This paper presents a modified Network DEA model (NDEA) to measure the performance of intermodal freight transport (IFT) chains and to find the sources of inefficiencies. The model addresses two challenges in the application of NDEA to the IFT domain: first, the chains may differ in the number of divisions (i.e., transshipment/transportation activities); and second, one needs to define a relevant intermediate service. For purposes of illustration, the model is applied to a particular European IFT network for which the inefficient transport/transshipment segments are identified and discussed.@en

Road transport has been the dominant modality for hinterland freight transport in the last two decades. This has resulted in congestion, air pollution and other external effects such as noise nuisance. Intermodal Freight Transport (IFT) as an alternative to road transport has been stimulated by the European Commission. In particular, there is a target of shifting more than 50% of freight being transported further than 300 km by road to IFT by 2050. However, despite all efforts, and running different programs, the market share of IFT is still quite limited. Assuming that having a competitive market, and improving the performance of the IFT service will result in higher market share for IFT service, this thesis analyzes the market structure and the performance of the IFT service at the network-level.@en

To cope with an intense and competitive environment, intermodal freight transport operators have increasingly adopted business practices —like horizontal and vertical business integration—which aim to reduce the operational costs, increase the profit margins, and improve their competitive position in the market. These strategies and business practices could potentially affect the competition level in the IFT market by increasing the market concentration. The impact can be on the separate submarkets (e.g., transshipment market or main-haulage market) or the whole market for IFT services at the network level. To investigate the impact of these business practices on the market structure of IFT networks, we present a model to analyze the market structure of IFT submarkets and extend the results to the network level. Using this multi-level market analysis model, we can evaluate the decisions made by firms and the market outcomes that result. The application of the presented model is also illustrated using a numerical example. The numerical example shows, for instance, that the impact of a merger, as a business practice, on the competition level in an IFT market —and its submarkets— depends on the merger type (horizontal and vertical). Furthermore, different indicators that “represent” market structure and competition, might react differently to a merger in an IFT network.

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The analysis of market structure and concentration measures for the Intermodal Freight Transport (IFT) market is important to avoid market failure and to find the areas for policy making to promote IFT market share. This analysis can be performed for separate segments, for example, the market for transshipment service or the market for main-haulage service. However, due to the multistage characteristic of IFT service, the segmental analysis gives an incomplete view of the IFT market at the network level. In a previous paper (Saeedi et al., 2017), we present the Intermodal Freight Transport Market Structure (IFTMS) model to conduct a network-based study of the IFTMS in which distinctive actors (i.e., pre/post haulage operators, terminals, rail/barge operators, transport chains, and corridors) are competing at different levels inside distinctive markets to deliver an integrated IFT service. There are two main challenges in the application of IFTMS model in real cases, for example, the European IFT network. First, the definition of the geographical and spatial border of the transshipment market areas is needed to determine which actors are potentially competing for a specific service demand. The second challenge is the lack of disaggregated data and the consistency of existing data in nodes (i.e., the transshipment areas) and links (i.e., the rail and barge operators). To cope with these challenges, we develop a four-step methodology in which a model-based approach is used to define the geographic boundaries of the transshipment submarkets and provide detailed and consistent data for market analysis. We also apply the IFTMS model to study the market structure of European intermodal network. Our analysis shows that the majority of transshipment markets as well as main-haulage markets are highly concentrated markets. The corridor markets – which include the IFT chains – are unconcentrated markets. Furthermore, the majority of corridors in the European Union are inside highly concentrated origin-destination markets.

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This paper analyses structures of the inland terminal markets that constitutes one of the main parts of the intermodal freight transport supply chains markets, by developing two different heuristic approaches to define the market. The main questions that this paper will answer are: 1. how can the terminal market be defined, 2. how can limited data about terminals be used to analyse the market structure, and 3. what are the structures of different terminal submarkets in the European Union. Although a lot of work has been done on intermodal freight transport, little has been done on quantitative analysis of intermodal markets, especially intermodal terminal markets. This paper develops two different approaches comparatively to define inland terminal markets. The first approach segments the market using the distance of terminals from central points of demand, while the second approach uses the aggregate trade in each of the demand areas. The results of the two different approaches have been used to validate the approaches, and show that the both approaches could be used in situations we have limited data. By using concentration indexes to calculate the market structures of 18 main areas in the European Union, we find that the inland terminal markets in EU are oligopolistic markets, characterized mainly by high concentration. The north of EU shows less concentration than the central & southern regions. According to the second approach, the terminal markets have higher degree of concentration, in comparison to the first approach. In some market areas, barge terminals also play a role which results in lower concentration levels. The structure of terminal markets suggests that terminals have a favorable negotiation power in the intermodal freight transport supply chains, which could be used as an important parameter to construct coordinated intermodal freight transport supply chains, because coordination in supply chains can be achieved by creating incentives for the parties in the chain. However, the negotiating power of supply chain actors plays a role as well.@en