Key-cost drivers selection in local public bus transport services through machine learning

This paper is aimed at developing a workable model for the identification of key-cost drivers in the Italian Local Public Bus Transport (LPBT) sector. Disaggregated information about costs, technical characteristics and environmental characteristics have been collected by means of questionnaires sent to LPBT companies producing more than 500 million bus revenue kilometres in Italy in 2011. A supervised regression model is built by training a regularized Artificial Neural Network in order to determine the quantitative and qualitative characteristics that contribute to explaining the variability of the driving personnel and the unit cost of the fleet (which usually covers more than 50% of the total economic cost) and the remaining portion of the unit cost. The proposed models could be an effective and simple tool for local authorities to validate reserve prices in tender procedures.