rss_2.0Transport and Telecommunication Journal FeedSciendo RSS Feed for Transport and Telecommunication Journalhttps://sciendo.com/journal/TTJhttps://www.sciendo.comTransport and Telecommunication Journal 's Coverhttps://sciendo-parsed-data-feed.s3.eu-central-1.amazonaws.com/6374ff0f98240f0297d6413d/cover-image.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20221205T071614Z&X-Amz-SignedHeaders=host&X-Amz-Expires=604799&X-Amz-Credential=AKIA6AP2G7AKP25APDM2%2F20221205%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Signature=ce5b18397c893afb9b44b35e8cf3c389b4ed81ecde84f6b3473e1365205aebfc200300Risk Assessment of the Operation of Aviation Maintenance Personnel Trained on Virtual Reality Simulatorshttps://sciendo.com/article/10.2478/ttj-2022-0026<abstract> <title style='display:none'>Abstract</title> <p>Conducting a safe briefing is essential to educate aircraft maintenance personnel, who very often encounter various unexpected and dangerous incidents. Their reaction to situations should be quick and adequate. To train aircraft maintenance professionals who cannot be practiced in real life due to high cost, danger, time or effort, virtual training seems like an obvious choice. This paper is devoted to the development of a calculation algorithm for assessing the risk of actions taken at the aircraft repair site, which was implemented in the training version of the virtual reality (VR) simulation. It includes a number of factors and elements that form the simulation scenario, influencing the degree of its complexity and the assessment of the performance of each exercise. Various components of the algorithm are presented, which allow assessing the skills of students of aviation specialist courses. The criterion for the acceptability of the developed algorithm is the correct assessment of the student’s skills in the course of training.</p> </abstract>ARTICLE2022-11-16T00:00:00.000+00:00Dynamic Traveling Route Planning Method for Intelligent Transportation Using Incremental Learning-Based Hybrid Deep Learning Prediction Model with Fine-Tuninghttps://sciendo.com/article/10.2478/ttj-2022-0024<abstract> <title style='display:none'>Abstract</title> <p>Predicting the most favorable traveling routes for Vehicles plays an influential role in Intelligent Transportation Systems (ITS). Shortest Traveling Routes with high congestion grievously affect the driving comfort level of VANET users in populated cities. As a result, increase in journey time and traveling cost. Predicting the most favorable traveling routes with less congestion is imperative to minimize the driving inconveniences. A major downside of existing traveling route prediction models is to continuously learn the real-time road congestion data with static benchmarking datasets. However, learning the new information with already learned data is a cumbersome task. The main idea of this paper is to utilize incremental learning on the Hybrid Learning-based traffic Congestion and Timing Prediction (HL-CTP) to select realistic, congestion-free, and shortest traveling routes for the vehicles. The proposed HL-CTP model is decomposed into three steps: dataset construction, incremental and hybrid prediction model, and route selection. Firstly, the HL-CTP constructs a novel Traffic and Timing Dataset (TTD) using historical traffic congestion information. The incremental learning method updates the novel real-time data continuously with the TDD during prediction to optimize the performance efficiency of the hybrid prediction model closer to real-time. Secondly, the hybrid prediction model with various deep learning models performs better by taking the route prediction decision based on the best sub-predictor results. Finally, the HL-CTP selects the most favorable vehicle routes selected using traffic congestion, timing, and uncertain environmental information and enhances the comfort level of VANET users. In the simulation, the proposed HL-CTP demonstrates superior performance in terms of Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).</p> </abstract>ARTICLE2022-11-16T00:00:00.000+00:00Smartphone-Based Recognition of Access Trip Phase to Public Transport Stops Via Machine Learning Modelshttps://sciendo.com/article/10.2478/ttj-2022-0022<abstract> <title style='display:none'>Abstract</title> <p>The usage of mobile phones is nowadays reaching full penetration rate in most countries. Smartphones are a valuable source for urban planners to understand and investigate passengers’ behavior and recognize travel patterns more precisely. Different investigations tried to automatically extract transit mode from sensors embedded in the phones such as GPS, accelerometer, and gyroscope. This allows to reduce the resources used in travel diary surveys, which are time-consuming and costly. However, figuring out which mode of transportation individuals use is still challenging. The main limitations include GPS, and mobile sensor data collection, and data labeling errors. First, this paper aims at solving a transport mode classification problem including (still, walking, car, bus, and metro) and then as a first investigation, presents a new algorithm to compute waiting time and access time to public transport stops based on a random forest model. Several public transport trips with different users were saved in Rome to test our access trip phase recognition algorithm. We also used Convolutional Neural Network as a deep learning algorithm to automatically extract features from one sensor (linear accelerometer), obtaining a model that performs well in predicting five modes of transport with the highest accuracy of 0.81%.