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Mauro 'Orco

Politecnico di Bari, DICATECh - Department of Civil, Environmental, Building Engineering, and Chemistry, Faculty Member

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Mauro Dell'Orco

Politecnico di Bari

Mario Marinelli

Politecnico di Bari

Francesco Galante

Università degli Studi di Napoli "Federico II"

Università degli Studi del Sannio

Universiteit Hasselt

mauro dell'orco

Kyandoghere KYAMAKYA

North Carolina State University

North Carolina State University

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Papers by Mauro 'Orco

Modeling the dynamic effect of information on drivers’ choice behavior in the context of an Advanced Traveler Information System

Transportation Research Part C: Emerging Technologies, 2017

In this paper, we present a modeling approach, based on Fuzzy Data Fusion, to reproduce drivers' ... more In this paper, we present a modeling approach, based on Fuzzy Data Fusion, to reproduce drivers' dynamic choice behavior under an Advanced Traveler Information System (ATIS). The proposed model uses the Possibility Theory to model Uncertainty embedded in human perception of information. We have introduced a time-dependent Possibility Distribution of Information to model the users' changing perception of travel time also based on current network conditions. Drivers' choice models are often developed and calibrated by using Stated Preference (SP) surveys, amongst others. In this work, we present an experiment to set up an SP-tool based on a driving simulator developed at the Polytechnic University of Bari. The results obtained by the proposed model are analyzed and compared with the driver dynamic behavior observed in the experiment.

Modelling Drivers' Route Choice Behaviour through Possibility Theory Using Driving Simulator

This paper presents a modelling approach based on the Possibility Theory to reproduce drivers&#39... more This paper presents a modelling approach based on the Possibility Theory to reproduce drivers' choice behaviour under Advanced Traveller Information Systems (ATIS). The Possibility Theory is introduced to model uncertainty embedded in human perception of information through a fuzzy data fusion technique. Drivers' choice models are often developed and calibrated by using, among other, Stated Preferences (SP) surveys. An experiment 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 compare the outcomes of the proposed model with preferences stated in the experiment.

Simulation of Users Decision in Transport Mode Choice Using Neuro-Fuzzy Approach

Computational Science and Its Applications – ICCSA 2012, 2012

In this paper, soft computing and artificial intelligence techniques have been used to define a m... more 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.

Modeling risk perception in ATIS context through Fuzzy Logic

State of the Art in the European Quantitative Oriented Transportation and Logistics Research, 2011, 2011

This research is aimed at investigating the effect of accuracy of ATIS (Advanced Traveller Inform... more 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.

Artificial Bee Colony-Based Algorithm for Optimising Traffic Signal Timings

Advances in Intelligent Systems and Computing, 2013

Step 0: Initialization. Define the user specified parameters; the number of decision variables (n... more Step 0: Initialization. Define the user specified parameters; the number of decision variables (n), the constraints for each decision variable, the size of bee colony (SN), maximum cycle number (MCN). Step 1: Set t = 1. Step 2: Generate the random initial signal timings within the constraints of decision variables. Step 3: Distribute to the green timings to the stages. Step 4: Get the network data and fixed set of link flows for TRANSYT-7F traffic model. Step 5: Run TRANSYT-7F. Step 6: Get the network PI. At this step, the PI is determined using TRANSYT-7F traffic model. Step 7: If t = MCN then terminate the algorithm; otherwise, t = t+1 and go to Step 2.

Modelling parking choice behaviour using Possibility Theory

Transportation Planning and Technology, 2011

A community of agents as a tool to optimize industrial districts logistics

Trasporti europei, 2010

The aim of this paper is to find an optimal solution to operational planning of freight transport... more The aim of this paper is to find an optimal solution to operational planning of freight transportation in an industrial district. We propose a system architecture that drives agents-the industrial district firms-to cooperate in logistic field, to minimize transport and environmental costs. The idea is to achieve logistics optimization setting up a community made of district enterprises, preserving a satisfactory level of system efficiency and fairness. We address the situation in which a virtual coordinator helps the agents to reach an agreement. The objectives are: maximizing customers satisfaction, and minimizing the number of trucks needed. A fuzzy clustering (FCM), two Fuzzy Inference System (FIS) combined with a Genetic Algorithm (GA), and a greedy algorithm are thus proposed to achieve these objectives, and eventually an algorithm to solve the Travelling Salesman Problem is also used. The proposed framework can be used to provide real time solutions to logistics management problems, and negative environmental impacts.

