For a long time, many researchers have investigated the continuous network design problem (CNDP) ... more For a long time, many researchers have investigated the continuous network design problem (CNDP) to distribute equitably additional capacity between selected links in a road network, to overcome traffic congestion in urban roads. In addition, CNDP plays a critical role for local authorities in tackling traffic congestion with a limited budget. Due to the mutual interaction between road users and local authorities, CNDP is usually solved using the bilevel modeling technique. The upper level seeks to find the optimal capacity enhancements of selected links, while the lower level is used to solve the traffic assignment problem. In this study, we introduced the enhanced differential evolution algorithm based on multiple improvement strategies (EDEMIS) for solving CNDP. We applied EDEMIS first to a hypothetical network to show its ability in finding the global optimum solution, at least in a small network. Then, we used a 16-link network to reveal the capability of EDEMIS especially in t...
In this paper, a review of the main actions and policies that can be implemented to promote susta... more In this paper, a review of the main actions and policies that can be implemented to promote sustainable mobility is proposed. The work aims to provide a broad, albeit necessarily not exhaustive, analysis of the main studies and research that from different points of view have focused on sustainable mobility. The structure of the paper enables the reader to easily identify the topics covered and the studies related to them, so as to guide him/her to the related in-depth studies. In the first part of the paper, there is a preliminary analysis of the concept of sustainable mobility, the main transport policies implemented by the European Union and the USA, and the main statistical data useful to analyze the problem. Next, the main policies that can promote sustainable mobility are examined, classifying them into three topics: Environmental, socioeconomic , and technological. Many of the policies and actions examined could be classified into more than one of the three categories used; for each of them, there is a description and the main literature work on which the topic can be analyzed in more detail. The paper concludes with a discussion on the results obtained and the prospects for research.
In this paper, we propose a generalisation of the Method of Successive Averages (MSA) for solving... more In this paper, we propose a generalisation of the Method of Successive Averages (MSA) for solving traffic assignment problems. The generalisation consists in proposing a different step sequence within the general MSA framework to reduce computing times. The proposed step sequence is based on the modification of the classic 1/k sequence for improving the convergence speed of the algorithm. The reduction in computing times is useful if the assignment problems are subroutines of algorithms for solving Network Design Problems—such algorithms require estimation of the equilibrium traffic flows at each iteration, hence, many thousands of times for real-scale cases. The proposed algorithm is tested with different parameter values and compared with the classic MSA algorithm on a small and on two real-scale networks. A test inside a Network Design Problem is also reported. Numerical results show that the proposed algorithm outperforms the classic MSA with reductions in computing times, reaching up to 79%. Finally, the theoretical properties are studied for stating the convergence of the proposed algorithm.
Recently, several prominent logistic companies in Europe and the USA are seriously considering th... more Recently, several prominent logistic companies in Europe and the USA are seriously considering the idea of using drones launched from trucks and working in parallel to deliver packages. In the relevant literature, a novel problem formulation called travelling salesman problem with drone has been introduced, and some modelling and solution approaches have been presented. Existing approaches are based on the main assumption that the truck can dispatch and pick up a drone only at a node, i.e. the depot or a customer location. Here, the authors present a novel approach aimed to maximise the drone usage in parcel delivering. The authors consider that a truck can deliver and pick a drone up not only at a node but also along a route arc (en route). In this way, the operations of a drone are not strictly related to the customers’ position, but it can serve a wider area along the route. The authors tested the proposed heuristic on benchmark instances and analysed the benefits introduced with the en route approach.
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.
