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.

Users of road facilities generally express a judgment on road quality based on their psychophysical conditions, in relation to the environment they refer to. This judgment is made considering many aspects, for example, the presence of traffic lights, the frequency of interchanges, of lay-bys and gas stations, route conformation, environmental conditions, quality of road signs, etc. In this article, we propose a new index called Global Satisfaction Index, which uses vehicular traffic quality and quality of road pavement, to summarize these aspects, and to express the users’ global judgment about the ride comfort on rural roads. Since in this kind of judgment a subjective perception process is involved, we have used fuzzy theory to handle uncertainty embedded in the process. The attributes of the aspects considered have been expressed through fuzzy numbers, and the global judgment has been obtained through a fuzzy inference system. In this way the proposed index overcomes the limits of other existing indices, since it incorporates uncertainties and/or imprecision inherent in the drivers’ perception of the ride comfort. Moreover, it can be used for evaluation and comparison of different types of road sections. Finally, a numerical example is presented to assist in understanding the practical aspects of the proposed index.

In this study, a bi-level formulation is presented for solving the Equilibrium Network Design Problem (ENDP). The optimisation of the signal timing has been carried out at the upper-level using the Harmony Search Algorithm (HSA), whilst the traffic assignment has been carried out through the Path Flow Estimator (PFE) at the lower level. The results of HSA have been first compared with those obtained using the Genetic Algorithm, and the Hill Climbing on a two-junction network for a fixed set of link flows. Secondly, the HSA with PFE has been applied to the medium-sized network to show the applicability of the proposed algorithm in solving the ENDP. Additionally, in order to test the sensitivity of perceived travel time error, we have used the HSA with PFE with various level of perceived travel time. The results showed that the proposed method is quite simple and efficient in solving the ENDP.

Abstract In this paper travellers’ reactions to Advanced Traveller Information Systems (ATIS) are analysed. In particular two kinds of information (descriptive and prescriptive) and four levels of reliability have been tested. A web-based tool has been adopted in order to carry out a stated preference experiment for data collection. The presented research continues previous studies of the authors in the field of travellers’ compliance with information and travellers’ route choices under ATIS. In previous studies both a discrete choice theory approach and a Mamdani-type Fuzzy Inference System (FIS) were tested. Here several FIS approaches are analysed more in detail. Some preliminary analyses, are recalled from previous research work, furthermore collected data have been deeply analysed through the Sugeno FIS-type approach and by Adaptive-Network-Based FIS. The methods are applied to reproduce travellers’ behaviour and are compared with each other to find the best approach.

This study proposed Artificial Bee Colony (ABC) algorithm for finding optimal setting of traffic signals in coordinated signalized networks for given fixed set of link flows. For optimizing traffic signal timings in coordinated signalized networks, ABC with TRANSYT-7F (ABCTRANS) model is developed. The ABC algorithm is a new population-based metaheuristic approach, and it is inspired by the foraging behavior of honeybee swarm. TRANSYT-7F traffic model is used to estimate total network performance index (PI). The ABCTRANS is tested on medium sized signalized road network. Results showed that the proposed model is slightly better in signal timing optimization in terms of final values of PI when it is compared with TRANSYT-7F in which Genetic Algorithm (GA) and Hill-climbing (HC) methods exist. Results also showed that the ABCTRANS model improves the medium sized network’s PI by 2.4 and 2.7 % when it is compared with GA and HC methods.

This research is aimed at investigating the effect of accuracy of ATIS (Advanced Traveller Information Systems) in terms of route choices and travellers' concordance to informative system. A Stated Preference Experiment has been made by using a Travel Simulator developed at the Technische Universiteit of Delft (The Netherlands). During the experiment respondents have been asked to make repeated route choices in presence of ATIS. Two kinds of information have been tested: descriptive (respondents are provided with the estimated travel times on each route), and prescriptive (respondents are provided with the estimated shortest route). For each kind of information four levels of accuracy have been considered: high, low and two intermediate levels. The main research aims are: 1. investigating the relationship between accuracy of information and travellers' concordance to informative system; 2. investigating the relationship between accuracy of information and route choices. Some preliminary aggregate and statistical analyses have been made; additionally, collected data have been deeply analyzed, and a fuzzy logic approach has been applied in order to reproduce the travellers' behaviour.

Modelling route choices is one of the most significant tasks in transportation models. Route choice models under Advanced Traveller Information Systems (ATIS) are often developed and calibrated by using, among other, Stated Preferences (SP) surveys. Different types of SP approaches can be adopted, alternatively based on Travel Simulators (TSs) or Driving Simulators (DSs). Here a pilot study is presented, aimed at setting up an SP-tool based on driving simulator developed at the Technical University of Bari. The obtained results are analysed in order to check the accordance with expectations in particular the results of application of data fusion technique are shown in order to explain how data collected by DSs, can be used to reduce the effect of choice of behaviour in unrealistic scenarios in TSs

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 general, Continuous Network Design Problem (CNDP) is concerned with the optimal capacity expansion of existing links in a given network. The measure of network performance can be described as the sum of total travel times and the investment cost, converted to travel time, for capacity expansions of links. Due to the nonconvex characteristics of CNDP, the problem is formulated as a bi-level programming problem. Although proposed algorithms in literature are capable of solving CNDP for a given road network, an efficient algorithm, which is capable of finding the global or near global optima of the upper level decision variables of CNDP, is still needed. Therefore, this paper deals with the finding of optimal link capacity expansions in a given road network using the Artificial Bee Colony (ABC) algorithm. More precisely, a bi-level method has been proposed, in which the lower level problem is formulated as a user equilibrium traffic assignment model, solved by the ABC. To show the effectiveness of the ABC algorithm, the example road network is used for different size of travel demand. Results produced by the ABC algorithm are compared with the results obtained by Simulated Annealing (SA) and Genetic Algorithm (GA) taken from literature. According to the results, the ABC algorithm can be used effectively to solve CNDP with link capacity expansions within much less number of traffic assignments than SA and GA.

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