Mauro Dell’Orco

Bee Colony Optimization: Principles and Applications

The bee colony optimization metaheuristic (BCO) is proposed in the paper. The BCO represents the new metaheuristic capable to solve difficult combinatorial optimization problems. The artificial bee colony behaves partially alike, and partially differently from bee colonies in nature. In addition to proposing the BCO as a new metaheuristic, we also describe in the paper two BCO algorithms that we call the bee system (BS) and the fuzzy bee system (FBS). In the case of FBS the agents (artificial bees) use approximate reasoning and rules of fuzzy logic in their communication and acting. In this way, the FBS is capable to solve deterministic combinatorial problems, as well as combinatorial problems characterized by uncertainty. The proposed approach is illustrated by three case studies

Mitigating Traffic Congestion: Solving the Ride-Matching Problem by Bee Colony Optimization

Abstractn Urban road networks in many countries are severely congested. Expanding traffic network capacities by building more roads is very costly as well as environmentally damaging. Researchers, planners, and transportation professionals have developed various Travel Demand Management (TDM) techniques, i.e. strategies that increase travel choices to travelers. Ride sharing is one of the widely used TDM techniques that assumes the participation of two or more persons that together share a vehicle when traveling from few origins to few destinations. In ride-matching systems, commuters wishing to participate in ride sharing are matched by where they live and work, and by their work schedule. There is no standard method in the open literature to determine the best ride-matching method. In this paper, an attempt has been made to develop the methodology capable to solve the ride-matching problem. The proposed Bee Colony Optimization Metaheuristic is sufficiently general and could be applied to various combinatorial optimization problems.

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.

Data fusion for updating information in modelling drivers’ choice behaviour

In this article, a method for fusing fuzzy data relevant both to drivers’ experience and provided information is presented. Expected travel time is then updated according to the results of fusion. The method takes into account the ‘compatibility’ of data originating from different sources, and provides information about acceptability of results. Influence of uncertainty on drivers’ compliance with provided information is examined in detail, according to uncertainty-based information theory.

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.

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.

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.

A Mathematical Model for Evaluation of Information Effects in ATIS (Advanced Traveler Information Systems) Environment

Travel choices are made according to people’s personal preferences and knowledge of the system. Since increase, improvement and updating of knowledge is achieved through information, consequentially information itself is a crucial issue in transportation problems. If information was perfect, users could easily choose the best path from their point of view, but unfortunately complete and precise information about network conditions is rarely available, therefore uncertainty can cause anxiety and stress in decision makers.