The gate assignment problem (GAP) is one of the most important problems that operations managers ... more The gate assignment problem (GAP) is one of the most important problems that operations managers face daily. The GAP aims at determining an assignment of flights to terminal and ramp positions (gates), and an assignment of starting and ending times of the processing of a flight at its position. The objectives related to the flight gate assignment problem (FGAP) include the mini‐ mization of the number of flights assigned to remote terminals and the minimi‐ zation of passengers' total walking distance. The main aim of this research is to find a novel methodology to solve the FGAP. In this paper, we propose a hybrid approach called Biogeography-based Bee Colony Optimization (B-BCO). This approach is obtained by properly combining two metaheuristics: biogeography-based (BBO) and bee colony optimization (BCO) algorithms. The proposed B-BCO model integrates the BBO migration operator into to bee's search behavior. The obtained results show the better performances of the proposed approach in solving FGAP when compared to BCO. Keywords: Flight gate assignment · Bee Colony Optimization · Biogeography Based Optimization · Multicriteria analysis · Airport operations
In the field of Swarm Intelligence, the Bee Colony Optimization (BCO) has proven to be capable of... more In the field of Swarm Intelligence, the Bee Colony Optimization (BCO) has proven to be capable of solving high-level combinatorial problems, like the Flight-Gate Assignment Problem (FGAP), with fast convergence performances. However, given that the FGAP can be often affected by uncertainty or approximation in data, in this paper we develop a new metaheuristic algorithm, based on the Fuzzy Bee Colony Optimization (FBCO), which integrates the concepts of BCO with a Fuzzy Inference System. The proposed method assigns, through the multicriteria analysis, airport gates to scheduled flights based on both passengers' total walking distance and use of remote gates, to find an optimal flight-to-gate assignment for a given schedule. Comparison of the results with the schedules of real airports has allowed us to show the characteristics of the proposed concepts and, at the same time, it stressed the effectiveness of the proposed method.
Advances in Intelligent Systems and Computing, 2013
Modelling route choices is one of the most significant tasks in transportation models. Route choi... more 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.
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.
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.
One of the most important activity in airport operations is the gate scheduling. It is concerned ... more One of the most important activity in airport operations is the gate scheduling. It is concerned with finding an assignment of flights to terminal and ramp positions (gates), and an assignment of the start and completion times of the processing of a flight at its position. The objectives related to the flight gate assignment problem (FGAP) include the minimization of the number of flights assigned to remote terminals and the minimization of the total walking distance. The main aim of this research is to find a methodology to solve the FGAP. In this paper, we propose a hybrid approach called Biogeography-based Bee Colony Optimization (B-BCO). This approach is obtained fusing two metaheuristics: biogeography-based (BBO) and bee colony optimization (BCO) algorithms. The proposed B-BCO model integrates the BBO migration operator into to bee's search behaviour. Results highlight better performances of the proposed approach in solving FGAP when compared to BCO.
Modelling route choices is one of the most significant tasks in transportation models. Route choi... more 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
In the past decades, the increase of civil air-traffic and the corresponding growth of airports h... more In the past decades, the increase of civil air-traffic and the corresponding growth of airports have highlighted the importance of the gate scheduling as a key activity in airport operations. To solve this problem, different mathematical models for flights assignment to gates can often be found in technical literature. In this work we propose a method based on the Bee Colony Optimization (BCO) to find an optimal flight gate assignment for a given schedule. This metaheuristic represents an interesting methodology in the field of Swarm Intelligence for its capability to solve high level combinatorial problems with fast convergence performances. The proposed methodology includes a multicriteria analysis considering two main objectives: minimization of passenger total walking distance and remote gate usage. Results of the comparison with the Milano-Malpensa airport schedule highlight the effectiveness of the proposed method.
In this paper, a model based on Artificial Neural Network (ANN) has been applied to real estate a... more 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 2 nd industrial harbour are located.
This paper presents a modelling approach based on the Possibility Theory to reproduce drivers' ch... 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.
Computer-based Modelling and Optimization in Transportation
Tragic events in overcrowded situations have highlighted the importance of the availability of go... more Tragic events in overcrowded situations have highlighted the importance of the availability of good models for pedestrian behaviour under emergency conditions. Crowd models are generally macroscopic or microscopic. In the first case, the crowd is considered to be like a fluid, so that its movement can be described through differential equations. In the second case, the collective behaviour of the crowd is the result of interactions among individual elements of the system. In this paper, we propose a microscopic model of crowd evacuation that incorporates the fuzzy perception and anxiety embedded in human reasoning. A Visual C++ application was developed to evaluate the outcomes of the model. The model was tested in scenarios with presence of a fixed obstacle. Simulation results have been analyzed in terms of door capacity and compared with an experimental study.
