Volume 22 (2021): Edizione 1 (February 2021)

Using a qualitative instrumental case study research design, this study examines the strategies and carbon reduction measures implemented by Sydney Airport to achieve their goal of being a carbon neutral airport by 2025. The study period was from 2013 to 2019. The qualitative data was analyzed using document analysis. Sydney Airport has implemented a wide range of carbon reduction measures that underpin its strategy to become a carbon neutral airport. Sydney Airport’s annual emissions intensity per passenger declined in each year examined in study. Sydney Airport has participated in the Airports Council International Airport Carbon Accreditation Program since 2014 and currently holds Accreditation Level 3: Optimization. Sydney Airport’s goal is to be awarded Accreditation Level 3+: Carbon Neutrality by 2025. Sydney Airport has a carbon offsets agreement in place with a not-for-profit organization.

A sustainable transportation system is possible only through an efficient evaluation of transportation network performance. The efficiency of the transport network structure is analyzed in terms of its connectivity, accessibility, network development, and spatial pattern. This study primarily aims to propose a methodology for modeling the accessibility based on the structural parameters of the urban road network. Accessibility depends on the arrangement of the urban road network structure. The influence of the structural parameters on the accessibility is modeled using Multiple Linear Regression (MLR) analysis. The study attempts to introduce two methods of Artificial Intelligence (AI) namely Artificial Neural Networks (ANN) and Adaptive network-based neuro-fuzzy inference system (ANFIS) in modeling the urban road network accessibility. The study also focuses on comparing the results obtained from MLR, ANN and ANFIS modeling techniques in predicting the accessibility. The results of the study present that the structural parameters of the road network have a considerable impact on accessibility. ANFIS method has shown the best performance in modeling the road network accessibility with a MAPE value of 0.287%. The present study adopted Geographical Information Systems (GIS) to quantify, extract and analyze different features of the urban transportation network structure. The combination of GIS, ANN, and ANFIS help in improved decision-making. The results of the study may be used by transportation planning authorities to implement better planning practices in order to improve accessibility.

Vehicular queue length measurement is an important parameter to detect the traffic congestion, which is resulted from several issues such as traffic lights, accidents, and poor roads infrastructures. In this paper, a system in real-time is proposed to detect and measure the vehicular queue length at intersections. The proposed system consists of two main steps: the first step is the detection of queue by using frames differencing method to detect the motion in the target areas. If there is no a motion, then the second step is implemented to detect the vehicles in these areas by using Single Shot Multibox Detector (SSD) algorithm. If there are vehicles, that means the queue exists and the measurement process begins. Some modifications are applied on SSD algorithm to fit with in our system and to improve the accuracy of the vehicle detection process. The system is applied on videos obtained by stationary cameras. The experiments demonstrate that this system is able to accurately detect and measure the vehicular queue length.

The operating condition of bus transit system has not been efficient in most cities of Iran, and many management methods such as regular bus scheduling, assigning exclusive bus lanes, etc., which are necessary for increasing the efficiency of this system, were not regarded enough. Thus, achieving a method for locating the bus stops and optimizing the number of such stops based on a non-homogeneous spatial and temporal distribution of passengers as well as the local traffic patterns are important to be investigated. As such, the present study aims to investigate the modeling of a bus transit system corridor according to the non-homogeneous spatial and temporal distribution of passengers throughout the route aiming at optimization of the number of attracted passengers to the bus. For this purpose, the 8-km route from Vali-e-asr roundabout to Gas roundabout in the city of Rasht in the north of Iran is selected for modeling. Hammersley sampling method, as well as two heuristic optimization techniques, including a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm, are used for generating a non-uniform population and solving the optimization model. Therefore, the results of this analysis are compared to the optimization results by using the probabilistic analysis without considering the reference uncertainty. Finally, the PSO is selected as the superior algorithm for modeling and locating the bus stops due to its results in less travel time, and the validity of robust optimization model is shown due to its higher accuracy and adaptation to the real-world environment. Overall, although the optimization results based on indeterminate analysis in comparison to determinate analysis brought about more average travel time, more population sets were covered by the new introduced stops during 18 active hours of the bus transit system.

