نوع مقاله : پژوهشی - کاربردی
نویسندگان
1 استاد گروه سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده برنامهریزی و علوم محیطی، دانشگاه تبریز، تبریز، ایران.
2 کارشناسی ارشد گروه سنجش از دور و سیستم اطلاعات جغرافیایی، دانشکده برنامهریزی و علوم محیطی، دانشگاه تبریز، تبریز، ایران.
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسندگان [English]
The increasing growth of urban travel and the concentration of service and commercial activities in urban centers have led to high traffic congestion, a decline in the level of service of roads, and an increase in environmental pollution. One of the fundamental solutions to control these challenges is the revision and optimization of land-use patterns. This study aims to develop an optimization model to reduce urban traffic congestion using Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) in the central area of Tabriz city. This area, with its high population density, concentration of commercial and administrative centers, and strategic role in the city's spatial structure, faces chronic traffic issues. In this study, using VISSIM software, transportation network modeling was conducted under various scenarios, and performance indicators such as travel time, delay, and level of service were analyzed. The results showed that the proposed model, compared to the existing situation, significantly improved network performance and reduced traffic congestion. With its high convergence capability, the model not only enhances the quality of results but also maintains adequate stability. The findings suggest that integrating spatial data with optimization algorithms can provide a scientific and practical foundation for integrated land use and transportation management, supporting sustainable urban development
کلیدواژهها [English]
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