JournalTOCs

Transport and Telecommunication
Journal Prestige (SJR): 0.294

Citation Impact (citeScore): 1
Number of Followers: 4

This is an Open Access Journal

Open Access journal
ISSN (Print) 1407-6160 - ISSN (Online) 1407-6179
Published by Sciendo

[370 journals]

Research of an Influence of a Traffic Flow Movement Intensity Change on
the Possibility of Nonstop Passage of the Traffic Lights Objects
- Abstract: 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.
  PubDate: Sat, 30 Apr 2022 00:00:00 GMT
Predicting Australia’s Domestic Airline Passenger Demand using an
Anfis Approach
- Abstract: 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.
  PubDate: Sat, 30 Apr 2022 00:00:00 GMT
Feature Selection Method for Ml/Dl Classification of Network Attacks in
Digital Forensics
- Abstract: 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.
  PubDate: Sat, 30 Apr 2022 00:00:00 GMT
A Public Value-Based, Multilevel Evaluation Framework to Examine Public
Bike-Sharing Systems. Implications for Cities’ Sustainable Transport
Policies
- Abstract: 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.
  PubDate: Sat, 30 Apr 2022 00:00:00 GMT
Social Distance Evaluation in Transportation Systems and Other Public
Spaces using Deep Learning
- Abstract: 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.
  PubDate: Sat, 30 Apr 2022 00:00:00 GMT
Mapping Undermined Role of Information and Communication Technologies in
Floods
- Abstract: 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.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.
  PubDate: Sat, 30 Apr 2022 00:00:00 GMT
Ammonia as Clean Shipping Fuel for the Baltic Sea Region
- Abstract: 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.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.
  PubDate: Fri, 18 Feb 2022 00:00:00 GMT
A New Method to Boost VoIP Performance Over IPv6 Networks
- Abstract: The demands on virtual communication have increased noticeably during the COVID-19 pandemic lockdown. As an essential part of virtual communication, VoIP should be promoted to achieve the desired level of performance. One critical area investigated in VoIP is the bandwidth utilization (BWU) of the VoIP over IPv6 networks. Enhancing BWU will impact call capacity and quality; it increases call capacity and boosts call quality. Unfortunately, a considerable amount of bandwidth is wasted when running VoIP over IPv6 networks. This is due to the large size of the IPv6 header and the small size of the speech frame. This paper proposes a new method to handle the inefficient BWU when running VoIP over IPv6 networks. The proposed method combines multiplexing multiple VoIP packets in one IPv6 header and using the superfluous fields to carry a portion of the speech frame. Therefore, the proposed is called packet multiplexing and carrier fields (PMCF). Investigation of the PCMF method has been done using four metrics to measure the promotion in BWU, namely calls capacity, header size, bandwidth saving, and speech frame shortening metrics. With the four metrics, the PMCF method has outperformed the comparable methods. For instance, the call capacity has been promoted by up to 269% compared to the typical IPv6 method in the tested scenarios. Therefore, the PMCF method is a feasible solution to facilitate the BWU of VoIP when running VoIP over IPv6 networks.
  PubDate: Fri, 18 Feb 2022 00:00:00 GMT
Method of Improving Incomplete Spatial-Temporal Data in Inland Navigation,
on the Basis of Industrial Camera Images – West Oder River Case Study
- Abstract: Main aim of the paper is to use a single non-metric camera to support the determination of the position of.
  Authors propose to use the existing infrastructure of CCTV cameras mounted on bridges and wharves to determine the position of inland waterway vessels. Image from cameras giving the pixel coordinates of moving object is transformed to the geodetic data domain using a modified projective transformation method. Novel approach is to use of Sequential Projection Transformation (SPT) which additionally uses virtual reference points. The transformation coefficients calculated using the virtual points are used to determine the position of the vessels and are also simultaneously used to calibrate the industrial camera. The method has been verified under real conditions, and the results obtained are average 30% more accurate compared to the traditionally used projective transformation using a small number of real points.
