# Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review
## 4 Results
### Optimization and Simulation
#### 4.5.1 An Optimization-Based Approach to Designing Urban Low-Emission Zones [29]  
Optimization-based methodologies for designing LEZs are a key factor. This work studied how to maximize the area covered while minimizing user travel costs and network link saturation. The approach addresses the limitations of existing trial-and-error LEZ design methods by providing a systematic framework based on Mixed Integer Linear Programming (MILP). The main contributions of the study are as follows:  
* 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.  
The optimization framework provides a robust foundation for dynamic LEZ design, enabling real-time adjustments to LEZ boundaries and infrastructure based on traffic and emission data. This approach aligns with adaptive strategies for sustainable urban mobility by incorporating network-wide impacts and user costs.  
5.2 Optimized Design of Low-Emission Zones in SUMO: A Dual Focus on Emissions Reduction and Travel Time Improvement [24]  
This study explores optimizing Low-Emission Zones (LEZs) through a novel integration of the Simulation of Urban Mobility (SUMO) framework and genetic algorithms. The approach balances emission reduction and urban mobility, proposing dynamic configurations to address real-time traffic conditions. The key contributions are as follows:  
* 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 CO\({}_{2}\) 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.  
It showcases how real-time adjustments can significantly enhance both environmental and traffic outcomes. The results suggest that optimized LEZs offer a viable pathway for cities to reduce emissions while improving mobility efficiency.