</p> </abstract>ARTICLE2022-11-16T00:00:00.000+00:00Ec-Funded Projects’ Lessons Learned In Earth Friendly Freight Transportationhttps://sciendo.com/article/10.2478/ttj-2022-0029<abstract> <title style='display:none'>Abstract</title> <p>The paper is based on the research project ePIcenter (<ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://www.ePIcenterproject.eu">www.ePIcenterproject.eu</ext-link>) supported by the EU HORIZON 2020 Programme. ePIcenter connects thirty-six partners: port authorities, logistic service providers, manufacturers, academic institutions, and technology partners. The main goal is to develop and test AI driven logistic software solutions, apply new technologies and methodologies to increase the efficiency of global supply chains and reduce their environmental impact. One of the significant aspect the project focusses on is optimisation, using AI, digitalisation, automation and innovations in freight transport and handling technologies. Finally, modelling powerful solutions to enable resilient, efficient and environment friendly supply chains.</p> <p>Knowledge sharing is one of powerful tool for researchers, policymakers, service providers and other stakeholders to develop a holistic and comprehensive common knowledge base. First, a theoretical framework had been developed through identification and review knowledge available from previous projects funded through the European Commission as well as other international funding projects. Second, lessons learned and success stories from previous EC-funded projects and other international research programmes were reviewed and provided in the first year of project implementation.</p> </abstract>ARTICLE2022-11-16T00:00:00.000+00:00CO Emission Costs Due to the Production and Use of Vehicles in the Context of Automotive Developmenthttps://sciendo.com/article/10.2478/ttj-2022-0025<abstract> <title style='display:none'>Abstract</title> <p>The aim of this article is to assess the carbon dioxide (CO<sub>2</sub>) emissions of three types of cars: two internal combustion cars and one Fuel Cell Electric Vehicle (FCEV), measured on the basis of type approval regulations.</p> <p>The article also assesses the CO<sub>2</sub> emission costs resulting from the production of fuel and the production of the car.</p> <p>It was assumed as a research hypothesis that the development and growing serial production of vehicles with different power systems will bring measurable changes in CO<sub>2</sub> emissions from road transport. Based on their own research, the authors also analyzed the credibility of the assumptions made about the benefits related to emissions resulting from replacing the classic vehicle with hydrogen one. They estimated the duration and intensity of use of a hydrogen vehicle that offers CO<sub>2</sub> benefits compared to a conventional vehicle.</p> </abstract>ARTICLE2022-11-16T00:00:00.000+00:00Relevance of Regulatory and Data Availability Issues to Transport and Logistics Processes, Based on the Insights of the Epicenter Projecthttps://sciendo.com/article/10.2478/ttj-2022-0028<abstract> <title style='display:none'>Abstract</title> <p>The article was prepared using research output received implementing the ePIcenter project funded by the European Union program HORIZON 2020. A brief description of the project is presented. The paper presents theoretical research regarding essential data requirements that may be important for improving logistics processes. After identifying the main data requirements, scientific research is presented. Analysis of valid legal acts to determine the regulatory criteria and aspects of the relevance and availability of data is performed. Essential areas of data exchange, their importance and trends are identified, taking into account business and governmental organisations. Trends and possible development perspectives are presented.</p> </abstract>ARTICLE2022-11-16T00:00:00.000+00:00A Modelling System for Evaluating Options for Building and Using a Fleet of Battery Electric Truckshttps://sciendo.com/article/10.2478/ttj-2022-0027<abstract> <title style='display:none'>Abstract</title> <p>Many shipping companies tackle the challenge of potentially replacing conventional trucks with electric ones in economically developed countries. The aim of this work is to describe the principle of scaling models for analysing the operation of a fleet of vehicles, when the decision to use a particular type of model is made considering the accuracy and completeness of the initial data, as well as the goals of modelling. At the end of the report, information is provided on the TraPodSim simulation system developed by the authors, which is based on a multi-agent simulation model created using AnyLogic software.</p> <p>The paper considers modelling methods aimed at assessing the physical indicators of the transportation process. Various aspects of using three types of mathematical models are discussed: a) analytical deterministic models, b) analytical models using the Monte Carlo method and c) simulation models.