OPTIMIZING MULTI MODEL TRANSPORTATION FOR REDUCED CARBON EMISSIONS

Abstract

It accounts for a large majority of world greenhouse gas emissions, and as it develops rapidly its environmental impact depends. By weaving together all available modes into a single transportation system (such as roads, rails, air and waterways), multi-modal transport offers a viable means both of cutting emissions as well increasing system efficiencies and even connectivity. In this paper we study multi-modal transportation network optimization, initially with a view towards minimizing CO2 emissions and particularly in regional urban contexts. The main goal of this research, then, is to deliver models and procedures that can balance environmental sustainability operations efficiency, cost-effectiveness. With these advanced computing techniques – including optimization algorithms, data analytics and machine learning methods- it is hoped that the study will manage to identify the most efficient routes and mode combinations that cut the carbon footprint of transportation to its lowest level. Having made a detailed analysis of current transportation networks, on which energy consumption patterns for each source can be derived from existing emissions factors, we are now in a position to lay out models that can be transported into reality. Furthermore, the research evaluates such new technologies as electric vehicles, independent transport and smart logistic systems as well as the role they play in adding further benefits to the sustainability of multi-modal networks. Throughout a series of case studies and experiments the study demonstrates the potential benefits of multi-modal optimization in achieving lower carbon emissions at least accessible to both policy-makers as well as transportation planners and manufacturing elements wherever they may be located. This research contributes to a greener, more efficient transportation strategy in numerous ways while also proposing how environmental goals can be integrated into transportation infrastructure planning.