Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks

Traffic emissions are considered one of the leading causes of environmental impact in megacities and their dangerous effects on human health. This paper presents a hybrid model based on data mining and GIS models designed to predict vehicular Carbon Monoxide (CO) emitted from traffic on the New Klang Valley Expressway, Malaysia. The hybrid model was developed based on the integration of GIS and the optimized Artificial Neural Network algorithm that combined with the Correlation based Feature Selection (CFS) algorithm to predict the daily vehicular CO emissions and generate prediction maps at a microscale level in a small urban area by using a field survey and open source data, which are the main contributions to this paper. The other contribution is related to the case study, which represents the spatial and quantitative variations in the vehicular CO emissions between toll plaza areas and road networks. The proposed hybrid model consists of three steps: the first step is the implementation of the correlation-based Feature Selection model to select the best model’s predictors; the second step is the prediction of vehicular CO by using a multilayer perceptron neural network model; and the third step is the creation of micro scale prediction maps. The model was developed using six traffic CO predictors: number of vehicles, number of heavy vehicles, number of motorbikes, temperature, wind speed and a digital surface model. The network architecture and its hyperparameters were optimized through a grid search approach. The traffic CO concentrations were observed at 15-min intervals on weekends and weekdays, four times per day. The results showed that the developed model had achieved validation accuracy of 80.6 %. Overall, the developed models are found to be promising tools for vehicular CO simulations in highly congested areas.

[1]  R. Harley,et al.  Impact of Oxygenated Gasoline Use on California Light-Duty Vehicle Emissions , 1996 .

[2]  Abdallah Shanableh,et al.  Modeling roadway traffic noise in a hot climate using artificial neural networks , 2017 .

[3]  Archontoula Chaloulakou,et al.  Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens. , 2003, The Science of the total environment.

[4]  Mukesh Sharma,et al.  Passive measurement of NO2 and application of GIS to generate spatially-distributed air monitoring network in urban environment , 2015 .

[5]  Jean-Michel Guldmann,et al.  Land-use regression panel models of NO2 concentrations in Seoul, Korea , 2015 .

[6]  Marija Zlata Boznar,et al.  A neural network-based method for short-term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain , 1993 .

[7]  Nico Vandaele,et al.  A QUEUEING BASED TRAFFIC FLOW MODEL , 2000 .

[8]  Juan Manuel de Andrés,et al.  Microscale traffic simulation and emission estimation in a heavily trafficked roundabout in Madrid (Spain). , 2016, The Science of the total environment.

[9]  G. Righini,et al.  GIS based assessment of the spatial representativeness of air quality monitoring stations using pollutant emissions data , 2014 .

[10]  Anil Kumar Singh,et al.  Performance evaluation of CALINE 4 dispersion model for an urban highway corridor in Delhi , 2013 .

[11]  C. Sioutas,et al.  Traffic-related Air Pollution and Blood Pressure in Elderly Subjects With Coronary Artery Disease , 2010, Epidemiology.

[12]  Norun Hjertager Krog,et al.  Road traffic noise and markers of obesity - a population-based study. , 2015, Environmental research.

[13]  K. Fameli,et al.  Development of a road transport emission inventory for Greece and the Greater Athens Area: effects of important parameters. , 2015, The Science of the total environment.

[14]  Mohammad Hossein Fazel Zarandi,et al.  Interval type-2 fuzzy expert system for prediction of carbon monoxide concentration in mega-cities , 2012, Appl. Soft Comput..

[15]  Michael Brauer,et al.  Application of land use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter. , 2007, Environmental science & technology.

[16]  Yafeng Yin,et al.  Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach , 2009 .

[17]  P. J. García Nieto,et al.  Analysis of lead times of metallic components in the aerospace industry through a supported vector machine model , 2010, Math. Comput. Model..

[18]  S. Z. Mousavi,et al.  GIS-based spatial prediction of landslide susceptibility using logistic regression model , 2011 .

[19]  Gordon Mitchell,et al.  TEMMS: an integrated package for modelling and mapping urban traffic emissions and air quality , 2002, Environ. Model. Softw..

[20]  Giuseppe Loprencipe,et al.  Procedure to Determine the Geometry of Road Alignment Using GPS Data , 2012 .

