CO2 Emission through Road Gradient and Real-Time Traffic Monitoring for Vehicle Routing Problems

Abstract

The upsurge in atmospheric CO2 levels has come to humankind’s attention during the last couple of decades, mainly because the global temperature has risen, ice sheets have melted, and natural disasters have been happening more frequently and with more intensity. Hereby, the focus of this study is to develop a robust routing model that minimizes CO2 transportation emissions that could be used worldwide. The empirical study proves that the advantages of our proposed method for CO2 reduction in the region 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. Therefore, the main contribution of this study is to penalize very steep or slow roads from the fleets rout map. The application of this method is vastly relevant in areas where elevation above sea level of towns is very high. Therefore, the way streets 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 on a dynamic Capacitated Vehicle Routing Problem as an NPhard problem and generates a linear model that efficiently can capture CO2 emissions. Numerical results using Ant Colony Optimization validate the proposed strategy. Key findings highlight -2.62% of CO2 emissions changes within a short computational time.