Automated classification of urban locations for environmental noise impact assessment on the basis of road-traffic content

Torija Martinez, AJ; Ruiz, DP

Abstract

Urban and road planners must take right decisions related to urban traffic management
and controlling noise pollution. Their assessments and resolutions have important consequences on the
annoyance of population exposed to road-traffic-noise and controlling other environmental pollutants
(e.g. NOx or ultrafine 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
traffic, banning or reducing traffic of heavy vehicles, inspection of motorbikes sound emission, etc.
For an efficient decision-making in noise control actions, it is critical to classify a given
location in a sensitive area according to the different prevailing traffic conditions.
This paper outlines an expert system aimed to help urban planners to classify urban locations based
on their traffic 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 classification 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 models’ complexity. The overall procedure was
tested on a full database collected in the city of Granada (Spain), which includes urban
locations with road-traffic 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 classification 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-traffic-noise related problems, as well as for
gathering information in order to
take more well-founded actions against urban road-traffic noise.

Citation

Torija Martinez, A., & Ruiz, D. (2016). Automated classification of urban locations for environmental noise impact assessment on the basis of road-traffic content. Expert systems with applications, 53, 1-13. https://doi.org/10.1016/j.eswa.2016.01.011

• Journal Article Type: Article
• Acceptance Date: Jan 11, 2016
• Online Publication Date: Jan 15, 2016
• Publication Date: Jul 1, 2016
• Deposit Date: Dec 3, 2019
• Journal: Expert Systems with Applications
• Print ISSN: 0957-4174
• Publisher: Elsevier
• Volume: 53
• Pages: 1-13
• DOI: https://doi.org/10.1016/j.eswa.2016.01.011
• Publisher URL: https://doi.org/10.1016/j.eswa.2016.01.011
• Related Public URLs: https://www.sciencedirect.com/journal/expert-systems-with-applications
• Additional Information: Funders : University of Malaga and the European Commission, seventh Framework Programme for R & D of the EU, granted within the People Programme, «Co-funding of Regional, National and International Programmes» (COFUND).;“Campus de Excelencia Internacional BIOTIC Granada” (CIE BioTic) of Spain;Ministerio de Economía y Competitividad of Spain; Grant Number: Agreement Grant No. 246550; Grant Number: P-CP-27; Grant Number: TEC2012-38883-C02-02