For a long time, many researchers have investigated the continuous network design problem (CNDP) ... more For a long time, many researchers have investigated the continuous network design problem (CNDP) to distribute equitably additional capacity between selected links in a road network, to overcome traffic congestion in urban roads. In addition, CNDP plays a critical role for local authorities in tackling traffic congestion with a limited budget. Due to the mutual interaction between road users and local authorities, CNDP is usually solved using the bilevel modeling technique. The upper level seeks to find the optimal capacity enhancements of selected links, while the lower level is used to solve the traffic assignment problem. In this study, we introduced the enhanced differential evolution algorithm based on multiple improvement strategies (EDEMIS) for solving CNDP. We applied EDEMIS first to a hypothetical network to show its ability in finding the global optimum solution, at least in a small network. Then, we used a 16-link network to reveal the capability of EDEMIS especially in t...
In this paper, a review of the main actions and policies that can be implemented to promote susta... more In this paper, a review of the main actions and policies that can be implemented to promote sustainable mobility is proposed. The work aims to provide a broad, albeit necessarily not exhaustive, analysis of the main studies and research that from different points of view have focused on sustainable mobility. The structure of the paper enables the reader to easily identify the topics covered and the studies related to them, so as to guide him/her to the related in-depth studies. In the first part of the paper, there is a preliminary analysis of the concept of sustainable mobility, the main transport policies implemented by the European Union and the USA, and the main statistical data useful to analyze the problem. Next, the main policies that can promote sustainable mobility are examined, classifying them into three topics: Environmental, socioeconomic , and technological. Many of the policies and actions examined could be classified into more than one of the three categories used; for each of them, there is a description and the main literature work on which the topic can be analyzed in more detail. The paper concludes with a discussion on the results obtained and the prospects for research.
In this paper, we propose a generalisation of the Method of Successive Averages (MSA) for solving... more In this paper, we propose a generalisation of the Method of Successive Averages (MSA) for solving traffic assignment problems. The generalisation consists in proposing a different step sequence within the general MSA framework to reduce computing times. The proposed step sequence is based on the modification of the classic 1/k sequence for improving the convergence speed of the algorithm. The reduction in computing times is useful if the assignment problems are subroutines of algorithms for solving Network Design Problems—such algorithms require estimation of the equilibrium traffic flows at each iteration, hence, many thousands of times for real-scale cases. The proposed algorithm is tested with different parameter values and compared with the classic MSA algorithm on a small and on two real-scale networks. A test inside a Network Design Problem is also reported. Numerical results show that the proposed algorithm outperforms the classic MSA with reductions in computing times, reaching up to 79%. Finally, the theoretical properties are studied for stating the convergence of the proposed algorithm.
Recently, several prominent logistic companies in Europe and the USA are seriously considering th... more Recently, several prominent logistic companies in Europe and the USA are seriously considering the idea of using drones launched from trucks and working in parallel to deliver packages. In the relevant literature, a novel problem formulation called travelling salesman problem with drone has been introduced, and some modelling and solution approaches have been presented. Existing approaches are based on the main assumption that the truck can dispatch and pick up a drone only at a node, i.e. the depot or a customer location. Here, the authors present a novel approach aimed to maximise the drone usage in parcel delivering. The authors consider that a truck can deliver and pick a drone up not only at a node but also along a route arc (en route). In this way, the operations of a drone are not strictly related to the customers’ position, but it can serve a wider area along the route. The authors tested the proposed heuristic on benchmark instances and analysed the benefits introduced with the en route approach.
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.
