DEA (data envelopment analysis) is a non-parametric linear programming method for determining the relative efficiencies of a set of decision making units (e.g. public transport companies) with multi-inputs and multi-outputs. The advantage of using DEA is that it does not require any assumption on the shape of the frontier surface and it makes no assumptions concerning the internal operations of a DMU. It is very often used in the transport sector for estimating the efficiency of airports, ports, railways and public transport companies. In order to make better assessment of green intermodal freight transportation, the aim of this paper is to applied DEA method to evaluate the relative efficiency of two green intermodal freight transport (GIFT) corridors (IV,V) of South East Europe. Input variables considered in this DEA problem are relative unit cost, transport time, delay risk, CO2 and SO2 emissions. Frequency of regular service is output variable. The application of this method aims at the identification of strengths and weaknesses of each GIFT Corridor and will pinpoints the actions that should be taken in order to improve their operational status and their transformation to Green Corridors.

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.

LAMRECOR project, funded by MiUR with Poste Italiane as prime contractor, aims at demonstrating an innovative integrated system for advanced logistics targeted at minimizing the environmental impact of the fleet of vehicles, optimizing the productive processes, improving the safety of the postal workers, and developing new logistics procedures for mail delivery. For enhancing the safety of the workers, an integrated sensors system has been designed. The system includes an on-board unit to control the appropriate use of personal protective equipment, improving protection of the postmen also through the activation of an emergency call in case of accident, and to monitor functional performances of the vehicles. A prototype of the sensors system has been developed confirming the design results. Some mathematical models for studying the driving behaviour have been also analysed. A Decision Support System for the management of the logistics infrastructure has been developed and tested and data mining algorithms have been studied

For a static/dynamic O-D matrix estimation, usually, the basic required information is a starting estimation of O-D matrix and a set of traffic counts. In the era of the Intelligent Transportation Systems, a dynamic estimation of traffic demand has become a crucial issue. Different Dynamic Traffic Assignment (DTA) models have been proposed, used also for O-D matrices estimation. This paper presents a dynamic O-D demand estimator, using a novel simulation-based DTA algorithm. The core of the proposed algorithm is a mesoscopic dynamic network loading model used in conjunction with a Bee Colony Optimization (BCO). The BCO is capable to solve high level combinatorial problems with fast convergence performances, allowing to overcome classical demand-flow relationships drawbacks.

Recently, great attention has been paid to the data envelopment analysis (DEA) for the analysis of efficiency of transportation systems. In real world applications, the data of production processes cannot be precisely measured or can be affected by 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 data errors. Many research works have faced the problem of using DEA models when the inputs and outputs are uncertain. Fuzzy Theory based methods are one of the approaches that have been recently proposed even without a determined (or unique) framework. In this work we have defined a fuzzy version of the classical DEA models, and, in particular, a feature selection analysis has been developed to investigate the effects of uncertainty on the efficiency of the considered transport services. The feature selection method developed in this paper is based on fuzzy entropy measures and it can be applied to DMUs (Decision Making Units) on the entire frontier. Having identified the efficient and inefficient DMUs in fuzzy DEA analysis, the focus is on the stability of classification of DMUs into efficient and inefficient performers. A numerical example is then presented, considering as DMUs a set of international container ports with given number of inputs and outputs properly modified.

In this paper, soft computing and artificial intelligence techniques have been used to define a model for simulating users’ decisional process in a transportation system. Through this framework, the variables involved are expressed by approximate or linguistic values, like in the humans’ reasoning way, in order to forecast users’ mode choice behavior. The model has been specified and calibrated using a set of real life data. Results appear good in comparison with those obtained by a classical random utility based model calibrated with the same data, and the methodology seems promising also in case of different applications in the field of choice behavior simulation

In this paper a simulation model for dynamic bikes redistribution process is presented. The objective of the model is to minimize the vehicles repositioning costs for bike-sharing operators, aiming at a high level users satisfaction, and assuming that it increases with the probability to find an available bike or a free docking point in any station at any time. The proposed model considers the dynamic variation of the demand (for both bikes and free docking slot) and micro-simulate the BSS in space and time determining the optimal repositioning flows, distribution patterns and time intervals between relocation operations by explicitly considering the route choice for trucks among the stations.

A crucial issue in bike-sharing systems (BSS) is the unbalanced distribution in space and time of the bikes among the stations. Literature shows several methods, to solve the vehicle reallocation problem and most of them are based on rigid control thresholds and refer to car-sharing systems. In this paper a more flexible fuzzy decision support system for redistribution process in BSS is presented. The aim of the proposed method is to minimize the redistribution costs for bike-sharing companies, determining the optimal bikes repositioning flows, distribution patterns and time intervals between relocation operations, with the objective of a high level for users satisfaction. The proposed method allows to define the best bikes repositioning jointly to the best route for the carrier vehicles. The optimization method has been applied to a simulated BSS that can be considered as a module of a wider real BSS thanks to the scalable architecture of the decision support system. The results of this first tests are interesting even if further investigation are in progress

Transport companies in many cases have to evaluate their competitiveness, comparing it with that of their competitors. Usually this assessment is performed through one or more indices representing facility performances, derived from a set of indicators relevant to problem representation. If the aim is to estimate the user evaluation for the service offered by a facility, the development of a synthetic index can be difficult since user’s choice is often characterized by significant uncertainties and it is not always governed by certain rules and rational behaviour, so that it could not be easily and explicitly represented by traditional mathematical techniques and models. Such uncertainties in the relationship between indicator values and facility attractiveness can be properly defined by explicitly specifying them in an approximate way using fuzzy sets theory. In this paper an innovative approach for the classification of Transport Facilities is proposed. The method is based on a Fuzzy Inference System and may be employed both as a benchmarking/ranking procedure and as a decision support tool to evaluate future scenarios as a result of facilities remodelling.

