A bottom-up cost model for electric railbased regional public transport services: The ITALIAN CONTEXT

When comparing the cost structure of different transport operators, tools standardising the expenses an ideal efficient operator would incur in producing a specific transport service are valuable instruments for private companies and public authorities. In this paper, we build a bottom-up cost model for electric-driven rail-based regional transport services. The proposed model includes (i) transport services production costs; (ii) maintenance costs; (iii) administrative costs and (iv) the cost of capital. Except for the expenses for electric traction, infrastructure costs have been excluded and considered, as frequently happens, upon an external infrastructure manager. The bottom-up approach, relying on engineering analysis of the production process, limits the influence of past inefficiencies, typical of methods based on historical data. The model is developed for a generic short-medium distance service; it is then calibrated on the Italian context, thanks to disaggregating data on production collected through questionnaires from Italian transport operators in 2012 (covering 95% of the national service). We realised two settings, representing respectively an average performance and an 'ideal' best practise. To show the potentiality of the tool for policy makers, we apply the model to four case studies and calculate the maximum economic compensation paid to transport operators for each of them. The cases mainly differ over three fundamental characteristics: number of stops along the route, frequency and commercial speed. The latter two result to be the most influential factors in defining the unit standard cost. Finally, we run numerical simulations to measure the marginal impact on efficiency obtained by modifying selected cost-driving variables and highlight the most promising interventions to enhance the performance gains. In particular, we consider both elements under the control of operators (e.g. vehicles and drivers productivity) and elements under the control of public authorities (e.g. average fleet age).