{"id":56,"date":"2018-11-06T16:06:12","date_gmt":"2018-11-06T16:06:12","guid":{"rendered":"http:\/\/maz-research.com\/?p=56"},"modified":"2018-11-07T09:51:05","modified_gmt":"2018-11-07T09:51:05","slug":"transportszenarien-energiespeicher-und-infrastruktur-fuer-den-zukuenftigen-guetertransport-transport-scenarios-energy-sources-and-infrastructure-for-future-freight-transport","status":"publish","type":"post","link":"https:\/\/maz-research.com\/?p=56","title":{"rendered":"Transportszenarien, Energiespeicher und Infrastruktur f\u00fcr den zuk\u00fcnftigen G\u00fctertransport \/\/ Transport Scenarios, Energy Sources and Infrastructure for Future Freight Transport"},"content":{"rendered":"

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Deutsch \/\/ English version below<\/p>\n

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In meiner Master-Semesterarbeit hatte ich mich mit dem zuk\u00fcnftigen Langstreckeng\u00fctertransport f\u00fcr die Jahre 2030 und 2040 befasst. Eine Doppelanstrengung, die sich damit auseinandersetze sowohl zweimal 30 Zukunftsthemen f\u00fcr die Jahre 2030 & 2040 statistisch zu ermitteln sowie eine m\u00f6glichst automatisierte Zukunftsprojektionsmethodik zu etablieren. F\u00fcr die Automation der Zukunftsprojektion bestand die Idee darin, dass Expertenwissen nur am Anfang f\u00fcr Zukunftszusammenh\u00e4nge bez\u00fcglich Einflussnahme, Kausalit\u00e4t & vertiefter Konsistenz der 30 Themen einflie\u00dfen und danach maschinell die Zukunftsszenarien generiert werden. Das Clustern von viel Information ist bis heute mehrheitlich ein manueller Schritt, der viel Experten- & Clustering-Wissen erfordert. Jedoch wenn man moderne Methoden des Matrix gest\u00fctzten Produkt-Komplexit\u00e4tsmanagements in die Szenariotechnik einflie\u00dfen l\u00e4sst, ergeben sich hier neue, verbesserte M\u00f6glichkeiten. Maschinelles Lernen (Machine learning) k\u00f6nnte dies weiter automatisieren sowie optimieren. Mein Fazit f\u00fcr den G\u00fcterverkehr der Zukunft. Wir m\u00fcssen die Umweltauswirkungen aktiv \u00fcberall monet\u00e4r einflie\u00dfen lassen und Transport, Logistik sowie Multimodalit\u00e4t in 3 anstatt 2 Dimensionen denken. Der Klimawandel kommt.<\/p>\n

Mehr auf http:\/\/www.truck2030.tum.de<\/a><\/p>\n

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During my Master term thesis, I analysed the future long-distance freight transport for the years 2030 and 2040. A double-challenging task which included twice identifying 30 future projections for the years 2030 & 2040 in a statistical way. For the automation of building projections there has been the idea using expert knowledge just at the beginning due to influences, causality & deepened consistency of the 30 future topics. After that machine generated future projection should develop automatically. Nowadays Clustering of information is mainly a manual step which needs loads of expert and clustering knowledge at the same time. But if you use modern methods in matrix-based product complexity management in the field of scenarios new possibilities of improvement are coming up. Machine learning could even improve it again. My conclusion for the future freight transport: Integrating environmental impacts directly in way of environmental costs everywhere and thinking transport, logistics and multimodality in 3 instead of 2 dimensions. Climate change is coming.<\/p>\n

Find more on http:\/\/www.truck2030.tum.de<\/a><\/p>\n

Publication no. \/ Report no.: 2018_maz_000002<\/p>\n","protected":false},"excerpt":{"rendered":"

Deutsch \/\/ English version below In meiner Master-Semesterarbeit hatte ich mich mit dem zuk\u00fcnftigen Langstreckeng\u00fctertransport f\u00fcr die Jahre 2030 und 2040 befasst. Eine Doppelanstrengung, die sich damit auseinandersetze sowohl zweimal 30 Zukunftsthemen f\u00fcr die Jahre 2030 & 2040 statistisch zu ermitteln sowie eine m\u00f6glichst automatisierte Zukunftsprojektionsmethodik zu etablieren. F\u00fcr die Automation der Zukunftsprojektion bestand die … Transportszenarien, Energiespeicher und Infrastruktur f\u00fcr den zuk\u00fcnftigen G\u00fctertransport \/\/ Transport Scenarios, Energy Sources and Infrastructure for Future Freight Transport<\/span> weiterlesen<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-56","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/maz-research.com\/index.php?rest_route=\/wp\/v2\/posts\/56","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/maz-research.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/maz-research.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/maz-research.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/maz-research.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=56"}],"version-history":[{"count":8,"href":"https:\/\/maz-research.com\/index.php?rest_route=\/wp\/v2\/posts\/56\/revisions"}],"predecessor-version":[{"id":74,"href":"https:\/\/maz-research.com\/index.php?rest_route=\/wp\/v2\/posts\/56\/revisions\/74"}],"wp:attachment":[{"href":"https:\/\/maz-research.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=56"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/maz-research.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=56"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/maz-research.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=56"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}