In the coming years, the share of hybrid electric vehicles is expected to grow significantly in personal transportation. Vehicles that can be charged from the electrical grid, such as plug-in hybrids, could introduce problems for the distribution network, especially if the vehicle adoption is spatially concentrated and the charging happens unmanaged. Thus, where and when the hybrid vehicle adoption occurs is an important question for policy makers and planners. Currently, promoted by the European Union directives such as INSPIRE and PSI, there is a trend of public sector data being harmonised and opened for free usage across the Europe. The vast amount of information in the various registers of the society has a huge and largely untapped potential for modelling societal and environmental phenomena. To discover correlations and relationships from such large databases, data mining methods can be useful. In this paper, we utilise a data mining approach to identify relationships from public sector data between hybrid vehicle adoption and small area level socio-demography. Based on the discovered model, we assess how favourable each area type is for the adoption. The approach combines Self-Organizing Map based data compression, a feature selection using Genetic Algorithm and a linear regression model.
['Jukka Saarenpää', 'Mikko Kolehmainen', 'Harri Niska']
Identifying socio-demographically favourable area types for early hybrid electric vehicle adoption through mining of public sector data