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Analysing the intermeshed patterns of road transportation and macroeconomic indicators through neural and clustering techniques

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As is widely acknowledged, the transportation of goods by road can, in one way or another, be linked to a range of macroeconomic indicators. A hybrid artificial intelligence system is proposed in this paper to analyse the interaction between transportation patterns and the economy. The temporal patterns of road transportation and macroeconomic trends are studied, by combining the use of both (supervised and unsupervised) neural networks and clustering techniques. The proposed system is validated, by establishing links between road transportation data from Spain and macroeconomic trends over 6 years (2011–2017). The results reveal an interesting inner structure of the data, through data visualizations of intermeshed relations between road transportation patterns and macroeconomic indicators. The same data structure was also visible in the output of the clustering techniques. Furthermore, a number of high-quality predictions were advanced by processing the road transportation data as time series, and forecasting the future values of the main series. These results demonstrated the validity of the proposed linkage between road transportation data and macroeconomic indicators.

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We are grateful for the complete datasets of the Permanent Survey of Goods Transport by Road (Encuesta Permanente de Transporte de Mercancías por Carretera) facilitated by the General Sub-Directorate of Economic Studies and Statistics of the Ministry of Development (Subdirección General de Estudios Económicos y Estadísticas del Ministerio de Fomento) of Spain.

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Correspondence to Álvaro Herrero.

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Alonso de Armiño, C., Manzanedo, M.Á. & Herrero, Á. Analysing the intermeshed patterns of road transportation and macroeconomic indicators through neural and clustering techniques. Pattern Anal Applic (2020).

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  • Artificial intelligence
  • Exploratory projection pursuit
  • Clustering
  • Forecast
  • Economy
  • Road transportation