</p> </abstract>ARTICLE2022-11-16T00:00:00.000+00:00Highway Vehicles’ Overtaking Classificationhttps://sciendo.com/article/10.2478/ttj-2022-0023<abstract> <title style='display:none'>Abstract</title> <p>Highway users can apprehension to certain subjects with utilizing of Vehicular Ad-hoc Networks (VANET) applications if the rules for safe overtaking movement are violated to make the lane change maneuver between vehicles on the highway road. In our research, we suggest an algorithm for semi-automated vehicles S-AV compliant lane change to emphasize rules for safe overtaking between vehicles on the highway. The proposed algorithm technique classify the safe overtaking into major categories and critically analyzed them depending on various classes of lane change movements between vehicles interrelated to road condition based on different performance criteria; this technique will add awareness to drivers traveling on highway to increasing the comfort and safety of driving. Finally, we have conclude and suggest research issues associated to Vehicular Ad-hoc Networks to investigate and ensure the real-time decision of safe overtaking between vehicles on the highway, which is important research task to motivate researchers to connect the semi-automated vehicles with driver face emotion detection and increase driving safety.</p> </abstract>ARTICLE2022-11-16T00:00:00.000+00:00The Concept of Dependency Game Tree Graphs as a Black Box in the Analysis of Automatic Transmissionshttps://sciendo.com/article/10.2478/ttj-2022-0017<abstract> <title style='display:none'>Abstract</title> <p>The methodology of graph theory has long had applications in mechanics. Graphs allow the simultaneous consideration and synthesis of the structure of a real system and the idealized structure of an equivalent model of a mechanical system. In particular, there are many applications of graph theory in the analysis of automatic transmissions. This paper presents continuing research on applications of game tree graphs in the analysis of automatic transmissions. Black box ideas for dependency graphs are presented. This makes it possible to automatically generate simplified models from given physical models of an automatic transmission and to determine the optimal number of teeth. The developed methodology allows also to perform real-time simulations. In a further stage, the methods of decision logic trees can be applied to analyze the functional diagrams of selected gears.</p> </abstract>ARTICLE2022-07-04T00:00:00.000+00:00Dynamic Weighing to Improve Rail Freight Traffic Safety: A Case Study from the Czech Republichttps://sciendo.com/article/10.2478/ttj-2022-0018<abstract> <title style='display:none'>Abstract</title> <p>Railway traffic safety is a decisive factor involved in any decision-making process in the railway transport, including the overall weight of cars, i.e., potential overloading. Overloaded rolling stock may cause many serious accidents. The presented article comprises two parts: the theoretical one explores the ways of progressive dynamic weighing of shipment. The practical part took place at Horní Dvořiště railway station, measuring the impact of the dynamic weighing on the decline in vehicular overloading.</p> </abstract>ARTICLE2022-07-04T00:00:00.000+00:00Novel Method of Reducing the Allocated Bandwidth by Voiphttps://sciendo.com/article/10.2478/ttj-2022-0020<abstract> <title style='display:none'>Abstract</title> <p>Old telecommunication systems are gradually being replaced by a new system that works over IP networks, which is known as voice over internet protocol (VoIP). VoIP has several merits (e.g., very cheap call rate), which make it increasingly popular in the telecommunication world. However, VoIP faces numerous impediments that decelerate its promotion. One of the top impediments is the wasted bandwidth caused by VoIP systems. Numerous methods have been proposed to handle this impediment, including packet aggregation methods. This paper proposes a novel aggregation method, called packet aggregation and carrier header (PA-CH), to reduce the amount of the large bandwidth caused by VoIP. As the name suggests, PA-CH saves in bandwidth by aggregating the packets in a header and using the redundant fields in the packet header to carry a portion of the packet voice data. The performance of the introduced PA-CH method was investigated based on three main metrics, namely, link capacity, allocated bandwidth reduction, and voice data shortening. Simulation results indicate that the proposed PA-CH method outperforms the comparison methods in three factors. For instance, the proposed method’s allocated bandwidth reduction ratio reaches 51% when the number of calls running concurrently reaches 100. Therefore, the proposed PA-CH method achieves its goal of reducing the wasted bandwidth caused by VoIP.