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Development of an Artificial Intelligence-Based Method for Evaluating Freight Transport Corridors: A Case Study of Eurasian | ||
| Earth Observation and Geomatics Engineering | ||
| دوره 9، شماره 1، شهریور 2025 | ||
| نوع مقاله: Original Article | ||
| شناسه دیجیتال (DOI): 10.22059/eoge.2026.409711.1195 | ||
| نویسندگان | ||
| Fatemeh Naderi؛ Mohammad Reza Malek* | ||
| GIS Dept. Faculty of Geodesy and Geomatics Eng. K.N. Toosi University of Technology | ||
| چکیده | ||
| Freight transport corridors constitute critical infrastructure for global trade, economic integration, and geopolitical security. Geospatial information systems and network analysis techniques are fundamental to understanding the complex spatial relationships, connectivity patterns, and geographic constraints that characterize these multimodal transport networks, enabling comprehensive assessment of corridor performance and supporting evidence-based decision-making in transport infrastructure development. Among these, Eurasian corridors represent strategically vital arteries connecting Asian and European markets, where optimization challenges are particularly pronounced due to diverse infrastructure conditions and complex multimodal integration requirements. The optimization of Eurasian freight corridors faces significant challenges due to multiple competing objectives and the complex nature of transport networks. This study presents a comparative analysis of three algorithms - Dijkstra, NSGA-II, and MOACO - for multi-objective optimization of multimodal transport routes considering cost, time, and distance criteria. Using a network of 48 nodes and 61 edges, optimal routes were identified and evaluated based on efficiency metrics, solution diversity, and path quality. Results demonstrated that all three algorithms achieved the same best combined objective value (45.252), while exhibiting distinct advantages: Dijkstra achieved instant execution for single-objective optimization, NSGA-II excelled in exploring trade-offs and maintaining diversity, while MOACO generated the highest number of non-dominated solutions. Path analysis revealed both a robust trunk route and flexible routing alternatives in initial segments. This research provides a framework for algorithm selection based on different operational requirements in Eurasian freight transport planning. | ||
| کلیدواژهها | ||
| Freight transport؛ Corridor evaluation؛ Artificial Intelligence؛ Genetic Algorithm؛ Ant Colony Optimization | ||
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آمار تعداد مشاهده مقاله: 34 |
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