CO2 Emission using Road Gradient and Real-Time Traffic Monitoring in Vehicle Routing Problems

Abstract

The upsurge in atmospheric CO2 levels has come to humankind’s attention during the last couple of decades, mainly because of the rise in the global temperature, ice sheets melting, and more frequent and intense natural disasters. Hereby, the focus of this study is to develop a robust routing model that minimizes CO2 transportation emissions so that this can be used worldwide to minimize global warming issues. The empirical study in this work proves that the advantages of our proposed method for CO2 reduction in regions with a growing economy, especially in Latin America and the Caribbean, where GDP growth disassociates from fossil fuel consumption and the topography can seriously affect the air pollution generated by vehicles, is an efficient solution as a short-term mitigation strategy. The main contribution of this study is to penalize very steep or slow roads from the fleet route map. The application of this method is vastly relevant in areas where elevation above sea level of towns is very high. Therefore, how the routes are made plays a huge role in the carbon footprint of vehicle fleets. The problem considered in this paper is to produce routes for a fleet of delivery vehicles that minimizes fuel emissions, considering the load of the vehicle, the time traveled, the distance traveled, and the road gradient of a road network using the information extracted from Google Earth. The study investigates a dynamic capacitated vehicle routing problem as an NP-hard problem and generates a linear model that efficiently can capture CO2 emissions. Numerical results using ant colony optimization (ACO) validate the proposed strategy. Key findings highlight -2.62% of CO2 emissions changes within a short computational time.