Since the implementation of Emission Control Areas (ECA) in 2015, investment decisions related to abatement technologies represent a crucial task in the maritime industry. Currently, the focus in the maritime sector is on Sulphur reductions due to SECA regulations and the legislative of Global Sulphur Cap that started in 2020. A special challenge appears in maritime fleet management where sets of ships have to be considered, representing portfolios of assets that have to be equipped with a variety of different options of abatement technologies.

Modern portfolio theory has been applied to various economic decisions to achieve an optimal allocation of resources among different investment opportunities. The research investigates and discusses the application of the Markowitz´ optimization in the context of SECA regulations for maritime fleets. The optimal investment portfolios are taking into account the three most important compliance options based on the use of low Sulphur fuel, the use of LNG fuel and the use of HFO with a scrubber. The theoretical results are empirically validated by a case of a shipping line operating 10 vessels.

After the implementation of Emission Control Areas (ECA) in Northern Europe in 2015, ship operators have to decide for new compliance methods. Among different abatement technologies, also the use of LNG as maritime fuel is one option. Hence, the number of LNG-fueled vessels plying in North Sea and Baltic Sea is growing, so ports have to cope with the increasing demand for LNG. Until now, German ports have been a little bit delayed in the installation of LNG infrastructure. Currently, a new LNG-terminal at Brunsbüttel, the western entrance to the Kiel Canal, is under construction.

The distribution of LNG from the new LNG-hub to the other German ports can be modelled as an Inventory Routing Problem (IRP) that is usually solved by combinatorial optimization methods. This paper investigates the research question, how the distribution can be modeled as an IRP, which distribution mode is the most economic for the German ports and which modal mix for the LNG supply leads to the greenest distribution. The results of this paper are empirically validated by data that were collected in several EU-projects on sustainable supply chain management and green logistics.

In India, traffic control management is a difficult task due to an increment in the number of vehicles for the same infrastructure and systems. In the smart-city project, the Adaptive Traffic Light Control System (ATLCS) is one of the major research concerns for an Intelligent Transportation System (ITS) development to reduce traffic congestion and accidents, create a healthy environment, etc. Here, we have proposed a Vehicular Density Value (VDV) based adaptive traffic light control system method for 4-way intersection points using a selection of rotation, area of interest, and Statistical Block Matching Approach (SBMA). Graphical User Interface (GUI) and Hardware-based results are shown in the result section. We have compared, the normal traffic light control system with the proposed adaptive traffic light control system in the results section. The same results are verified using a hardware (raspberry-pi) device with different sizes, colors, and shapes of vehicles using the same method.

This article proposes a model of systems dynamics for the analysis and study of physical flows in a distribution logistics chain. The proposed model is a decision support tool that allows testing several scenarios in order to study the behavior of physical flows within a supply chain depending on the inventory and transport costs and taking into consideration the environmental issues through the integration of a CO₂ tax in transport costs. The developed model can be used to analyze various scenarios and perform various “what-if” analyzes, as well as to answer questions about the long-term operation of distribution chains. The results show that logistic decisions and strategies taken at the supply chain level influence inventory and transport costs. They show also that the model is a well decision-support tool for calculating and analyzing transport and inventory costs. We first expose the methodology and the literature review. Next, we present in detail the structure of the model consisting of the causal loops diagram and stock and flow diagram. Finally, we discuss the results of the model.

The ever-increasing use of private vehicles makes Advanced Driver Assistance Systems (ADAS) more necessary as they improve users’ convenience, safety and travel time. Although these systems offer significant advantages, they call into question the traditional role of users, making the psychology of drivers towards these technologies a necessary factor for their adoption. The purpose of this paper is to investigate the effects of psychological factors on the use of one of the most widely used ADAS, the Global Navigation Satellite Systems (GNSS). Towards this direction, a literature review was conducted to identify the factors that influence drivers’ behavior and the psychology of drivers towards new technologies. Furthermore, a questionnaire survey was organized in Greece, based on the Theory of Planned Behavior, including additional variables, which were identified in the literature, such as technophilia, trust in technology and endorsement. From the data collected, models predicting the behavior of drivers were developed through structural equation modelling, concerning the use of navigation systems in both urban and interurban networks. The findings of the research reveal that the intention to use a navigation system is determined by various factors such as behavioral beliefs about its usage, normative beliefs and technophilia. The actual use of a navigation system depends to some extent on this intention.