Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Traffic Parameters Estimation to Predict Road Side Pollutant Concentrations using Neural Networks
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.
Traffic Parameters Estimation to Predict Road Side Pollutant Concentrations using Neural Networks
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.
Traffic Parameters Estimation to Predict Road Side Pollutant Concentrations using Neural Networks
Galatioto, Fabio (Autor:in) / Zito, Pietro (Autor:in)
Environmental Modeling & Assessment ; 14 ; 365-374
13.11.2007
10 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Traffic Parameters Estimation to Predict Road Side Pollutant Concentrations using Neural Networks
| Springer Verlag | 2009
Traffic Parameters Estimation to Predict Road Side Pollutant Concentrations using Neural Networks
| Online Contents | 2007
Traffic Parameters Estimation to Predict Road Side Pollutant Concentrations using Neural Networks
| Online Contents | 2009
Impact of wind direction on near-road pollutant concentrations
| Elsevier | 2013