Mathematical and statistical analysis of operational data from transportation systems that impact the final result of greenhouse gas emissions and other atmospheric pollutants

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Volume: 11 | Issue: 02 | Year 2025 | Subscription
International Journal of Transportation Engineering and Traffic System
Received Date: 09/02/2025
Acceptance Date: 09/13/2025
Published On: 2025-09-15
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By: Carlos Eduardo Sanches de Andrade and Alberto Maurício Souza Corrêa

Abstract

Abstract

The main objective of this study is to identify which mathematical and statistical models are used to forecast greenhouse gas emissions and other atmospheric pollutants from transportation systems. The research demonstrates how mathematical and statistical tools are used to identify and anticipate future scenarios from an environmental perspective, thus contributing to better solutions for mitigating increased emissions. The research concludes that mathematical and statistical models can more accurately anticipate and predict future challenges regarding the predictability of emissions from the transportation sector, promoting strategies that make urban mobility more sustainable. By identifying patterns and trends in emission data, these models contribute to developing proactive measures aimed at mitigating the negative impacts of transportation on the environment. The findings underscore that mathematical and statistical models play a crucial role in accurately predicting and managing future challenges associated with transportation-related emissions, thereby facilitating the development of policies and technological interventions that promote environmentally friendly, efficient, and sustainable urban mobility solutions. In addition, the study emphasizes the importance of integrating real-time data from traffic monitoring systems, fuel consumption reports, and meteorological inputs into forecasting models to improve their precision and adaptability. The growing availability of big data, combined with advancements in computational power, has allowed for more robust and dynamic emission models that can account for temporal and spatial variations in traffic patterns. However, challenges remain in terms of data quality, uncertainty in input variables, and the complexity of human behavior affecting transportation trends. Addressing these challenges requires a multidisciplinary approach that combines expertise from environmental science, transportation engineering, data analytics, and public policy.Furthermore, the adoption of such predictive tools can lead to the implementation of smarter transportation infrastructures, optimized traffic management systems, and targeted interventions aimed at reducing peak emissions. Encouraging the use of cleaner fuels, promoting electric vehicles, and enhancing public transport networks can be guided by insights from these models. Overall, this research reinforces that mathematical and statistical modeling is not only vital for forecasting emissions but also for driving systemic changes that align with global sustainability goals and foster healthier urban environments.

Keywords:greenhouse gas emissions, transportation systems, statistical models, mathematical modeling, air pollution forecasting, sustainable mobility, machine learning, time-series analysis, emission control, urban planning

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How to cite this article: Carlos Eduardo Sanches de Andrade and Alberto Maurício Souza Corrêa, Mathematical and statistical analysis of operational data from transportation systems that impact the final result of greenhouse gas emissions and other atmospheric pollutants. International Journal of Transportation Engineering and Traffic System. 2025; 11(02): -p.

How to cite this URL: Carlos Eduardo Sanches de Andrade and Alberto Maurício Souza Corrêa, Mathematical and statistical analysis of operational data from transportation systems that impact the final result of greenhouse gas emissions and other atmospheric pollutants. International Journal of Transportation Engineering and Traffic System. 2025; 11(02): -p. Available from:https://journalspub.com/publication/ijtets/article=22326

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