  PubDate: Fri, 18 Feb 2022 00:00:00 GMT
A Kalman Filter Based Hybrid Routing Protocol for Efficient Vehicle
Connectivity and Traffic Management
- Abstract: The geographic routing protocols in Vehicular Ad Hoc Networks (VANETs) are contemplated as most efficacious protocols. Though, such types of protocols communicate a huge quantity of data that influence the network connectivity negatively. Also, out of bound issue is the second major disadvantage of geographic routing protocols. To provide a solution to these impediments, a novel K-PGRP (Kalman filter-Predictive Geographic Routing Protocol) routing protocol is proposed in this paper. K-PGRP is an improvement to PGRP (Predictive Geographic Routing Protocol) routing protocol and wields Kalman filter as a prediction module in PGRP routing protocol in order to anticipate the neighbor location and to select the propitious neighbor for advancing packets in both urban and highway framework which leads to efficient connectivity in the network and improves road safety. K-PGRP is then compared with PGRP, GPSR (Greedy Perimeter Stateless Routing) and GPCR (Greedy Perimeter Coordinator Routing) routing protocols in terms of throughput and packet delivery ratio metrics and outperformed all the simulation cases. The simulations were performed on MATLAB R2018a along with traffic simulator SUMO.
  PubDate: Fri, 18 Feb 2022 00:00:00 GMT
Occupancy Estimation at Bus Stops Through Wi-Fi Connectivity Assessment
– A Study in Guayaquil City
- Abstract: Guayaquil is one of the most congested cities on the American continent. Several users cannot access on-time transport service according to the demand requested at bus stops. This work presents a simulation of the Wi-Fi technology to determine the number of users present at a bus stop. The objective is to know an estimation of them in different time intervals throughout the day, which could be useful for several actions, e.g., to set up a better distribution of transportation schedules or to place the bus stops in more demanding sites by users. The methodology included simulation scenarios for user counting at the bus stops. The results depicted that Wi-Fi technology performed suitable even in high-level attenuation scenarios.
  PubDate: Fri, 18 Feb 2022 00:00:00 GMT
Reliable and Seamless Communications of Networked IAV in Container
Terminal Using UAV Technology
- Abstract: Managing container loading and unloading operations at a container terminal using Intelligent Autonomous Vehicle (IAV) is challenging, especially at intersections in the yard, which are often inevitable. For ensuring efficient and accident-free management of these intersections, the IAV must cooperate by exchanging messages. Due to signal obstruction at these intersections, indirect communication is established through an additional relay node to ensure reliable communication. This paper proposes distributed approach using Unmanned Aerial Vehicle (UAV) connectivity to avoid collision and deadlock between IAV at intersections. Due to the obstacles formed by stacked containers blocking the radio transmissions, the proposed algorithm automatically switches the ongoing communication through the UAV to ensure successful communication at intersections. Thus, the idea of introducing UAV as communication relay in a container terminal is an interesting solution that we have adopted in this work. Simulation techniques are used to evaluate our proposal. The obtained results confirm that our UAV-based approach ensures reliable communication and automated intersections management in the yard while further ensuring the safety of IAV traffic.
  PubDate: Fri, 18 Feb 2022 00:00:00 GMT
Efficiency of Ship Operation in Transportation of Oversized and Heavy
Cargo by Optimizing the Speed Mode Considering the Impact of Weather
Conditions
- Abstract: Prior to commencing the voyage planning procedure, the entire navigation area on the forthcoming passage should be divided into several sections, depending on various factors, such as traffic density, restricted depths, availability of ship reporting systems, hydrometeorological conditions, high risk navigation areas in order to outline measures to ensure the safety of ship’s navigation. In addition, these factors have direct impact on the ship’s speed during the voyage. On the other hand, slow steaming can reduce fuel consumption on the same section of the route by 10-15%. Reducing the ship’s speed can significantly minimize its operating costs. However, when choosing economically feasible ship’s speed it should be kept in mind that reducing the speed can lead to a significant decrease in the number of voyages per year and, consequently, to the reduction of annual freight income. Therefore, a practically important and relevant problem is the necessity to find the speed of ship operation, which will provide a balance between economical fuel consumption and profits from ship operation. The classic approach to optimizing the speed mode of ships is based on the cubic dependence of fuel consumption on speed. Therefore, depending on the distance between ports, the duration of the voyage and the conditions for the time of arrival of the ship to the port, in the modern publications were proposed approaches to the optimization of the speed mode, based on the additional income or time-charter equivalent.
