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DOI: https://doi.org/10.4491/eer.2017.093
Modelling of CO2 and NOx on signalize roundabout using Modified Adaptive Neural Fuzzy Inference System (ANFIS) model
Ghassan Sulaiman1, Mohammad K. Younes2, and Ghassan A. Al-Dulaimi2
1Department of Civil Engineering, Aqaba University of Technology, 11947, Aqaba, Jordan
2Department of Civil Engineering, Philadelphia University, 19392, Amman, Jordan
Corresponding Author: Mohammad K. Younes ,Tel: +962-7913-68173 , Fax: +962-6-4799040, Email: myounes@philadelphia.edu.jo
Received: July 19, 2017;  Accepted: October 10, 2017.
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Air quality and pollution have recently become a major concern; vehicle emissions significantly pollute the air, especially in large and crowded cities. There are various factors that affect vehicle emissions; this research aims to find the most influential factors affecting CO2 and NOx emissions using Adaptive Neural Fuzzy Inference System (ANFIS) as well as a systematic approach. The Modified ANFIS (MANFIS) was developed to enhance modelling and Root Mean Square Error was used to evaluate the model performance. The results show that percentages of CO2 from trucks represent the best input combination to model. While for NOx modelling, the best pair combination is the vehicle delay and percentage of heavy trucks. However, the final MANFIS structure involves two inputs, three membership functions and nine rules. For CO2 modelling the triangular membership function is the best, while for NOx the membership function is two-sided Gaussian.
Keywords: Air pollution | Air quality index | ANFIS | Traffic congestion | Transportation emissions
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