1 research outputs found
Automated classification of urban locations for environmental noise impact assessment on the basis of road-traffic content
Urban and road planners must take right decisions related to urban traļ¬c management
and controlling noise pollution. Their assessments and resolutions have important consequences on the
annoyance of population exposed to road-traļ¬c-noise and controlling other environmental pollutants
(e.g. NOx or ultraļ¬ne particles emitted by heavy vehicles). One of the key decisions is the
selection of which noise control actions should be taken in sensitive areas (residential or
hospital areas, school areas etc), that could include costly measures such as reducing the overall
traļ¬c, banning or reducing traļ¬c of heavy vehicles, inspection of motorbikes sound emission, etc.
For an eļ¬cient decision-making in noise control actions, it is critical to classify a given
location in a sensitive area according to the different prevailing traļ¬c conditions.
This paper outlines an expert system aimed to help urban planners to classify urban locations based
on their traļ¬c composition. To induce knowledge into the system, several machine learning
algorithms are used, based on multi-layer Perceptron and support vector machines with sequential
minimal optimization. As input variables for these algorithms, a combination of environment
variables was used. For the development of the classiļ¬cation models, four feature selection
techniques, i.e., two subset evaluation (correlation-based feature-subset selection and
consistency-based subset evaluation) and two at- tribute evaluation (ReliefF and minimum redundancy
maximum relevance) were implemented to reduce the modelsā complexity. The overall procedure was
tested on a full database collected in the city of Granada (Spain), which includes urban
locations with road-traļ¬c as dominant noise source. Among all the possibilities tested, support
vector machines based models achieves the better results in classifying the considered urban
locations into the 4 categories observed, with values of average weighted F-measure and Kappa
statistics (used as indicators) up to 0.9 and 0.8. Regarding the feature selection techniques,
attribute evaluation algorithms (ReliefF and mRMR) achieve better classiļ¬cation results than subset
evaluation algorithms in reducing the model complexity, and so relevant environmental variables
are chosen for the proposed procedure. Results show that these tools can be used for addressing a
prompt assessment of potential road-traļ¬c-noise related problems, as well as for
gathering information in order to
take more well-founded actions against urban road-traļ¬c noise