Scientific papers related to intermodal transport research are reviewed in the article. Attention is focused on classification of scientific issues of intermodality. The methods, algorithms, models used for intermodal transport research are described. The areas of huge scientific focus and poor areas in intermodality research are highlighted.

The measurement of terminal productivity is the issue of extreme importance to both terminal owners and management and customers. As the sector of transport is highly intensive in terms of investments into the infrastructure, the productivity of a terminal may play a crucial role in competing with other terminals. Productivity is defined in terms of inputs and output. The majority of the available studies, wherein this issue is addressed, are generally focused on the determination of functional dependence between inputs and output using the method of regressive analysis. The present article provides an insight into the Data Envelopment Analysis method as a tool for measuring productivity. This technique enables a rather accurate evaluation of terminal productivity by means of comparative analysis, which, in fact, appears to be the only feasible alternative in cases where statistic data required for performing regressive analysis is lacking.

The article presents a valuable concept seeking to solve the problem of demand uncertainty in intermodal transport. Regular traffic is quite important for moving containers, trailers and swap bodies. To keep regularity with uncertain demand means to have backlogs or empty space. Both of them are inefficient from an economical point of view. In practice, a day‐by‐day demand forecast is meaningful only for the next two or three days. This poses serious allotment management problems to freight forwarders and shippers since long‐term contract allotments need to be planned many months ahead. The article presents a stochastic dynamic programming model for a short‐term allotment planning a model that would be very valuable for implementing intermodal solutions. The presented model evaluates optimal cost policy based on the economic trade‐off between the cost of backlogged shipment and the cost of acquiring additional allotment.

Research background: The European Green deal set by the European Commission has launched new business models in sustainable development. Major contributions are expected in the road transport sector; as far as conventional internal combustion creates significant input in Green House Gas emission inventories. Each EU member state has an obligation to reduce GhG emission by accelerating Electric Vehicle development. In order to foster growth of EVs, there is the need of significant investment into charging infrastructures. The article propose the model of forecasting of investment based on the forecast of the growth of the amount of electric vehicles and their demand on energy. The model includes the behaviouristic approach based on the total cost of ownership model as well as calculations of efficient usage of EV charging points. The model takes into account all types of vehicles including personal and commercial, freight and passenger. Purpose: The aim of this article is to present a complex model for forecasting the required investments based on the fore-cast of the increase in the number of electric vehicles and their demand on energy and investments. Research methodology: The general algorithm of forecasting consists of several consecutive phases: (1) Forecasting the number of electric vehicles, (2) Forecasting the energy needed for electric vehicles, based on the forecast (1) and the predicted usage level of these vehicles. (3) Forecasting the charging station number with the expected technical capacities and characteristics of these charging stations based on the forecasts (1) and (2). (4) Forecasting the need to upgrade the low-voltage grid based on the forecast (3). (5) Calculating the total investment needed based on the results of the forecasts (3) and (4). The main limitations of the study are related to the statistics available for modelling and human behaviour uncertainty, especially in the evaluation impact of measures to foster use of electric vehicles. Results: The findings of the Lithuanian case analysis, which is expressed in three scenarios, focuses on two trends. The most promising scenario projects 319,470 electric vehicles by 2030 which will demand for 1.09 TWh of electricity, representing 8.4–9.9 percent of the total energy consumption in the country. It requires EUR 230, million in the low-voltage grid and EUR 209, million in the charging stations. Novelty: The scientific problem is that the current approach on the forecasting of electric vehicles is too abstract, forecast models cannot be transferred from country to country. This article proposes a model of forecasting investments based on the forecast of the increase in the number of electric vehicles and their demand on energy. The model includes the behaviouristic approach based on the total cost of ownership model as well as calculations of efficient usage of EV charging points. The model takes into account all types of vehicles including personal and commercial, freight and passenger. The article has proven that statistics-based forecasting gives very different results compared to the objective function and to the evaluation of the effects of measures. This has not been compared in previous studies.

The author of the article describes the theoretical assumptions of the DEA method used for measuring the productivity of airports described in the article ‘Research into the methods of analysing the productivity indicators of transport terminals’ (Jaržemskienė 2009). The essential insights presented in the above mentioned paper reveal that Data Envelopment Analysis (DEA) is a relatively new ‘data oriented’ approach to evaluating the performance of the so-called Decision Making Units (DMU) that convert multiple inputs into multiple outputs. The article focuses on the findings of the study carried out by the author in accordance with those assumptions. Research represents the Data Envelopment Analysis (DEA) method testing 15 selected airports situated in the Baltic Region, including Vilnius, Kaunas, Palanga, Riga, Tallinn, St. Petersburg, Helsinki, Turku, Stockholm, Malme, Copenhagen, Hamburg, Gdansk, Warsaw and Minsk. Airport productivity indicators are ranked considering importance and using the method of Delphi expert survey made of two rounds. The author presented the following indicators (expressed as ‘ratio’) as the major ones estimated by PAX/LAND, AIR/LAND, PAX/AIR, PAX/RW, PAX/RWA, GA and INTER experts. The succeeding indicators were introduced by PAX/TERMAREA, PAX/GATES, AIR/RW, AIR/RWA, AIR/TERMAREA, AIR/GATES, FR/LAND and FR/RW. 10 indicators were accepted as the most important and selected from the current set in the following sequence: AIR/LAND, AIR/RW, PAX/RW, PAX/LAND, AIR/RWA, PAX/AIR, PAX/RWA, AIR/TERMAREA, PAX/GATES and PAX/TERMAREA. AIR/LAND and AIR/RW were submitted as two main indicators. The acronyms are explained as follows: LAND – airport area, RWA – runway length, PAX – the number of passengers, AIR – the number of aircraft take-offs and landings, RW – the number of runways, GATES – the number of gates, FR – the amount of freight served, TERMAREA – the area of passenger terminal, GA – a general aviation market share of airport served aircraft by percentage, INTER – the percentage of international passengers considering all passengers served by airports. After two key productivity indicators were chosen conducting the expert survey, airport productivity was compared applying the DEA method.