WIT Press


Neural Networks To Estimate Pollutant Levels In Canyon Roads

Price

Free (open access)

Paper DOI

10.2495/UT010361

Volume

52

Pages

8

Published

2001

Size

692 kb

Author(s)

S. Amoroso & M. Migliore

Abstract

Many data can be exchanged between different places and in short time by using the new technology. New researches are developing in urban traffic management in order to elaborate data coming from the network in real time and to suggest on-line solutions of many problems. Also in the environmental field the new technology can be used to develop an on-line air quality monitoring system by elaborating data. In this paper neural networks have been used in order to develop a model able to estimate in real time the roadside pollutant levels in a canyon road by elaborating weather and traffic conditions. Neural networks can carry out quantitative estimations in real time and with a higher precision than classical analytical models. The survey is done in a Sicilian town. The vehicular flows, desegregated in dif- ferent categories, and the

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