Abstract
This work presents a neural network based model for inferring air quality from traffic measurements. It is important to obtain information on air quality in urban environments in order to meet legislative and policy requirements. Measurement equipment tends to be expensive to purchase and maintain. Therefore, a model based approach capable of accurate determination of pollution levels is highly beneficial. The objective of this study was to develop a neural network model to accurately infer pollution levels from existing data sources in Leicester, UK. Neural Networks are models made of several highly interconnected processing elements. These elements process information by their dynamic state response to inputs. Problems which were not solvable by traditional algorithmic approaches frequently can be solved using neural networks. This paper shows that using a simple neural network with traffic and meteorological data as inputs, the air quality can be estimated with a good level of generalisation and in near real-time. By applying these models to links rather than nodes, this methodology can directly be used to inform traffic engineers and direct traffic management decisions towards enhancing local air quality and traffic management simultaneously.
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References
Health effects of particulate matter: Policy implications for countries in Eastern Europe, Caucasus and Central Asia. World Health Organization, Technical report (2013)
Review of evidence on health aspects of air pollution. Technical report, WHO/Europe (2013)
Fisher, B.E.: Fuzzy approaches to environmental decisions: application to air quality. Environ. Sci. Policy 9(1), 22–31 (2005)
Grivas, G., Chaloulakou, A.: Artificial neural network models for prediction of PM10 hourly concentrations, in the greater area of Athens, Greece. Atmos. Environ. 40(7), 1216–1229 (2006)
Hornik, K., Stinchcombe, M., White, H., Tinchcombe, M.: Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)
Ibarra-Berastegi, G., Elias, A., Barona, A., Saenz, J., Ezcurra, A., Diaz de Argandoña, J.: From diagnosis to prognosis for forecasting air pollution using neural networks: air pollution monitoring in Bilbao. Environ. Model. Softw. 23(5), 622–637 (2008)
Kukkonen, J., 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. Atmos. Environ. 37(32), 4539–4550 (2003)
Leshno, M., Lin, V.Y., Pinkus, A., Schocken, S.: Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Netw. 6(6), 861–867 (1993)
Moore II, J.E., Mattingly, S.P., MacCarley, C.A., McNally, M.G.: Anaheim advanced traffic control system field operations test: a technical evaluation of scoot. Transp. Plann. Technol. 28(6), 465–482 (2005)
Nagendra, S.S., Khare, M.: Artificial neural network approach for modelling nitrogen dioxide dispersion from vehicular exhaust emissions. Ecol. Model. 190(1–2), 99–115 (2006)
Niska, H., Hiltunen, T., Karppinen, A., Ruuskanen, J., Kolehmainen, M.: Evolving the neural network model for forecasting air pollution time series. Eng. Appl. Artif. Intell. 17(2), 159–167 (2004)
Passow, B.N., Elizondo, D., Chiclana, F., Witheridge, S., Goodyer, E.: Adapting traffic simulation for traffic management: a neural network approach. In: 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), pp. 1402–1407. No. ITSC/IEEE, October 2013. http://ieeexplore.ieee.org/document/6728427/
Pelliccioni, A., Tirabassi, T.: Air dispersion model and neural network: a new perspective for integrated models in the simulation of complex situations. Environ. Model. Softw. 21(4), 539–546 (2006)
Pérez-Roa, R., Castro, J., Jorquera, H., Pérez-Correa, J., Vesovic, V.: Air-pollution modelling in an urban area: correlating turbulent diffusion coefficients by means of an artificial neural network approach. Atmos. Environ. 40(1), 109–125 (2006)
Pfeiffera, H., Baumbacha, G., Sarachaga-Ruiza, L., Kleanthousb, S., Poulidab, O., Beyaz, E.: Neural modelling of the spatial distribution of air pollutants. Atmos. Environ. 43(20), 3289–3297 (2009)
Sowlat, M.H., Gharibi, H., Yunesian, M., Mahmoudi, M.T., Lotfi, S.: A novel, fuzzy-based air quality index (FAQI) for air quality assessment. Atmos. Environ. 45(12), 2050–2059 (2011)
Viotti, P., Liuti, G., Di Genova, P.: Atmospheric urban pollution: applications of an artificial neural network (ANN) to the city of Perugia. Ecol. Model. 148(1), 27–46 (2002)
Zito, P., Chen, H., Bell, M.: Predicting real-time roadside CO and \({\rm NO}_2\) concentrations using neural networks. IEEE Trans. Intell. Transp. Syst. 9(3), 514–522 (2008)
Acknowledgment
The authors would like to thank Leicester City Council for their support and for providing the data for this study. The authors also thankfully acknowledge the grant of the Universidad de Málaga.
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Molina-Cabello, M.A., Passow, B.N., Dominguez, E., Elizondo, D., Obszynska, J. (2019). Infering Air Quality from Traffic Data Using Transferable Neural Network Models. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11506. Springer, Cham. https://doi.org/10.1007/978-3-030-20521-8_68
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