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

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. An erratum to this article can be found at     

De-Europeanising or disengaging? EU environmental policy and Brexit

ABSTRACT

The European Union (EU) has had a profound effect upon its members’ environmental policy. Even in the United Kingdom (UK), the EU’s most recalcitrant member state (historically labeled the ‘Dirty man of Europe’), environmental policy has been Europeanised. As the UK moves to the EU’s exit door it is timely to assess the utility of Europeanisation for understanding policy dynamics in the UK. Drawing upon interviews and extensive engagement with stakeholders, this article analyses the potential impact of Brexit upon environmental policy and politics. The analytical toolkit offered by de-Europeanisation is developed to identify the factors that drive and inhibit de-Europeanisation processes, thereby providing insights that may be applicable in other settings. Disengagement and policy stagnation are presented as more likely environmental outcomes of Brexit, with capacity emerging as a central explanatory variable.