The gate assignment problem (GAP) is one of the most important problems that operations managers ... more The gate assignment problem (GAP) is one of the most important problems that operations managers face daily. The GAP aims at determining an assignment of flights to terminal and ramp positions (gates), and an assignment of starting and ending times of the processing of a flight at its position. The objectives related to the flight gate assignment problem (FGAP) include the mini‐ mization of the number of flights assigned to remote terminals and the minimi‐ zation of passengers' total walking distance. The main aim of this research is to find a novel methodology to solve the FGAP. In this paper, we propose a hybrid approach called Biogeography-based Bee Colony Optimization (B-BCO). This approach is obtained by properly combining two metaheuristics: biogeography-based (BBO) and bee colony optimization (BCO) algorithms. The proposed B-BCO model integrates the BBO migration operator into to bee's search behavior. The obtained results show the better performances of the proposed approach in solving FGAP when compared to BCO. Keywords: Flight gate assignment · Bee Colony Optimization · Biogeography Based Optimization · Multicriteria analysis · Airport operations
In the field of Swarm Intelligence, the Bee Colony Optimization (BCO) has proven to be capable of... more In the field of Swarm Intelligence, the Bee Colony Optimization (BCO) has proven to be capable of solving high-level combinatorial problems, like the Flight-Gate Assignment Problem (FGAP), with fast convergence performances. However, given that the FGAP can be often affected by uncertainty or approximation in data, in this paper we develop a new metaheuristic algorithm, based on the Fuzzy Bee Colony Optimization (FBCO), which integrates the concepts of BCO with a Fuzzy Inference System. The proposed method assigns, through the multicriteria analysis, airport gates to scheduled flights based on both passengers' total walking distance and use of remote gates, to find an optimal flight-to-gate assignment for a given schedule. Comparison of the results with the schedules of real airports has allowed us to show the characteristics of the proposed concepts and, at the same time, it stressed the effectiveness of the proposed method.
Advances in Intelligent Systems and Computing, 2013
Modelling route choices is one of the most significant tasks in transportation models. Route choi... more 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.
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.
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.
One of the most important activity in airport operations is the gate scheduling. It is concerned ... more One of the most important activity in airport operations is the gate scheduling. It is concerned with finding an assignment of flights to terminal and ramp positions (gates), and an assignment of the start and completion times of the processing of a flight at its position. The objectives related to the flight gate assignment problem (FGAP) include the minimization of the number of flights assigned to remote terminals and the minimization of the total walking distance. The main aim of this research is to find a methodology to solve the FGAP. In this paper, we propose a hybrid approach called Biogeography-based Bee Colony Optimization (B-BCO). This approach is obtained fusing two metaheuristics: biogeography-based (BBO) and bee colony optimization (BCO) algorithms. The proposed B-BCO model integrates the BBO migration operator into to bee's search behaviour. Results highlight better performances of the proposed approach in solving FGAP when compared to BCO.
Modelling route choices is one of the most significant tasks in transportation models. Route choi... more 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
In the past decades, the increase of civil air-traffic and the corresponding growth of airports h... more In the past decades, the increase of civil air-traffic and the corresponding growth of airports have highlighted the importance of the gate scheduling as a key activity in airport operations. To solve this problem, different mathematical models for flights assignment to gates can often be found in technical literature. In this work we propose a method based on the Bee Colony Optimization (BCO) to find an optimal flight gate assignment for a given schedule. This metaheuristic represents an interesting methodology in the field of Swarm Intelligence for its capability to solve high level combinatorial problems with fast convergence performances. The proposed methodology includes a multicriteria analysis considering two main objectives: minimization of passenger total walking distance and remote gate usage. Results of the comparison with the Milano-Malpensa airport schedule highlight the effectiveness of the proposed method.
In this paper, a model based on Artificial Neural Network (ANN) has been applied to real estate a... more 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 2 nd industrial harbour are located.
This paper presents a modelling approach based on the Possibility Theory to reproduce drivers' ch... 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.
Computer-based Modelling and Optimization in Transportation
Tragic events in overcrowded situations have highlighted the importance of the availability of go... more Tragic events in overcrowded situations have highlighted the importance of the availability of good models for pedestrian behaviour under emergency conditions. Crowd models are generally macroscopic or microscopic. In the first case, the crowd is considered to be like a fluid, so that its movement can be described through differential equations. In the second case, the collective behaviour of the crowd is the result of interactions among individual elements of the system. In this paper, we propose a microscopic model of crowd evacuation that incorporates the fuzzy perception and anxiety embedded in human reasoning. A Visual C++ application was developed to evaluate the outcomes of the model. The model was tested in scenarios with presence of a fixed obstacle. Simulation results have been analyzed in terms of door capacity and compared with an experimental study.
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Papers by Mario Marinelli
Keywords: traffic assignment; MSA algorithm; fixed-point problems; network design.
Keywords: traffic assignment; MSA algorithm; fixed-point problems; network design.