Fusion of Two Metaheuristic Approaches to Solve the Flight Gate Assignment Problem

AbstractOne 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

An urban bikeway network design model for inclusive and equitable transport policies

Abstract This study suggests an optimization framework to plan and design a network of bike lanes in an urban context, based on equity principles and subject to a given available budget. The novelty of the proposal consists in an objective function that aims at minimizing the existing inequities among different population groups in terms of accessibility/opportunity to the bikeways. The proposed methodology represents a reliable decision support system tool that could help transport authorities/managers to select the priority areas of their future investments related to the cycling infrastructures. To prove the effectiveness and value of the methodology, an application with relevant analysis to a test case study is presented

A fuzzy data meta training system for ranking hub container terminals

The potential and critical aspects of any transport service can be highlighted through the estimation of appropriate performance indicators of the examined system. Commonly, container terminal analysis is based first on the evaluation and comparison of quantitative parameters that describe the level of service of the terminal and, on the other side by means of performance indicators related to terminal productivity. In this paper a Fuzzy Inference System for evaluation of a synthetic performance indicator is proposed. This tool could help planners and managers in terminals performances analysis and ranking as well as in assessing the effects of possible intervention on the systems. The proposed approach is suitable in the case of hub container ports. In fact this system is characterised by significant uncertainties and it is not always governed by certain rules, rational behaviour, so that it cannot be easily represented by traditional mathematical techniques and models. In our opinion, could be convenient to define the values of the considered parameters by explicitly define them in an approximate way, that is to say by fuzzy sets

A green logistics solution for last-mile deliveries considering e-vans and e-cargo bikes

Abstract The environmental challenges and the initiatives for sustainable development in urban areas are mainly focused on eco-friendly transportation systems. Therefore, we introduce a new green logistics solution for last-mile deliveries considering synchronization between e-vans and e-cargo bikes, developed as a Two-Echelon Electric Vehicle Routing Problem with Time Windows and Partial Recharging (2E-EVRPTW-PR). The first echelon represents an urban zone, and the second echelon represents a restricted traffic zone (e.g., historical center) in which e-vans in the first and e-cargo bikes in the second echelon are used for customers' deliveries. The proposed 2E-EVRPTW-PR model aims to minimize the total costs in terms of travel costs, initial vehicles' investment costs, drivers' salary costs, and micro-depot cost. The effectiveness of the proposed solution has been demonstrated comparing two different cases, i.e., the EVRPTW-PR considering e-vans for the first case, and the 2E-EVRPTW-PR considering e-vans and e-cargo bikes for the second case. The comparison has been carried out on existing EVRPTW-PR instances for the first case, and on novel 2E-EVRPTW-PR instances for the second case, in which customers of initial EVRPTW-PR instances have been divided into two zones (urban and restricted traffic zones) by using Fuzzy C-mean clustering. Moreover, results encourage logistics companies to adopt zero-emission strategies for last-mile deliveries, especially in restricted traffic zones

A Fuzzy set-based method to identify the car position in a road lane at intersections by smartphone GPS data

Abstract Intelligent transportation systems (ITS) work by collections of data in real time. Average speed, travel time and delay at intersections are some of the most important measures, often used for monitoring the performance of transportation systems, and useful for system management and planning. In urban transportation planning, intersections are usually considered critical points, acting as bottlenecks and clog points for urban traffic. Thus, detecting the travel time at intersections in different turning directions is an activity useful to improve the urban transport efficiency. Smartphones represent a low-cost technology, with which is possible to obtain information about traffic state. However, smartphone GPS data suffer for low precision, mainly in urban areas. In this paper, we present a fuzzy set-based method for car positioning identification within road lanes near intersections using GPS data coming from smartphones. We have introduced the fuzzy sets to take into account uncertainty embedded in GPS data when trying to identify the position of cars within the road lanes. Moreover, we introduced a Genetic Algorithm to calibrate the fuzzy parameters in order to obtain a novel supervised clustering technique. We applied the proposed method to one intersection in the urban road network of Bari (Italy). First results reveal the effectiveness of the proposed methodology when comparing the outcomes of the proposed method with two well-known clustering techniques (Fuzzy C-means, K-means)

The impact of undesirable externalities on residential property values: spatial regressive models and an empirical study