[21]  Esmaeil Fatehifar,et al.  Using ANN and EPR models to predict carbon monoxide concentrations in urban area of Tabriz , 2015 .

[22]  Mark A. Hall,et al.  Correlation-based Feature Selection for Machine Learning , 2003 .

[23]  P. J. García Nieto,et al.  PM10 concentration forecasting in the metropolitan area of Oviedo (Northern Spain) using models based on SVM, MLP, VARMA and ARIMA: A case study. , 2018, The Science of the total environment.

[24]  Dejan J. Sobajic,et al.  Neural-net computing and the intelligent control of systems , 1992 .

[25]  Robert A Harley,et al.  High-resolution mapping of sources contributing to urban air pollution using adjoint sensitivity analysis: benzene and diesel black carbon. , 2015, Environmental science & technology.

[26]  M. Goldberg,et al.  Postmenopausal Breast Cancer Is Associated with Exposure to Traffic-Related Air Pollution in Montreal, Canada: A Case–Control Study , 2010, Environmental health perspectives.

[27]  M. Gardner,et al.  Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London , 1999 .

[28]  Deepak Singh,et al.  Statistical modeling of O3, NOx, CO, PM2.5, VOCs and noise levels in commercial complex and associated health risk assessment in an academic institution. , 2016, The Science of the total environment.

[29]  Hamid Reza Ranjbar,et al.  3 D Analysis and Investigation of Traffic Noise Impact from Hemmat Highway Located in Tehran on Buildings and Surrounding Areas , 2013 .

[30]  Hamid Reza Ranjbar,et al.  3D Analysis and Investigation of Traffic Noise Impact from Hemmat Highway Located in Tehran on Buildings and Surrounding Areas , 2012 .

[31]  Izabela Rojek.  Technological process planning by the use of neural networks , 2017, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[32]  Martina S. Ragettli,et al.  Statistical modeling of the spatial variability of environmental noise levels in Montreal, Canada, using noise measurements and land use characteristics , 2016, Journal of Exposure Science and Environmental Epidemiology.

[33]  J. Hooyberghs,et al.  A neural network forecast for daily average PM10 concentrations in Belgium , 2005 .

[34]  Yun Fat Lam,et al.  Developing a statistical based approach for predicting local air quality in complex terrain area , 2017 .

[35]  Nazmul Sohel,et al.  Estimation of sulfur dioxide air pollution concentrations with a spatial autoregressive model , 2013 .

[36]  S. Vardoulakis,et al.  Assessment of microscale spatio-temporal variation of air pollution at an urban hotspot in Madrid (Spain) through an extensive field campaign , 2016 .

[37]  J Tomić,et al.  Assessment of traffic noise levels in urban areas using different soft computing techniques. , 2016, The Journal of the Acoustical Society of America.

[38]  E. Garshick,et al.  Residence Near a Major Road and Respiratory Symptoms in U.S. Veterans , 2003, Epidemiology.

[39]  K S Chen,et al.  Motorcycle emissions and fuel consumption in urban and rural driving conditions. , 2003, The Science of the total environment.

[40]  Siti Halipah Ibrahim,et al.  Carbon monoxide levels along roadway , 2007 .

[41]  P. Goyal,et al.  Present scenario of air quality in Delhi: a case study of CNG implementation , 2003 .

[42]  Edward Ng,et al.  Developing Street-Level PM2.5 and PM10 Land Use Regression Models in High-Density Hong Kong with Urban Morphological Factors. , 2016, Environmental science & technology.

[43]  Ming Cai,et al.  A new probability statistical model for traffic noise prediction on free flow roads and control flow roads , 2016 .

[44]  Vlad Isakov,et al.  Evaluation of land-use regression models used to predict air quality concentrations in an urban area , 2010 .

[45]  M. Ketzel,et al.  Modelling traffic and air pollution in an integrated approach – the case of Munich , 2014 .

[46]  Gavin C. Cawley,et al.  Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki , 2003 .

[47]  Yu Zheng,et al.  U-Air: when urban air quality inference meets big data , 2013, KDD.

[48]  Hsiao-Tien Pao,et al.  Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model , 2012 .