used for LEZ. Individual vehicle estimations |
Collaborative road pricing strategy for heterogeneous vehicles considering emission constraints [27] | Rui Feng et al. | Urban congestion and emissions from heterogeneous vehicle types, lack of integrated congestion and emission pricing strategies | A bi-level collaborative road pricing model incorporating congestion and emission tolls for conventional vehicles (CVs) and new energy vehicles (NEVs) | Bi-level optimization model, distinct tolling mechanisms for VCs and NEVs, real case study in Beijing, dynamic analysis of NEV penetration and emission constraints | Achieved a 5.04% reduction in total travel time, a 10.0% reduction in LEZ emissions, and a 14.35% increase in bus participation rate; demonstrated significant environmental and efficiency benefits | 2023 | Beijing, China |
Privacy-Preserving and Secure Decentralized Access Control System for LEZ [28] | Roger Jardí-Cedó et al. | Privacy concerns and limitations of centralized access-control systems in LEZs | A decentralized access-control system using blockchain technology to preserve privacy and prevent fraud | Implementation of blockchain-based transactions, pseudonyms for user anonymity, cryptographic proofs for fraud prevention, and scalability with cryptocurrency mixing services | Provides privacy preservation, non-repudiation, and fraud control, ensuring scalable and adaptable LEZ management | 2016 | Theoretical. No experimental data used |
An Optimization-based Approach for Designing Urban Low Emission Zones [29] | Pili et al. | Limitations of trial-and-error approaches in designing LEZs, such as inefficiency in determining optimal boundaries and locations for infrastructure | A Mixed Integer Linear Programming (MILP)-based optimization framework to design LEZs by balancing coverage area, user travel costs, and network link saturation | Integrated optimization model, hexagonal zoning system for precise configuration, multi-criteria objective function, and case study validation | Achieved significant reductions in travel costs and network congestion, with efficient computational performance, solving scenarios in under 20 s | 2023 | Synthetic graph data. Small scenario |
Advancing Sustainability Impact Assessment: A Comprehensive Tool for Low Emission Zone Management [30] | Fernández et al. | Lack of integrated, real-time data-driven tools for effective LEZ management and impact assessment | A comprehensive tool that combines real-time monitoring, air quality modeling, and data analytics to enhance urban sustainability in LEZs | Use of IoT sensors, cameras, advanced air quality models, and real-time data integration for monitoring and evaluating LEZ effectiveness | Achieved up to 86% CO2 equivalent reductions in Helsinki, demonstrating the potential of adaptive LEZ management strategies | 2023 | Simulation in several EU cities: Tallinn, Amsterdam, Helsinki, Paris, Stavanger |
On DICE-free Smart Cities, Particulate Matter, and Feedback-Enabled Access Control [31] | Katsikouli et al. | Lack of regulation on non-exhaust particulate matter (PM) emissions, mainly from tire and brake wear, exacerbated by increased use of electric vehicles | A feedback-enabled access-control system that regulates vehicle access based on real-time PM emission levels and encourages ride-sharing | Introduction of a feedback-based regulation mechanism, ride-sharing promotion through a probabilistic selection method, and a digital token compliance system to enforce ride-sharing agreements | Simulations showed that the system effectively maintains PM levels within safe thresholds while promoting fair access for drivers and passengers | 2020 | Simulation over Dublin, Ireland |
Security and Privacy in a Blockchain-Powered Access Control System for Low Emission Zones [32] | Carles Anglés-Tafalla et al. | High infrastructure costs and privacy concerns in traditional LEZ access-control systems, particularly with vehicle-installed devices | A lightweight, privacy-preserving access-control system utilizing smartphones for driver identification and access validation | Use of low-cost cryptographic protocols, smartphone integration for access control, and real-time data collection to optimize LEZ management | Laboratory and field tests validated the system’s effectiveness, confirming its scalability, low-cost implementation, and adaptability to various traffic and environmental conditions | 2018 | Synthetic generic simulation scenario |
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Manglano-Redondo, P.; Paricio-Garcia, A.; Lopez-Carmona, M.A. Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review. Appl. Sci. 2025, 15, 2915. https://doi.org/10.3390/app15062915
Manglano-Redondo P, Paricio-Garcia A, Lopez-Carmona MA. Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review. Applied Sciences. 2025; 15(6):2915. https://doi.org/10.3390/app15062915
Chicago/Turabian StyleManglano-Redondo, Pablo, Alvaro Paricio-Garcia, and Miguel A. Lopez-Carmona. 2025. "Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review" Applied Sciences 15, no. 6: 2915. https://doi.org/10.3390/app15062915
APA StyleManglano-Redondo, P., Paricio-Garcia, A., & Lopez-Carmona, M. A. (2025). Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review. Applied Sciences, 15(6), 2915. https://doi.org/10.3390/app15062915