A Novel Approach to the Road Maintenance Planning, Based on the Possibility Theory

13 th Mini-EURO …, 2002

The roads existence is due to the mobility need of people that, after the infrastructure construc... more

Advanced OR and AI Methods in Transportation BEE COLONY OPTIMIZATIONA COOPERATIVE LEARNING APPROACH TO COMPLEX TRANSPORTATION …

Abstract. Various natural systems teach us that very simple individual organisms can create syste... more

Simulation of Users Decision in Transport Mode Choice Using Neuro-Fuzzy Approach

by Mauro Dell'Orco, Michele Ottomanelli, and Mauro 'Orco

Lecture Notes in Computer Science, 2012

In this paper, soft computing and artificial intelligence techniques have been used to define a m... more 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.

Modeling the dynamic effect of information on drivers' choice behavior in the context of an Advanced Traveler Information System

by Mario Marinelli, Mauro Dell'Orco, and Mauro 'Orco

Transportation Research Part C: Emerging Technologies, 2017

In this paper, we present a modeling approach, based on Fuzzy Data Fusion, to reproduce drivers' ... more In this paper, we present a modeling approach, based on Fuzzy Data Fusion, to reproduce drivers' dynamic choice behavior under an Advanced Traveler Information System (ATIS). The proposed model uses the Possibility Theory to model Uncertainty embedded in human perception of information. We have introduced a time-dependent Possibility Distribution of Information to model the users' changing perception of travel time also based on current network conditions. Drivers' choice models are often developed and calibrated by using Stated Preference (SP) surveys, amongst others. In this work, we present an experiment to set up an SP-tool based on a driving simulator developed at the Polytechnic University of Bari. The results obtained by the proposed model are analyzed and compared with the driver dynamic behavior observed in the experiment.

A metaheuristic dynamic traffic assignment model for OD matrix estimation using aggregate data

by Mario Marinelli, Mauro Dell'Orco, and Mauro 'Orco

Procedia-Social and …, 2012

For a static/dynamic O-D matrix estimation, usually, the basic required information is a starting... more 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.

Features Selection based on Fuzzy Entropy for Data Envelopment Analysis Applied to Transport Systems

by Mauro 'Orco, Michele Ottomanelli, and Sara Bray

Recently, great attention has been paid to the data envelopment analysis (DEA) for the analysis o... more 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.

Intelligent Decision Support Tools for Optimal Planning of Rail Track Maintenance

by Michele Ottomanelli, Mauro Dell'Orco, and Mauro 'Orco

Department of Highways …, Jan 1, 2002

A Decision Support System Based on Neuro-Fuzzy System for Railroad Maintenance Planning

by Mauro 'Orco and Mauro Dell'Orco

Iceis, 2005

Lavoro basato sulla revisione della memoria:

Competitive egress behaviour: a fuzzy logic-inspired microscopic model

International Journal of Critical Infrastructures, 2007

Abstract: Some recent tragic events in overcrowded situations, as well as the terrorist threat, h... more

Features Selection based on Fuzzy Entropy for Data Envelopment Analysis Applied to Transport Systems

Transportation Research Procedia, 2014

Recently, great attention has been paid to the data envelopment analysis (DEA) for the analysis o... more 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.

A metaheuristic dynamic traffic assignment model for O-D matrix estimation using aggregate data

by Mario Marinelli, Mauro 'Orco, Michele Ottomanelli, and Mauro Dell'Orco

Proceedings of Ewgt 2012 - 15th Meeting of the Euro Working Group on Transportation, 2012

For a static/dynamic O-D matrix estimation, usually, the basic required information is a starting... more 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.

Modelling risk perception in ATIS context: a comparison of different Fuzzy Logic-based approaches

by Mauro 'Orco, Gennaro Nicola C Bifulco, and Mario Marinelli

Proceedings of Ewgt 2012 - 15th Meeting of the Euro Working Group on Transportation, 2012

In this paper travellers' reactions to Advanced Traveller Information Systems (ATIS) are analysed... more 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.

