Neural based contingent valuation of road traffic noise

- An artificial neural network approach for road noise valuing purposes is presented.
- The ANN model presents 85.7% more accuracy than an ordered probit econometric model.
- The model has a notable generalisation ability, although the subjectivity of the data.

In this paper, we present a new approach to value the willingness to pay to reduce road noise annoyance using an artificial neural network ensemble. The model predicts, with precision and accuracy, a range for willingness to pay from subjective assessments of noise, a modelled noise exposure level, and both demographic and socio-economic conditions. The results were compared to an ordered probit econometric model in terms of the performance mean relative error and obtained 85.7% better accuracy. The results of this study show that the applied methodology allows the model to reach an adequate generalisation level, and can be applicable as a tool for determining the cost of transportation noise in order to obtain financial resources for action plans.