Traffic Pollution Assessment Using Artificial Neural Network and Multivariate Analysis

Mario De Luca, Daiva Žilionienė, Saulius Gadeikis, Gianluca Dell’Acqua


The work addressed a study on pollution caused by traffic on the highway. In particular, it was considered the concentration of pollutant, resulting from the passage of vehicles on the freeway. Five different stations (sensors and samples) used to collect data. The data collection period around six months. Also, the following parameters were detected: wind speed and direction, temperature and traffic flow rate. Data processed with Multivariate Analysis and Artificial Neural Network approach. The best model it obtained with Artificial Neural Network approach. In fact, this model presented the best fit to the experimental data.


Artificial Neural Network; concentration of pollutant; Multivariate Analysis; traffic flow rate; wind speed and direction; temperature.

Full Text:



Briggs, G. A. 1967. Plume Rise: a Critical Survey, in Proc. of the USAEC Meteorological Information Meeting Held at Chalk River Nuclear Laboratories. 11–14 September, 1967, Chalk River Ontario, Canada. 2787: 1–21.

Chart-asa, C.; Gibson, J. M. 2015. Health Impact Assessment of Traffic-Related Air Pollution at the Urban Project Scale: Influence of Variability and Uncertainty, Science of the Total Environment 506–507: 409–421.

Drozdowicz, B.; Benz, S. J.; Santa Cruz, A. S. M.; Scenna, N. J. 1997. A Neural Network Based Model for the Analysis of Carbon Monoxide Contamination in the Urban Area of Rosario, Transactions on Ecology and the Environment 15: 677–689.

Gardner, M. W.; Dorling, S. R. 1998. Artificial Neural Networks (the Multilayer Perceptron) – a Review of Applications in the Atmospheric Sciences, Atmospheric Environment 32 (14–15): 2627–2636.

Gualtieri, G.; Tartaglia, M. 1997. A Street Canyon Model for Estimating NOx Concentrations due to Road Traffic, in International Conference on Measurements and Modelling in Environmental Pollution. 211–220.

Johansson, B. 1998. Will New Technology be Sufficient to Solve the Problem of Air Pollution Caused by Swedish Road Transport?, Transport Policy 5(4): 213–221.

Moseholm, L.; Silva, J.; Larson, T. 1996. Forecasting Carbon Monoxide Concentration Near a Sheltered Intersection Using Video Traffic Surveillance and Neural Networks, Transportation Research Part D: Transport and Environment 1(1): 15–28.

Pasquill, F. 1961. The Estimation of the Dispersion of Windborne Material, Meteorological Magazine 90(1063): 33−49.

Raimondi, P. M.; Rando, F.; Vitale, M. C.; Calcara, A. M. V. 1997. Short-Time Fuzzy DAP Predictor for Air Pollution due to Vehicular Traffic, WIT Transactions on Ecology and the Environment 19.

Sharan, M.; Yadav, A. K.; Singh, M. P.; Agarwal, P.; Nigam, S. 1996. A Mathematical Model for the Dispersion of Air Pollutants in Low Wind Conditions, Atmospheric Environment 30(8): 1209–1220.

Shorshani, M. F.; André, M.; Bonhomme, C.; Seigneur, C. 2015. Modelling Chain for the Effect of Road Traffic on Air and Water Quality: Techniques, Current Status and Future Prospects, Environmental Modelling & Software 64: 102–123.

Singh, M. P.; Yadav, A. K. 1996. Mathematical Model for Atmospheric Dispersion in Low Winds with Eddy Diffusivities as Linear Functions of Downwind Distance, Atmospheric Environment 30(7): 1137–1145.

Stockie, J. M. 2011. The Mathematics of Atmopheric Dispersion Modeling, SIAM Review 53(2): 349–372.

Tanaka, K.; Sano, M.; Watanabe, H. 1992. Identification and Analysis of Fuzzy Model for Air Pollution – an Approach to Self-Learning Control of CO Concentration, in Proc. of the International Conference on Industrial Electronics, Control, Instrumentation, and Automation on IEEE, Power Electronics and Motion Control, 13 November, 1992. 1431–1436.

Tao, Y.; Xinmiao, Y. 1998. Fuzzy Comprehensive Assessment, Fuzzy Clustering Analysis and Its Application for Urban Traffic Environment Quality Evaluation, Transportation Research Part D: Transport and Environment 3(1): 51–57.

Žilionienė, D.; De Luca, M.; Dell'Acqua, G.; Lamberti, R.; Bian¬cardo, S. A.; Russo, F. 2014. Evaluating Freeway Traffic Noise Using Artificial Neural Network, in Proc. of the International Conference “Environmental Engineering”, 22–23 May, 2014, Vilnius, Lithuania. 9 p.

DOI: 10.3846/bjrbe.2017.07


  • There are currently no refbacks.

Copyright (c) 2017 Vilnius Gediminas Technical University (VGTU) Press Technika