Mario Marinelli

Simulation of Crowd Dynamics in Panic Situations Using a Fuzzy Logic-Based Behavioural Model

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.

Artificial Bee Colony-Based Algorithm for Optimising Traffic Signal Timings

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 are 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.

A neural network approach to medical image segmentation and three-dimensional reconstruction

Medical Image Analysis represents a very important step in clinical diagnosis. It provides image segmentation of the Region of Interest (ROI) and the generation of a three-dimensional model, representing the selected object. In this work, was proposed a neural network segmentation based on Self-Organizing Maps (SOM) and a three-dimensional SOM architecture to create a 3D model, starting from 2D data of extracted contours. The utilized dataset consists of a set of CT images of patients presenting a prosthesis’ implant, in DICOM format. An application was developed in Visual C++, which provides an user interface to visualize DICOM images and relative segmentation. Moreover it generates a three-dimensional model of the segmented region using Direct3D.

Application of Data Fusion for Route Choice Modelling by Route Choice Driving Simulator

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.

A multivariate logic decision support system for optimization of the maritime routes

In recent years, maritime freight transportation and the consequent handling of containers are among the most dynamic and growing sectors. The aim of this research is to propose a Decision Support System (DSS) addressed mainly to shipping companies, allowing the choice, even en-route, of the hub port of destination for the successive multi-modal operations. The companies make choices in relation both to the ship location and to a dynamic accessibility indicator. The accessibility indicator is generally accepted as the parameter that better represents the interactions between a port and its hinterland. Different factors can influence the accessibility in maritime transport; some of them are characterized by low variability, while others show a high within-day dynamics. For example, the technical characteristics of ports (number of berths and their depths, number of cranes, storage area, etc.) belong to the first group; instead, the number of free berths, the delay time in freight loading and unloading operations, and weather conditions can change during the day. Their variability can be evaluated by a real-time monitoring, while the ship location can be easily obtained by GPS and radar signals. In the proposed DSS, we have considered data about the technical characteristics of ports and, depending on the request coming from ships, acquires the dynamic characteristics of each port, the ship location and the destination area. After the completion of the process, the DSS provides as output the port “closer” to the requests expressed by the users. Since some current values of both the dynamic characteristics of ports and information provided by shipping companies are subject to uncertainty, we proposed a DSS based on a multivariate accessibility indicator.

Optimizing Airport Gate Assignments Through a Hybrid Metaheuristic Approach

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 minimization of the number of flights assigned to remote terminals and the minimization 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.