Reducing Carbon Footprint with Real-Time Transport Planning and Big Data Analytics

Abstract

The growing concern about the impact of transportation on the environment has led to increased interest in developing more sustainable transportation systems. This paper presents a new approach to reduce carbon footprint by using real-time transport planning and big data analytics. The objective is to optimize transport operations, thereby reducing fuel consumption and greenhouse gas emissions. The current state of knowledge and the question posed in this paper is how to achieve sustainable transportation systems. To address this issue, the methodology used involves collecting data from traffic sensors and other sources to create real-time traffic models that can be used for optimal transport planning. Statistical modeling and machine learning techniques are applied to improve the accuracy of traffic predictions and optimize the routing of vehicles. The main results of this study demonstrate that the proposed approach is effective in reducing fuel consumption and greenhouse gas emissions. By analyzing real-time traffic data and optimizing transport operations, it is possible to reduce carbon footprint significantly. The benefits of this approach extend beyond the environment, as it can also lead to cost savings for transportation companies and improve traffic flow for road users. The consequences of this research are significant, as it offers a new solution for reducing the environmental impact of transportation. The proposed approach can be applied to a variety of transportation modes, including cars, trucks, and public transportation, and has the potential to be implemented in various cities and regions. By reducing carbon footprint, this approach can contribute to achieving global targets for reducing greenhouse gas emissions, as well as improve the overall sustainability of transportation systems.