Development of AI-Augmented optimization technique for analysis & prediction of modal mix in road transportation

Abstract

Transport sector contribution to global emissions is a known fact, however, the mitigation path to achieve nationally determined goals for carbon reduction is often not specified, A simplified technique based on minimax optimization using Grey relational grade and Random forest narrows down on most contributing input variables from twelve road transport modes. This is a region-specific, scenario-based technique applied to north Punjab, Province of Pakistan that first categorizes modes based on their emission and then integrates with AI modeling using Deep Neural Network to develop sustainable trade-offs for carbon reduction. The output parameter translates the problem into a systematic iterative technique that predicts optimization options with different scenarios to bring out an environment-friendly transport mix. A 25% reduction applied to the five most emission-releasing modes like Diesel Light and Heavy Duty vehicles, Gas Light and heavy-duty vehicles, and Gas-Cars results in 16.54 MT of Carbon dioxide which is 54.35% reduced to the predicted 36.24 MT for the year 2044. Similarly in another scenario replacing 25% Gas and Diesel Light Duty vehicles respectively by adding 50% Petrol Light Duty vehicles leads to 18.94 MT of emissions which brings the emission value in 2044 at par with emission releases of the year 2014. The technique offers a forward path that allows environment-friendly modal mix combinations based on business-as-usual to offer transport mix solutions for carbon reduction. It is a generalized model that is based on a customized transport mix. Future studies can also be applied to intermodal tradeoffs like rail, air, waterways, etc.