Bee Colony Optimization: Principles and Applications

2006 8th Seminar on Neural Network Applications in Electrical Engineering, 2006

Skip to Main Content. IEEE.org | IEEE Xplore Digital Library | IEEE Standards Association | Spect... more

Modeling the dynamic effect of information on drivers’ choice behavior in the context of an Advanced Traveler Information System

Transportation Research Part C: Emerging Technologies, 2017

In this paper, we present a modeling approach, based on Fuzzy Data Fusion, to reproduce drivers' ... more In this paper, we present a modeling approach, based on Fuzzy Data Fusion, to reproduce drivers' dynamic choice behavior under an Advanced Traveler Information System (ATIS). The proposed model uses the Possibility Theory to model Uncertainty embedded in human perception of information. We have introduced a time-dependent Possibility Distribution of Information to model the users' changing perception of travel time also based on current network conditions. Drivers' choice models are often developed and calibrated by using Stated Preference (SP) surveys, amongst others. In this work, we present an experiment to set up an SP-tool based on a driving simulator developed at the Polytechnic University of Bari. The results obtained by the proposed model are analyzed and compared with the driver dynamic behavior observed in the experiment.

Modelling Drivers' Route Choice Behaviour through Possibility Theory Using Driving Simulator

This paper presents a modelling approach based on the Possibility Theory to reproduce drivers&#39... more This paper presents a modelling approach based on the Possibility Theory to reproduce drivers' choice behaviour under Advanced Traveller Information Systems (ATIS). The Possibility Theory is introduced to model uncertainty embedded in human perception of information through a fuzzy data fusion technique. Drivers' choice models are often developed and calibrated by using, among other, Stated Preferences (SP) surveys. An experiment 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 compare the outcomes of the proposed model with preferences stated in the experiment.

Simulation of Users Decision in Transport Mode Choice Using Neuro-Fuzzy Approach

Computational Science and Its Applications – ICCSA 2012, 2012

In this paper, soft computing and artificial intelligence techniques have been used to define a m... more 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.

Modeling risk perception in ATIS context through Fuzzy Logic

State of the Art in the European Quantitative Oriented Transportation and Logistics Research, 2011, 2011

This research is aimed at investigating the effect of accuracy of ATIS (Advanced Traveller Inform... more 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.

Artificial Bee Colony-Based Algorithm for Optimising Traffic Signal Timings

Advances in Intelligent Systems and Computing, 2013

Step 0: Initialization. Define the user specified parameters; the number of decision variables (n... more Step 0: Initialization. Define the user specified parameters; the number of decision variables (n), the constraints for each decision variable, the size of bee colony (SN), maximum cycle number (MCN). Step 1: Set t = 1. Step 2: Generate the random initial signal timings within the constraints of decision variables. Step 3: Distribute to the green timings to the stages. Step 4: Get the network data and fixed set of link flows for TRANSYT-7F traffic model. Step 5: Run TRANSYT-7F. Step 6: Get the network PI. At this step, the PI is determined using TRANSYT-7F traffic model. Step 7: If t = MCN then terminate the algorithm; otherwise, t = t+1 and go to Step 2.

Modelling parking choice behaviour using Possibility Theory

Transportation Planning and Technology, 2011

A community of agents as a tool to optimize industrial districts logistics

Trasporti europei, 2010

The aim of this paper is to find an optimal solution to operational planning of freight transport... more The aim of this paper is to find an optimal solution to operational planning of freight transportation in an industrial district. We propose a system architecture that drives agents-the industrial district firms-to cooperate in logistic field, to minimize transport and environmental costs. The idea is to achieve logistics optimization setting up a community made of district enterprises, preserving a satisfactory level of system efficiency and fairness. We address the situation in which a virtual coordinator helps the agents to reach an agreement. The objectives are: maximizing customers satisfaction, and minimizing the number of trucks needed. A fuzzy clustering (FCM), two Fuzzy Inference System (FIS) combined with a Genetic Algorithm (GA), and a greedy algorithm are thus proposed to achieve these objectives, and eventually an algorithm to solve the Travelling Salesman Problem is also used. The proposed framework can be used to provide real time solutions to logistics management problems, and negative environmental impacts.

