WIT Press


KEY-COST DRIVERS SELECTION IN LOCAL PUBLIC BUS TRANSPORT SERVICES THROUGH MACHINE LEARNING

Price

Free (open access)

Paper DOI

10.2495/UT170141

Volume

176

Pages

12

Page Range

155 - 166

Published

2017

Size

450 kb

Author(s)

ALESSANDRO AVENALI, GIUSEPPE CATALANO, TIZIANA D’ALFONSO, GIORGIO MATTEUCCI, ANDREA MANNO

Abstract

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.

Keywords

standard costs, local public transport, fiscal federalism, cost drivers, machine learning