000145275 001__ 145275 000145275 005__ 20241015122514.0 000145275 0247_ $$2doi$$a10.3390/su16073041 000145275 0248_ $$2sideral$$a140122 000145275 037__ $$aART-2024-140122 000145275 041__ $$aeng 000145275 100__ $$aCurtò y Díaz, J. de 000145275 245__ $$aAnalysis of Transportation Systems for Colonies on Mars 000145275 260__ $$c2024 000145275 5060_ $$aAccess copy available to the general public$$fUnrestricted 000145275 5203_ $$aThe colonization of Mars poses unprecedented challenges in developing sustainable and efficient transportation systems to support inter-settlement connectivity and resource distribution. This study conducts a comprehensive evaluation of two proposed transportation systems for Martian colonies: a ground-based magnetically levitated (maglev) train and a low-orbital spaceplane. Through simulation models, we assess the energy consumption, operational and construction costs, and environmental impacts of each system. Monte Carlo simulations further provide insights into the cost variability and financial risk associated with each option over a decade. Our findings reveal that while the spaceplane system offers lower average costs and reduced financial risk, the maglev train boasts greater scalability and potential for integration with Martian infrastructural development. The maglev system, despite its higher initial cost, emerges as a strategic asset for long-term colony expansion and sustainability, highlighting the need for balanced investment in transportation technologies that align with the goals of Martian colonization. Further extending our exploration, this study introduces advanced analysis of alternative transportation technologies, including hyperloop systems, drones, and rovers, incorporating dynamic environmental modeling of Mars and reinforcement learning for autonomous navigation. In an effort to enhance the realism and complexity of our navigation simulation of Mars, we introduce several significant improvements. These enhancements focus on the inclusion of dynamic atmospheric conditions, the simulation of terrain-specific obstacles such as craters and rocks, and the introduction of a swarm intelligence approach for navigating multiple drones simultaneously. This analysis serves as a foundational framework for future research and strategic planning in Martian transportation infrastructure. 000145275 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000145275 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000145275 700__ $$0(orcid)0000-0002-5844-7871$$ade Zarzà y Cubero, I. de$$uUniversidad de Zaragoza 000145275 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf. 000145275 773__ $$g16, 7 (2024), 3041 [27 pp.]$$pSustainability (Basel)$$tSustainability (Switzerland)$$x2071-1050 000145275 8564_ $$s1671697$$uhttps://zaguan.unizar.es/record/145275/files/texto_completo.pdf$$yVersión publicada 000145275 8564_ $$s2576191$$uhttps://zaguan.unizar.es/record/145275/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000145275 909CO $$ooai:zaguan.unizar.es:145275$$particulos$$pdriver 000145275 951__ $$a2024-10-15-10:51:24 000145275 980__ $$aARTICLE