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.
DOI
|
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.
DOI
|
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. emission benefit of diesel (versus gasoline) powered vehicles. Environ. Sci. Technol. 2004;38:3217-3223.
DOI
|
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 |
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.
|
13 |
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.
DOI
|
14 |
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.
DOI
|
15 |
Karner AA, Eisinger DS, Niemeier DA. Near-roadway air quality: Synthesizing the findings from real-world data. Environ. Sci. Technol. 2010;44:5334-5344.
DOI
|
16 |
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.
DOI
|
17 |
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.
|
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.
DOI
|
19 |
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.
DOI
|
20 |
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.
|
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.
DOI
|