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RESEARCH PRODUCT
Traffic Parameters Estimation to Predict Road Side Pollutant Concentrations using Neural Networks
Fabio GalatiotoPietro ZitoMarco Miglioresubject
Pollutantgeographygeography.geographical_feature_categoryOccupancyArtificial neural networkMeteorologyPOLLUTANT CONCENTRATIONS NEURAL NETWORKSUrban areaTravel timeTransport engineeringWeather dataEnvironmental scienceSensitivity (control systems)QueueGeneral Environmental Sciencedescription
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 Liberta 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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2007-11-13 | Environmental Modeling & Assessment |