</p> </abstract>ARTICLE2022-07-04T00:00:00.000+00:00Factors Affecting the Growth of Demand on Carsharing Services Within Smart Cityhttps://sciendo.com/article/10.2478/ttj-2022-0021<abstract> <title style='display:none'>Abstract</title> <p>The carsharing services have become the necessary component of the life of smart and sustainable city. They meet the numerous requirements put forward by these cities concepts and make life in urban environment cleaner, more comfortable and convenient and better organized. The goal of this research is to determine the factors facilitating the demand for the carsharing services, and on the basis of this analysis to consider the costs structure of these companies. The results are obtained via PLS-SEM analyses implementation in SmartPLS-3.3.7 software. The analysis revealed that such factors as Convenience, Additional Values and Economic (saving factors) have positive impact on growth of demand for carsharing services while New Way of Thinking is insignificant. However, the developed Costs function demonstrate that companies assume the costs of promoting “green” effect of carsharing as important ones. If they reduce these types of costs, it will have positive impact on their efficiency without decrease of demand.</p> </abstract>ARTICLE2022-07-04T00:00:00.000+00:00Travellers’ Perception About Vehicular Emissions’ and its Impact on Pedestrian Travel Behaviourhttps://sciendo.com/article/10.2478/ttj-2022-0019<abstract> <title style='display:none'>Abstract</title> <p>Vehicular emissions have many impacts on human health and travel behaviour. A lot of evidence on the health effects of vehicular emissions is available but very few studies have looked at the impacts on travel behaviour. The current study attempts to fill this research gap by analysing the factors that influence pedestrian travel behaviour concerning vehicle emissions based on travellers’ perceptions in the Indian context. For this, a stated preference questionnaire survey was conducted and a factor-based regression followed by a mediation analysis was used to analyse the responses. Results showed that a person’s perceived impacts about vehicular emissions had a greater impact on their travel behaviour. Public concern and environmental attitude lead to direct changes in travel behaviour whereas vehicle technology and negligent attitude had significant indirect effects. The present study findings are useful to the urban planning policymakers in reducing the impact of vehicular emissions on pedestrians by implementing strategies that lessen human exposure to transport emissions.</p> </abstract>ARTICLE2022-07-04T00:00:00.000+00:00Research of an Influence of a Traffic Flow Movement Intensity Change on the Possibility of Nonstop Passage of the Traffic Lights Objectshttps://sciendo.com/article/10.2478/ttj-2022-0012<abstract> <title style='display:none'>Abstract</title> <p>There were examined the problems of passage of the regulated parts of a road. There were investigated the changes of a traffic movement intensity in Lutsk (Ukraine) during the spread of Covid-19 pandemic. The graphic dependences of the drivers’ actions estimation while passing the traffic lights objects on a chosen movement route at the beginning of quarantine measures, during the least movement intensity and at the increasing of movement intensity, were obtained. A method of increasing of a possibility of the traffic lights objects nonstop passage was offered.</p> </abstract>ARTICLE2022-04-30T00:00:00.000+00:00Predicting Australia’s Domestic Airline Passenger Demand using an Anfis Approachhttps://sciendo.com/article/10.2478/ttj-2022-0013<abstract> <title style='display:none'>Abstract</title> <p>The forecasting of future airline passenger demand is critical task for airline management. The objective of the present study was to develop an adaptive neuro-fuzzy inference system (ANFIS) for predicting Australia’s domestic airline passenger demand. The ANFIS model was trained, tested, and validated in the study. Sugeno fuzzy rules were used in the ANFIS structure and Gaussian membership function, and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. The results found that the mean absolute percentage error (MAPE) for the overall data set of the ANFIS model was 3.25% demonstrating that the ANFIS model has high predictive capabilities. The ANFIS model could be used in other domestic air travel markets.</p> </abstract>ARTICLE2022-04-30T00:00:00.000+00:00Feature Selection Method for Ml/Dl Classification of Network Attacks in Digital Forensicshttps://sciendo.com/article/10.2478/ttj-2022-0011<abstract> <title style='display:none'>Abstract</title> <p>The research is related to machine learning and deep learning (ML/DL) methods for clustering and classification that are compatible with anomaly detection (network attacks detection) in digital forensics. Research is conducted in the field of selecting subsets of features of a dataset useful for constructing a good predictor (classifier). In this study, a new feature selection method for a classifier based on the Analytical Hierarchy Process (AHP) method is presented and tested. The proposed step-by-step algorithm for the iterative selection of these features makes it possible to obtain the minimum required list of features that are associated with attack events and can be used to detect them. For the classification, Artificial Neural Network (ANN) method is used. The accuracy of attack detection by the proposed method has been verified in numerical experiments.