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Traffic Parameters Estimation to Predict Road Side Pollutant Concentrations using Neural Networks

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An Erratum to this article was published on 21 July 2009

Abstract

The analysis aims to evaluate which is the most important among traffic parameters (flows, queues length, occupancy degree, and travel time) to forecast CO and C6H6 concentrations. The study area was identified by Notarbartolo Road and bounded by Libertà Street and Sciuti Street in the urban area of Palermo in Southern Italy. In this area, various loop detectors and one pollution-monitoring site were located. Traffic data related to the pollution-monitoring site immediately near the road link were estimated by Simulation of Urban MObility (SUMO) traffic microsimulator software using as input the flows measured by loop detectors on other links of road network. Traffic and weather data were used as input variables to predict pollutant concentrations by using neural networks. Finally, after a sensitivity analysis, it was showed that queues length were the mostly correlated traffic parameters to pollutant concentrations.

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References

  1. Amoroso, S., & Migliore, M. (2001). Neural networks to estimate pollutant levels in canyon roads. Urban Transport, 7, 381–388.

    Google Scholar 

  2. Bishop, C. M. (1995). Neural network for pattern recognition. Oxford: Oxford University Press.

    Google Scholar 

  3. Dorzdowicz, B., Benz, S. J., et al. (1997). A neural network based model for the analysis of carbon monoxide concentration in the urban area of Rosario. Southampton: Computational Mechanics Publications.

    Google Scholar 

  4. Dougherty, M. S., & Cobbett, M. R. (1997). Short term interurban traffic forecasts using neural networks. Internatinal Journal of Forecasting, 13, 21–31.

    Article  Google Scholar 

  5. Gadner, M. W., & Dorling, S. R. (1999). Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London. Atmospheric Environment, 33, 709–719.

    Article  Google Scholar 

  6. Grivas, G., & Chaloulakou, A. (2006). Artificial neural network models for prediction of PM10 hurly concentrations, in the Greater Area of Athens, Grece. Atmospheric Environment, 40, 1216–1229.

    Article  CAS  Google Scholar 

  7. Hornik, K., Stinchcombe, M., & White, H. (1989). Multi layer feed forward networks are universal approximators. Neural Networks, 2, 359–366.

    Article  Google Scholar 

  8. Karakitsios, P. S., Papaloukas, C. L., Kassomenos, P. A., & Pilidis, G. A. (2006). Assessment and predictionof benzene concentrations in a street canyon using artificial neural networks and deterministic models. Their response to “what if” scenario. Ecological Modelling, 193, 253–270.

    Article  CAS  Google Scholar 

  9. Krauß, S. (1998). Microscopic modeling of traffic flow: investigation of collision free vehicle dynamics, Ph.D. Thesis. Koln University.

  10. Leutzbach, W. (1988). Introduction to the theory of traffic flow. Berlin: Springer.

    Google Scholar 

  11. Moseholm, L., Silva, J., & Larson, T. (1996). Forecasting carbon monoxide concentrations near a sheltered intersection using video traffic surveillance and neural networks. Transportation Research D: Transport and Enivironment, 1, 15–28.

    Article  Google Scholar 

  12. Pelliccioni, A., & Tirabassi, T. (2006). Air dispersion model and neural network: A new perspective for integrated models in the simulation of complex situations. Environment Modelling & Software, 21, 539–546.

    Article  Google Scholar 

  13. Perez, P., & Trier, A. (2001). Prediction of NO and NO2 concentrations near a street with heavy traffic in Santiago, Chile. Atmospheric Environment, 35, 1783–1789.

    Article  CAS  Google Scholar 

  14. Shi, J. P., & Harrison, R. M. (1997). Regression modelling of hourly NOx and NO2 concentrations in urban air in London. Atmospheric Environment, 31, 4081–4094.

    Article  CAS  Google Scholar 

  15. Viotti, P., Liuti, G., & Di Genova, P. (2002). Atmospheric urban pollution: applications of an artificial neural network (ANN) to the city of Perugia. Ecological Modelling, 148, 27–46.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We would like to thank Engr. M. Vultaggio for availability of data provided by AMIA of Palermo.

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Authors and Affiliations

  1. Department of Transportation Engineering, University of Palermo, Viale delle Scienze, Edificio n. 8, 90128, Palermo, Italy

    Fabio Galatioto & Pietro Zito

  2. Dipartimento di Ingegneria dei Trasporti, Università degli Studi di Palermo, Viale delle Scienze, Edificio n. 8, 90128, Palermo, Italy

    Fabio Galatioto & Pietro Zito

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  1. Fabio Galatioto
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  2. Pietro Zito
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Correspondence to Pietro Zito.

Additional information

An erratum to this article can be found at http://dx.doi.org/10.1007/s10666-009-9198-2

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Galatioto, F., Zito, P. Traffic Parameters Estimation to Predict Road Side Pollutant Concentrations using Neural Networks. Environ Model Assess 14, 365–374 (2009). https://doi.org/10.1007/s10666-007-9129-z

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  • DOI: https://doi.org/10.1007/s10666-007-9129-z

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