A Novel Approach to the Road Maintenance Planning, Based on the Possibility Theory

13 th Mini-EURO …, 2002

The roads existence is due to the mobility need of people that, after the infrastructure construc... more

Advanced OR and AI Methods in Transportation BEE COLONY OPTIMIZATIONA COOPERATIVE LEARNING APPROACH TO COMPLEX TRANSPORTATION …

Abstract. Various natural systems teach us that very simple individual organisms can create syste... more

Simulation of Users Decision in Transport Mode Choice Using Neuro-Fuzzy Approach

by Mauro Dell'Orco, Michele Ottomanelli, and Mauro 'Orco

Lecture Notes in Computer Science, 2012

In this paper, soft computing and artificial intelligence techniques have been used to define a m... more 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.

Modeling the dynamic effect of information on drivers' choice behavior in the context of an Advanced Traveler Information System

by Mario Marinelli, Mauro Dell'Orco, and Mauro 'Orco

Transportation Research Part C: Emerging Technologies, 2017

In this paper, we present a modeling approach, based on Fuzzy Data Fusion, to reproduce drivers' ... more In this paper, we present a modeling approach, based on Fuzzy Data Fusion, to reproduce drivers' dynamic choice behavior under an Advanced Traveler Information System (ATIS). The proposed model uses the Possibility Theory to model Uncertainty embedded in human perception of information. We have introduced a time-dependent Possibility Distribution of Information to model the users' changing perception of travel time also based on current network conditions. Drivers' choice models are often developed and calibrated by using Stated Preference (SP) surveys, amongst others. In this work, we present an experiment to set up an SP-tool based on a driving simulator developed at the Polytechnic University of Bari. The results obtained by the proposed model are analyzed and compared with the driver dynamic behavior observed in the experiment.

A metaheuristic dynamic traffic assignment model for OD matrix estimation using aggregate data

by Mario Marinelli, Mauro Dell'Orco, and Mauro 'Orco

Procedia-Social and …, 2012

For a static/dynamic O-D matrix estimation, usually, the basic required information is a starting... more 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.

Features Selection based on Fuzzy Entropy for Data Envelopment Analysis Applied to Transport Systems

by Mauro 'Orco, Michele Ottomanelli, and Sara Bray

Recently, great attention has been paid to the data envelopment analysis (DEA) for the analysis o... more 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.

Intelligent Decision Support Tools for Optimal Planning of Rail Track Maintenance

by Michele Ottomanelli, Mauro Dell'Orco, and Mauro 'Orco

Department of Highways …, Jan 1, 2002

A Decision Support System Based on Neuro-Fuzzy System for Railroad Maintenance Planning

by Mauro 'Orco and Mauro Dell'Orco

Iceis, 2005

Lavoro basato sulla revisione della memoria:

Competitive egress behaviour: a fuzzy logic-inspired microscopic model

International Journal of Critical Infrastructures, 2007

Abstract: Some recent tragic events in overcrowded situations, as well as the terrorist threat, h... more

Features Selection based on Fuzzy Entropy for Data Envelopment Analysis Applied to Transport Systems

Transportation Research Procedia, 2014

Recently, great attention has been paid to the data envelopment analysis (DEA) for the analysis o... more 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.

A metaheuristic dynamic traffic assignment model for O-D matrix estimation using aggregate data

by Mario Marinelli, Mauro 'Orco, Michele Ottomanelli, and Mauro Dell'Orco

Proceedings of Ewgt 2012 - 15th Meeting of the Euro Working Group on Transportation, 2012

For a static/dynamic O-D matrix estimation, usually, the basic required information is a starting... more 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.

Modelling risk perception in ATIS context: a comparison of different Fuzzy Logic-based approaches

by Mauro 'Orco, Gennaro Nicola C Bifulco, and Mario Marinelli

Proceedings of Ewgt 2012 - 15th Meeting of the Euro Working Group on Transportation, 2012

In this paper travellers' reactions to Advanced Traveller Information Systems (ATIS) are analysed... more 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.

Bee Colony Optimization: Principles and Applications

2006 8th Seminar on Neural Network Applications in Electrical Engineering, 2006

Skip to Main Content. IEEE.org | IEEE Xplore Digital Library | IEEE Standards Association | Spect... more