A novel sectoral group analytic hierarchy process model with explicit market share – Understanding policy gaps in the rail freight market

Abstract

Rail freight policy should reflect both the current and foreseen situations of the certain national rail freight market. The situation analysis is often based on expert evaluations, in many cases with the participation of the market players themselves, and their synthesized group opinion is the basis of policy-making. However, the creation of opinion synthesis has not considered the market power of the players so far, and a clear research gap exists on how to properly addressing weights to the respondent experts in the group of evaluators to gain a realistic image on the present and future of the rail freight market. The objective of this paper is to identify problems, risks, and development potential in an EU national rail freight market by an expert survey based new methodology (Sectoral Group Analytic Hierarchy Process, SGAHP) that assigns different decision-maker weights in the respondent group based on the different market power of the players to gain a clear and overall image on the examined market. As a case study, a survey has been conducted in Hungary involving the significant representatives of the national rail freight. Results show that the recruitment and training of human resource is a common problem, while supporting single wagon traffic is meaningless for all players. However, big companies prioritise the reduction of locomotive maintenance time, while small ones strive to have own maintenance facilities. As an implication, the proposed model might help state decision-makers in customizing financial or other support to efficiently increase the competitiveness of the sector, as well as the rail companies to better adopt to the situation. The proposed new model has been proven successful not only from the aspect of robustness and sensitivity, but also of recommending practical modifications in rail freight to transport planners both on national and EU levels.