The concept of transport corridors is marked by a concentration of freight traffic between major hubs and by relatively long distances of transport. Green transport corridors will reflect an integrated transport concept where short sea shipping, rail, inland waterways and road complement each other to enable the choice of environmentally friendly transport. The main aim of Green Intermodal Freight Transport (GIFT) project is to map, analyze, and evaluate the status of the transport sector in the selected transport network and propose new policies and strategies in infrastructure, processes, assets, Information and Communication Technologies, legislation, norms and harmonization/standardization issues, in order to promote innovative green intermodal freight transport corridors. The purpose of this paper is to present the central activities of the project, and the expected/ achieved results and outputs.

Recently, great attention has been paid to the uncertainties associated with Multi Regional Input-Output (MRIO) models related to the available data sources. We propose a new method based on the entropy maximization principle and fuzzy optimization, which takes explicitly into account the uncertainty embedded in available information. It allows to estimate jointly the values of production level, the trade coefficients and the final demand values assuming the availability of incomplete and/or approximate data on some elements of trade coefficients and of final demand of goods. The model, applied to real scale problem, shows good estimation performances and robustness in different scenario.

A crucial step in transportation planning process is the measure of systems efficiency. Many efforts have been made in this field in order to provide satisfactory answer to this problem. One of the most used methodologies is the Data Envelopment Analysis (DEA) that has been applied to a wide number of different situations where efficiency comparisons are required. The DEA technique is a useful tool since the approach is non-parametric, and can handle many output and input at the same time. In a lot of real applications, input and output data cannot be precisely measured. Imprecision (or approximation) may be originated from indirect measurements, model estimation, subjective interpretation, and expert judgment of available information. Therefore, methodologies that allow the analyst to explicitly deal with imprecise or approximate data are of great interest, especially in freight transport where available data as well as stakeholders’ behavior often suffer from vagueness or ambiguity. This is particularly worrying when assessing efficiency with frontier-type models, such as Data Envelopment Analysis (DEA) models, since they are very sensitive to possible imprecision in the data set. The specification of the evaluation problem in the framework of the fuzzy set theory allows the analyst to extend the capability of the traditional “crisp” DEA to take into account and, thus, to represent the uncertainty embedded in real life problems. The existing fuzzy approaches are usually categorized in four categories: a) the tolerance approaches; b) the defuzzification approaches c) the α- level based approaches; d) the fuzzy ranking. In this paper, we have explored the Fuzzy Theory-based DEA model, to assess efficiency measurement for transportation systems considering uncertainty in data, as well as in the evaluation result. In particular, the method is then applied to the evaluation of efficiency of container ports on the Mediterranean See with a sensitivity analysis in order to investigate the properties of the different approaches. The results are then compared with traditional DEA.

Origin–destination (O–D) matrix estimation methods based on traffic counts have been largely discussed and investigated. The most used methods are based on Generalised Least Square estimators (GLS) that use as input data a starting O–D matrix and a set of traffic counts. In addition to traffic counts, the analysts could know other general information about travel demand or link flows, based on their experience, or spot data, but few works deal with the matter of including effectively these sources of information. This paper proposes a Fuzzy-GLS estimation method that allows to improve the estimation performances of classic GLS estimator by including, in addition to traffic counts, uncertain information about starting O–D demand (i.e. outdated estimates, spot data, expert knowledge, etc.). The methods explicitly take into account the relevant level of uncertainty by taking as much advantage as possible from the few vague available data. The method is developed using fuzzy sets theory and fuzzy programming that seems to be a convenient theoretical framework to represent uncertainty in the available data. A solution algorithm for the proposed problem is also presented. The method has been tested by numerical applications and then compared to the classical GLS method under different sets of constraints to the problem.