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Traffic parameters estimation to predict road side pollutant concentrations using neural networks

Lookup NU author(s): Dr Fabio Galatioto

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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.

Publication metadata

Author(s): Galatioto F, Zito P

Publication type: Article

Publication status: Published

Journal: Environmental Modeling and Assessment

Year: 2009

Volume: 14

Issue: 3

Pages: 365-374

ISSN (print): 1420-2026

ISSN (electronic): 1573-2967

Publisher: Springer Netherlands

URL: http://dx.doi.org/10.1007/s10666-007-9129-z

DOI: 10.1007/s10666-007-9129-z

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