The potential and critical aspects of any transport service can be highlighted through the estimation of appropriate performance indicators of the examined system. Commonly, container terminal analysis is based first on the evaluation and comparison of quantitative parameters that describe the level of service of the terminal and, on the other side by means of performance indicators related to terminal productivity. In this paper a Fuzzy Inference System for evaluation of a synthetic performance indicator is proposed. This tool could help planners and managers in terminals performances analysis and ranking as well as in assessing the effects of possible intervention on the systems. The proposed approach is suitable in the case of hub container ports. In fact this system is characterised by significant uncertainties and it is not always governed by certain rules, rational behaviour, so that it cannot be easily represented by traditional mathematical techniques and models. In our opinion, could be convenient to define the values of the considered parameters by explicitly define them in an approximate way, that is to say by fuzzy sets

In this paper, a model based on Artificial Neural Network (ANN) has been applied to real estate appraisal. Moreover, an evaluation of ANN performances in estimating the sale price of residential properties has been carried out. Artificial Neural Networks (ANNs) are useful in modelling input-output relationships learning directly from observed data. This capability can be very useful in complex systems like the real estate ones where motivations, tastes and budget availability often do not follow rational behaviours. This study also analyses the impact of such key environmental conditions that represent a problem related to many industrial cities where pollution and landscaping consequences affect the real estate market and residential location choices. We have considered a set of asking price's houses collected in the urban area of Taranto (Italy) where the biggest European steel factory and the 2nd industrial harbour are located.

In this paper a Generalized Least Square estimator for the simultaneous estimation of O-D matrix and equilibrium traffic assignment model parameters is presented. The problem is formulated as fixed-point model (equilibrium programming) assuming the congested network case. In the optimization step the variability of both O-D demand vector and the matrix of link choice probabilities is considered. We assume as input information a set of observable network data, such as link traffic counts and travel time, as well starting estimates of both O-D matrix and models parameters. Along the paper, the theoretical aspects of the proposed estimator, the solution algorithm as well as the results of numerical applications are discussed.

Network design is one of the crucial activity in transportation engineering whose goal is to determine an optimal solution to traffic network layout with respect to given objectives and technical and/or economic constraints. In most of the practical problem the input data are not always precisely known as well as the information is not available regarding certain input parameters that are part of a mathematical model. Also constraints can be stated in approximate or ambiguous way. Thus, starting data and/or the problem constraints can be affected by uncertainty. Uncertain values can be represented using of fuzzy values/constraints and then handled in the framework of fuzzy optimization theory. In this paper we present a fuzzy linear programming method to solve the optimal signal timing problem on congested urban. The problem is formulated as a fixed point optimization subject to fuzzy constraints. The method has been applied to a test network for the case of priority corridors that are used for improve transit and emergency services. A deep sensitivity analysis of the signal setting parameters is then provided. The method is compared to classical linear programming approach with crisp constraints

Network design models allow to define an optimal network configuration by means of objective functions subject to a series of constraints. The starting data and/or the constraints of the problem can be affected by uncertainty. These uncertain values are better managed through the use of fuzzy values/constraints. In this paper we present a fuzzy non-linear programming to solve the equilibrium Network Design problem for urban areas. This problem is formulated as a fixed point optimization subject to fuzzy constraints. The proposed method has been applied to a test network. The obtained results show that the proposed approach is very interesting

The aim of this research is to investigate the effects of environmental conditions in a given area on the residential location and the consequences on urban sprawl and accessibility. In particular, the study focuses on the effects of environmental quality and landscaping on property values. To this aim, the paper presents some hedonic Multiple Linear Regression models (MLR) estimating the housing price in metropolitan areas as a function of real-estate, environmental and accessibility variables. The hedonic models have been estimated using data collected in the province of Taranto (South-Italy) where the biggest steel factory in Europe (namely, ILVA), and one of the most important industrial port in the Mediterranean Sea are located. The set of considered variables were carried out from a location choice survey and hedonic regression estimators are presented to verify to what extent a relationship between the accessibility conditions, environmental context and the dwelling market values does exist. The results indicate that the inclusion, in the model specification, of the environmental variables between zones fit the data significantly better.

This article presents hedonic Multiple Linear Regression models (MLR) to estimate real estate price variations in metropolitan areas as a result of changing environmental and accessibility conditions. The goodness of fit of the model has been compared along with a series of hypotheses about the performance of the specifications considering spatial relationships between observations. The case study for such analysis is the metropolitan area of Taranto (Southern Italy). The models which considered spatial dependence between observations offered a greater degree of fit in a scenario showing strong spatial correlation in MLR residuals

Road accident reports show that a large part of accidents involves pedestrians, the category of road users generally considered "weak" with respect to other mobility users, and occurs at pedestrian crossings. It is therefore essential to provide traffic engineers with simulation tools able to forecast the response of a given design solution and compare it with other alternatives. In this paper a simplified and logic model to simulate interaction between pedestrians and vehicles’ drivers at road crossings is presented. The model the crossing process has been represented as a Discrete Events System and allows to estimate the mutual interactions by considering basic and easy to collect parameters. Pedestrian behavior has been characterized in the decision phase considering a gap acceptance criterion, based on parameters derived from probabilistic distribution to take into account the heterogeneity of the pedestrian population. Interaction between pedestrians during the crossing phase is also taken into account considering a cellular model of the crossing area. The model allows estimating safety benefits for pedestrians, and crossing level of service both under pedestrians and vehicular flows points of view, starting from site geometry and field measurements of flows parameters of pedestrians and vehicles In order to check the performance of the model it has been applied to a real site case study. Then the effects of implementation of traffic-calming intervention are also simulated and evaluated.