</p> </abstract>ARTICLE2022-04-30T00:00:00.000+00:00A Public Value-Based, Multilevel Evaluation Framework to Examine Public Bike-Sharing Systems. Implications for Cities’ Sustainable Transport Policieshttps://sciendo.com/article/10.2478/ttj-2022-0016<abstract> <title style='display:none'>Abstract</title> <p>This article proposes a multilevel bike-sharing assessment framework based on the concept of public value. This approach makes it possible to combine customer satisfaction with the transport service system with determinants of demand for bicycle services in the form of value. The framework aims to evaluate the parameters of public bike systems (PBS) that determine user value, and that co-create user value, system value, and social and ecological value, to identify the characteristics of the bicycle that need improvement in order to meet users’ needs and optimize quality. The framework uses empirical verification through satisfaction surveys of PBS users in Lodz, Poland. The results of the study were subjected to factor analysis, which revealed four groups of factors that satisfy public bike users: (1) impact on the health, environment, mobility and traffic in the city, (2) reliability, and comfort, (3) intramodality, (4) price and technical availability.</p> </abstract>ARTICLE2022-04-30T00:00:00.000+00:00Social Distance Evaluation in Transportation Systems and Other Public Spaces using Deep Learninghttps://sciendo.com/article/10.2478/ttj-2022-0014<abstract> <title style='display:none'>Abstract</title> <p>This research put forward an efficacious real-time deep learning-based technique to automate the process of monitoring the social distancing in transportation systems (e.g., bus stops, railway stations, airport terminals, etc.) and other public spaces with the purpose to mitigate the impact of coronavirus pandemic. The proposed technique makes use of the YOLOv3 model to segregate humans from the background of each image of a surveillance video and the linear Kalman filter for tracking the humans’ motion even in case in which another object or person overlaps the trajectory of the person under analysis. The performance of the model in human detection is extremely high as demonstrated by the accuracy of the model that reaches values higher than 95%. The detection algorithm can be applied for alerting people to keep a safe distance from each other when they are in crowded places or in groups.</p> </abstract>ARTICLE2022-04-30T00:00:00.000+00:00Mapping Undermined Role of Information and Communication Technologies in Floodshttps://sciendo.com/article/10.2478/ttj-2022-0015<abstract> <title style='display:none'>Abstract</title> <p>This paper reports the undermined potential of broad range of (Information and communication technologies) ICTs that remained effective yet unnoticed in different flood-phases to exchange traffic, travel, and evacuation related information. The objective was to identify convenient ICTs that people found operational in life cycle of a flood. For the purpose, ICTs were tested in relation to 18 different variables based on personal capabilities, demographic, and vehicle-based information etc.</p> <p>Samples of 105 and 102 subjects were recruited from flood-prone communities of developing and developed case-studies respectively, through random sampling and analyzed through Multinomial Logistic Regression. Those categories of independent variables that showed p-value ≥ 0.05 were considered to model the results. The main findings showed that in developed countries TV, mobile phone subscriptions and international news channels were prominent source of information whilst in developing countries multiple messengers, Facebook and contributory websites were impactful for information dissemination. The results are useful for academia, engineers, and policy makers and for future work same variables can be tested for different disaster affected communities.</p> </abstract>ARTICLE2022-04-30T00:00:00.000+00:00Ammonia as Clean Shipping Fuel for the Baltic Sea Regionhttps://sciendo.com/article/10.2478/ttj-2022-0010<abstract> <title style='display:none'>Abstract</title> <p>The international shipping industry transports about 90 per cent of the global trade volume and is responsible for only two per cent of the anthropogenic carbon dioxide emissions. Consequently, the shipping sector is considered as an environmentally friendly transport mode. Nevertheless, global shipping can also improve its environmental footprint. So that in recent years clean shipping initiatives have been placed on the political agenda with the implementation of the Sulphur Emission Control Area (SECA) and Nitrogen Emission Control Area (ECA) regulations and the Global Cap. The next target of the International Maritime Organisation (IMO) in the sequel of the Paris Agreement of climate protection is dedicated to reduction of the Greenhouse Gas (GHG) emissions by up to 50 % until the year 2050.</p> <p>The paper investigates and discusses the research questions to what extent ammonia can be used in Baltic Sea Region (BSR) to propel merchant vessels and how ammonia can fulfil future demands under technical, economic and infrastructural aspects to become the green fuel for the Baltic Sea Region (BSR) shipping industry. The study benchmarks the properties of ammonia as marine fuel against Marine Gas Oil (MGO) and Liquified Natural Gas (LNG). The research is based on secondary data analysis that is complemented by expert interviews and case studies, and the results are empirically validated by data that were collected during the EU projects “EnviSuM”, “GoLNG”, “CSHIPP” and “Connect2SmallPorts” that took place within the last four years in the BSR.</p> </abstract>ARTICLE2022-02-18T00:00:00.000+00:00en-us-1