  PubDate: Fri, 18 Feb 2022 00:00:00 GMT
Competence of Bus Rapid Transit Systems Coupled with Transit Signal
Priority at Signalized Junctions
- Abstract: One of the primary causes of poor public transport performance is delays at intersections. Among the efficient and sustainable solutions to boost mass transportation performance, Bus Rapid Transit (BRT) consists of infrastructures integrating dedicated bus lanes and smart operational service with different ITS technologies like Transit Signal Priority (TSP). This research studies the competence of buses operating on junctions of the BRT corridor where they have Signal Priority on the dedicated lane. The studied intersection is located around the center of the Addis Ababa BRT-B2 line, which is relatively gentle grade and characterized by the high traffic and pedestrian volume. Microscopic models were created for the chosen intersection, along with possible calibration and validation; moreover, a statistical comparison was performed to evaluate different scenarios with the goal of displaying the deployment benefits. To assess the performance of BRT buses and their overall influence on general traffic, scenarios with and without TSP were evaluated. PTV VISSIM and the VisVAP add-on simulation program were used to examine TSP alternatives. Incorporating TSP reduced the travel time by up to 4.78% in the priority direction, the average travel speed increased by 7.25%, and the queue length also reduced by a maximum of 6%, whereas in the non-priority direction, the queue length increased by a maximum of 2.5%. Moreover, the overall average passenger delay has reduced by an average amount of 15%. One of the simplest ways to improve transit performance could be signal priority strategies, which has a minor influence on the general traffic.
  PubDate: Fri, 18 Feb 2022 00:00:00 GMT
Crash Distribution Dataset: Development and Validation for the Undivided
Rural Roads in Oromia, Ethiopia
- Abstract: Predicting the number of crashes that may occur as a result of specific highway features is critical in evaluating different treatment or design alternatives. Since different highway geometric characteristics can influence crash distribution datasets, Highway Safety Manual’s (HSM’s) predictive method encourages users to predict crashes based on their severity and collision type proportions. This study used crash data from rural two-way two-lane road segments in the Oromia region over seven years to develop Oromia’s fixed crash distribution dataset on Interactive Highway Safety Design Model (IHSDM) software. The crash distribution dataset has two parts; the crash severity proportions and the collision type percentages. The developed Oromia’s fixed crash distribution dataset was compared and validated against the default HSM crash configuration. As a result, the Crash Prediction Model (CPM) evaluation results confirmed that the developed crash severity proportion (the first part of the crash distribution dataset) estimates are more accurate and closer to the observed values. Furthermore, the findings show that crashes in the Oromia region are severer than in the states where the HSM crash configuration was developed. According to the second part of the crash distribution dataset evaluation (collision type percentage), the developed fixed crash distribution dataset outperforms the default HSM configuration in most collision type proportions, but not in all. For instance, from the ten collision type proportions developed, Right-Angle and sides-wipe collision proportions are predicted more precisely by the default HSM configuration. This points to the need for developing collision type proportion (the second part of the crash distribution dataset) as a function rather than a fixed configuration for a better result, based on the availability of complete crash data (i.e. crash location). In general, the study revealed that in order to exploit the full potential of HSM’s predictive approach, researchers must develop a jurisdiction crash distribution dataset using local crash data. The methodology demonstrated in this study to develop the jurisdiction’s crash distribution dataset has been validated as true thus, safety practitioners are encouraged to adopt it.