Pollutant emissions, noise and other externalities generated by heavy infrastructures, might impact negatively on real estate values. To test this effect, this paper presents the results of an analysis based on Hedonic Linear Regression, Spatial Hedonic Linear Regression and Hedonic Geographically Weighted Regression models, carried out for the study case of the province of Taranto (Italy). The biggest steel factory in Europe is located here, and some population movements have been observed in relation to the high levels of pollution in the areas close to the factory. The variables used to measure the impact of externalities are of two types: objective indicators such as the distance from the industrial area and the levels of NO2 and PM10, and subjective indicators such as the level of pollution and noise perceived by the population. Results show that the distance from factory was a positive factor in the real estate prices although not always clearly significant, and among pollution indicators, only high levels of NO2 had a negative effect. The accessibility to employment did not prove to be a significant variable in the real estate prices, which indicates that factors related to environmental quality have a greater weight in residential location. Moreover, models including subjective indicators do not show better estimates than models considering only objective indicators. Finally, spatial regression models were useful to analyse the spatial dependence and spatial heterogeneity observed in the data.This study was supported by research funding form the Spanish Ministry of Economy and Competitiveness through the projects TRA2012-37659 (co-financed with FEDER funds) and TRA2013-48116-R

Study of the Accessibility Inequalities of Cordon-Based Pricing Strategies Using a Multimodal Theil Index

The implementation of an appropriate pricing policy in an urban area could alleviate both environmental and congestion problems by encouraging a shift towards more sustainable modes of transportation. However, any positive net social welfare balance delivered by the policy can hide unacceptable regressive effects. Therefore, it is crucial to investigate any change in relative levels of accessibility among different categories of transport network users. This study focuses on the application of a cordon-based congestion pricing scheme on a multimodal network, where private cars and public transportation coexist, and includes a sensitivity analysis by varying the size of the charging area and the amount of the toll, for a monocentric urban reality. Taking into account an elastic demand associated with each proposed charging scenario, the related distributional effects are explored using the Theil index, with a quantitative assessment of the inequalities in the accessibility variations across the users of the network

Better for everyone: an approach to multimodal network design considering equity.

We propose a formulation of the Network Design Problem (NDP) to support transport planners in dealing with multimodal networks in contexts characterised by different (and sometimes conflicting) interests and limited resources in a transparent way. We expect that the implementation of the method can increase the acceptability of transport schemes.The proposed formulation expands the scope of traditional NDP approaches: firstly, it takes public transit into account alongside private transport. Then, it considers the relevance of equity among other planning goals, enabling the achievement of solutions with a fair distribution of transport impacts (benefits and costs) among the users. Finally, it proposes the conjoint use of fuzzy and rigid goals and constraints to improve the quality of the solutions.Equityis defined as the mode-specific relative variation of the overall mobility between Origin-Destination (OD) pairs. We propose two specifications ofthe equitable NDP: one uses a crisp approach, with objective function equal to the overall network cost. The other is a fuzzy maximisation of the level of satisfaction generated by a certain network configuration. The level of satisfaction depends on the extent at which a given solution achieves private and public transport equity and overall network cost targets.We illustrate the approach in the case of a signal time planning problem in a small network. The evaluation of the performance of crisp and fuzzy optimisation shows that the former approach providesbetter solutions to private transport and vice versa. We propose that, when using fuzzy optimisation, the decision maker should evaluate a set of nearly-optimal solutions selected on the basis of Pareto optimality

The Effects of Environmental Quality on Residential Choice Location

ABSTRACT: The aim of this research is to investigate the effects of environmental conditions in a given area on the residential location and the consequences on urban sprawl and accessibility. In particular, the study focuses on the effects of environmental quality and landscaping on property values. To this aim, the paper presents some hedonic Multiple Linear Regression models (MLR) estimating the housing price in metropolitan areas as a function of real-estate, environmental and accessibility variables. The hedonic models have been estimated using data collected in the province of Taranto (South-Italy) where the biggest steel factory in Europe (namely, ILVA), and one of the most important industrial port in the Mediterranean Sea are located. The set of considered variables were carried out from a location choice survey and hedonic regression estimators are presented to verify to what extent a relationship between the accessibility conditions, environmental context and the dwelling market values does exist. The results indicate that the inclusion, in the model specification, of the environmental variables between zones fit the data significantly better