Environmental Engineering Research

Korean Society of Environmental Engineers (대한환경공학회)
권호 표지
DOI QR코드

DOI QR Code

Modelling CO2 and NOx on signalized roundabout using modified adaptive neural fuzzy inference system model

  • Sulaiman, Ghassan (Department of Civil Engineering, Aqaba University of Technology) ;
  • Younes, Mohammad K. (Department of Civil Engineering, Philadelphia University) ;
  • Al-Dulaimi, Ghassan A. (Department of Civil Engineering, Philadelphia University)
  • Received : 2017.07.19
  • Accepted : 2017.10.10
  • Published : 2018.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

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 $CO_2$ and $NO_x$ 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 $CO_2$ from trucks represent the best input combination to model. While for $NO_x$ 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 $CO_2$ modelling the triangular membership function is the best, while for $NO_x$ the membership function is two-sided Gaussian.

Keywords

References

  1. Jin J, Rafferty P. Does congestion negatively affect income growth and employment growth? Empirical evidence from US metropolitan regions. Transport Policy 2017;55:1-8. https://doi.org/10.1016/j.tranpol.2016.12.003
  2. Suleiman GM, Bezgin NO, Ergun M, Gursoy M, Karasahin M. Effects of speed management and roadway parameters on traffic flow along arterials. In: Proceedings of the Institution of Civil Engineers-Transport. Thomas Telford Ltd.; 2017.
  3. Zhou H, Li Y, Liu H, et al. Temporal distribution, influencing factors and pollution sources of urban ambient air quality in Nanchong, China. Environ. Eng. Res. 2015;20:260-267. https://doi.org/10.4491/eer.2015.0035
  4. Studer L, Ketabdari M, Marchionni G. Analysis of adaptive traffic control systems design of a decision support system for better choices. J. Civil Environ. Eng. 2015;5:195.
  5. Solomon S. Segmental assessment of level of traffic congestion on Kality Ring Road to Dukem Bridge [dissertation]. Addis Ababa: Univ. of Addis Ababa; 2015.
  6. Sullivan JL, Baker RE, Boyer BA, et al. $CO_2$ emission benefit of diesel (versus gasoline) powered vehicles. Environ. Sci. Technol. 2004;38:3217-3223. https://doi.org/10.1021/es034928d
  7. Weichenthal S, Ryswyk KV, Kulka R, Sun L, Wallace L, Joseph L. In-vehicle exposures to particulate air pollution in canadian metropolitan areas: The urban transportation exposure study. Environ. Sci. Technol. 2014;49:597-605.
  8. Chen BP, Ma ZQ. Short-term traffic flow prediction based on ANFIS. In: International Conference on Communication Software and Networks; 27-28 February 2009; Macau, China: IEEE.
  9. Zengqiang M, Cunzhi P, Yongqiang W. Road safety evaluation from traffic information based on ANFIS. In: Control Conference, 2008. CCC 2008. 27th Chinese. 2008. IEEE.
  10. Soh AC, Rahman RZA, Rhung LG, Sarkan HM. Traffic signal control based on adaptive neural-fuzzy inference system applied to intersection. In: 2011 IEEE Conference on Open Systems (ICOS); 25-28 September 2011; Langkawi, Malaysia: IEEE.
  11. Khodayari A, Ghaffari A, Kazemi R, Manavizadeh N. ANFIS based modeling and prediction car following behavior in real traffic flow based on instantaneous reaction delay. In: 2010 13th International IEEE Conference on Intelligent Transportation Systems (ITSC); 19-22 September 2010; Funchal, Portugal: IEEE.
  12. Piri J, Kisi O. Modelling solar radiation reached to the Earth using ANFIS, NN-ARX, and empirical models (Case studies: Zahedan and Bojnurd stations). J. Atmos. Sol-Terr. Phy. 2015;123:39-47. https://doi.org/10.1016/j.jastp.2014.12.006
  13. Younes MK, Nopiah ZM, Basri NE, Basri H, Abushammala MF, Maulud KNA. Solid waste forecasting using modified ANFIS modeling. J. Air Waste Manage. Assoc. 2015;65:1229-1238. https://doi.org/10.1080/10962247.2015.1075919
  14. Karner AA, Eisinger DS, Niemeier DA. Near-roadway air quality: Synthesizing the findings from real-world data. Environ. Sci. Technol. 2010;44:5334-5344. https://doi.org/10.1021/es100008x
  15. Willmott CJ, Matsuura K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res. 2005;30:79. https://doi.org/10.3354/cr030079
  16. Antanasijevic DZ, Pocajt VV, Povrenovic DS, Ristic MĐ, Peric-Grujic AA. PM10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Sci. Total Environ. 2013;443:511-519.
  17. Younes MK, Nopiah ZM, Ahmad Basri NE, et al. Landfill area estimation based on integrated waste disposal options and solid waste forecasting using modified ANFIS model. Waste Manage. 2016;55:3-11.
  18. Pramanik N, Panda RK. Application of neural network and adaptive neuro-fuzzy inference systems for river flow prediction. Hydrol. Sci. J. 2009;54:247-260. https://doi.org/10.1623/hysj.54.2.247
  19. Khatibinia M, Salajegheh J, Fadaee MJ, Salajegheh E. Prediction of failure probability for soil-structure interaction system using modified ANFIS by hybrid of FCM-FPSO. Asian J. Civil Eng. 2012;13:1-27.
  20. Lin KP, Pai PF, Lu YM, Chang PT. Revenue forecasting using a least-squares support vector regression model in a fuzzy environment. Inform. Sci. 2013;220:196-209. https://doi.org/10.1016/j.ins.2011.09.003
  21. Shancita I, Masjuki HH, Kalam MA, Rizwanul Fattah IM, Rashed MM, Rashedul HK. A review on idling reduction strategies to improve fuel economy and reduce exhaust emissions of transport vehicles. Energ. Convers. Manage. 2014;88:794-807. https://doi.org/10.1016/j.enconman.2014.09.036