  PubDate: Fri, 18 Feb 2022 00:00:00 GMT
Modeling of Mobile and Fixed Broadband Subscriptions of Countries with
Fractional Calculus
- Abstract: Today, operators have become the resource of telecommunications data. Therefore, knowledge about subscriptions is easily available for most countries. In recent years, high-speed mobile internet access subscriptions are also increasing rapidly. There are two important subscriptions reviewed by The International Telecommunication Union (ITU): mobile broadband (MBB) subscriptions and Fixed broadband (FBB) subscriptions. In this study, we proposed an original mathematical model employing the fractional analysis theory and evaluate its validity by modeling the mobile broadband and fixed broadband subscriptions of six countries including France, Italy, Turkey, Germany, Spain, and the U.K. Later, we compared the Fractional Model we developed with the Polynomial Model. The results show that the Fractional Model is superior to the conventional Polynomial Model in modeling broadband subscriptions. For all selected countries, our proposed Fractional approach outperforms conventional polynomial regression. For all investigation categories, on average, the fractional approach works better by at least 10% and at most 30%.
  PubDate: Fri, 18 Feb 2022 00:00:00 GMT
Object and Lane Detection Technique for Autonomous Car Using Machine
Learning Approach
- Abstract: The main objective of this work is to develop a perception algorithm for self-driving cars which is based on pure vision data or camera data. The work is divided into two major parts. In part one of the work, we develop a powerful and robust lane detection algorithm which can determine the safely drive-able region in front of the car. In part two we develop and end to end driving model based on CNNs to learn from the drivers driving data and can drive the car with only the camera data from on-board cameras. Performance of the proposed system is observed by the implementation of the autonomous car that can be able to detect and classify the stop signs and other vehicles.
  PubDate: Sat, 20 Nov 2021 00:00:00 GMT
Urban Travel Behavior and Socio-Spatial Issues in the Mena Region: What Do
We Know'
- Abstract: Unlike literature and studies coming from high-income or Western countries, the existing conducted on the Middle East and North Africa fail to draw a nearly complete image of the characteristics of passenger travel behaviors in the urban areas of the region. This gap necessitates a holistic review of the previous studies and comparing their results of those of the international findings. This paper summarizes the status of urban travel behavior studies on the MENA region under eight categories of socioeconomics, land use, perceptions and attitudes, urban sprawl, neighborhood design, public transportation use, active mobility, and new technologies and concepts. Descriptive literature review and desk research depicts both lack of research results or data and differences between the behaviors in the MENA region and the Western countries. Moreover, based on the background review, this paper provides a list of recommendations for having more sustainable mobility in the MENA region.
  PubDate: Sat, 20 Nov 2021 00:00:00 GMT
Development of Reliable Models of Signal-Controlled Intersections
- Abstract: The paper considers an approach to building various mathematical models for homogeneous groups of intersections manifested through the use of clustering methods. This is because of a significant spread in their traffic capacity, as well as the influence of several random factors. The initial data on the traffic flow of many intersections was obtained from real-time recorders of the convolutional neural network. As a result of the analysis, we revealed statistically significant differences between the groups of intersections and compiled their linear regression models as a basis for the subsequent formation of generic management decisions. To demonstrate visually the influence of random factors on the traffic capacity of intersections, we built distribution fields based on the fuzzy logic methods for one of the clusters consisting of 14 homogeneous intersections. Modeling was based on the Gaussian type of membership functions as it most fully reflects the random nature of the pedestrian flow and its discontinuity.
  PubDate: Sat, 20 Nov 2021 00:00:00 GMT
Aviation Profiling Method Based on Deep Learning Technology for Emotion
Recognition by Speech Signal
- Abstract: This paper proposes a method of automatic speaker-independent recognition of human psycho-emotional states by analyzing the speech signal based on Deep Learning technology to solve the problems of aviation profiling. For this purpose, an algorithm to classify seven human psycho-emotional states, including anger, joy, fear, surprise, disgust, sadness, and neutral state was developed. The algorithm is based on the use of Mel-frequency cepstral coefficients and Mel spectrograms as informative features of speech signals audio recordings. These informative features are used to train two deep convolutional neural networks on the generated dataset. The developed classifier testing on a delayed verification dataset showed that the metric for the multiclass fraction of correct answers’ accuracy is 0.93. The solution proposed in the paper can be in demand in human-machine interfaces creation, medicine, marketing, and in the field of air transportation.
  PubDate: Sat, 20 Nov 2021 00:00:00 GMT