Transport and Telecommunication Journal最新文献

{"title":"Fractional Vehicle Ownership and Revenue Generation Through Blockchain Asset Tokenization","authors":"Elena Viţelaru, Luca Persia","doi":"10.2478/ttj-2023-0011","DOIUrl":"https://doi.org/10.2478/ttj-2023-0011","url":null,"abstract":"Abstract The automotive industry is on a continuous transition towards a more sustainable and integrated ecosystem influenced by the fast-paced adoption of Electrical Vehicles (EVs) and the developments of emerging technologies such as Automated Vehicles (AVs). The road transportation sector is also experimenting with the emergent decentralized blockchain technology in various ways ranging from supply chain transparency to insurance and tokenization. Some of the recent use cases are the use of Non-Fungible Tokens (NFTs), unique digital assets designed to be immutable, to certify ownership of a vehicle, the data history of it or just for fan base development. The current paper reviews the literature findings concerning the potential of Non-Fungible Tokens for the automotive industry and proposes a new car ownership and revenue generation model using the ERC-1155 token standard. Our proof-of-concept based on fractional vehicle ownership demonstrates the feasibility of such a model that allows for revenue distribution amongst the vehicle owners according to the percentile invested in the vehicle acquisition.","PeriodicalId":44110,"journal":{"name":"Transport and Telecommunication Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78565783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Model for Identifying Road Risk Class","authors":"A. Ryguła, K. Brzozowski, A. Maczyński","doi":"10.2478/ttj-2023-0015","DOIUrl":"https://doi.org/10.2478/ttj-2023-0015","url":null,"abstract":"Abstract In many road safety, traffic management, and travel planning analyses, it is useful to classify road sections according to risk level. Such classification is labour-intensive and needs to be reviewed periodically. The authors propose a model for identifying a discrete risk class for road sections based on selected traffic flow parameters, which are available in most measurement systems monitoring current traffic conditions. The Surrogate Safety Measures approach was applied in the model formulated using Principal Components Analysis. As input to the model SSMs are used in the form of a set of hourly average traffic flow parameters. The SSMs used are: the percentage of light vehicles exceeding the speed limit by a value in the range 21 to 30 km/h; the percentage of light vehicles exceeding the speed limit by more than 30 km/h; the traffic volume of light vehicles; the traffic volume of heavy vehicles and the mean speeds of light vehicles and heavy vehicles. This paper presents results of calculations of risk class obtained from the model for different locations on single-carriageway two-lane roads in Poland. Satisfactory compliance of risk classes designated by the road operator and identified by the model based on current traffic data was achieved. The proposed model can be used as the core of an effective alternative road safety screening method.","PeriodicalId":44110,"journal":{"name":"Transport and Telecommunication Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83308677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Microscopic Analysis of the Relationship Between Prior Knowledge About Self-Driving Cars and the Public Acceptance: A Survey in the US","authors":"Kareem Othman","doi":"10.2478/ttj-2023-0012","DOIUrl":"https://doi.org/10.2478/ttj-2023-0012","url":null,"abstract":"Abstract Previous studies have shown that the level of awareness of SDVs is a deciding factor that affects the public attitude towards this emerging technology; however, none of these studies focuses on understanding the relationship between these two variables. Thus, this study utilizes a questionnaire survey with the objective of drawing the relationship between the public attitude and level of knowledge. A total of 2447 complete responses were revised from participants from the US. The results show that people with prior knowledge about SDVs are more likely to travel on SDVs. However, participants who know a bit about SDVs were the most likely to travel on SDVs when compared with participants who had no knowledge and participants who know a lot about SDVs. In addition, the analysis shows that the relationship between the level of knowledge and the level of acceptance of SDVs is not linear but rather parabolic.","PeriodicalId":44110,"journal":{"name":"Transport and Telecommunication Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74691393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification of the Condition of Pavement with the Use of Machine Learning Methods","authors":"Pawel Tomilo","doi":"10.2478/ttj-2023-0014","DOIUrl":"https://doi.org/10.2478/ttj-2023-0014","url":null,"abstract":"Abstract The publication includes a review of information on the methods of pavement condition recognition using various methods. Measurement system has been presented that allows to determine the condition of the pavement using the Inertial Measurement Unit (IMU) and machine learning methods. Three machine learning methods were considered: random forest, gradient boosted tree and custom architecture neural network (roadNet). Due to the developed system the set of learning and validation data was created on 3 vehicles: Opel Corsa, Honda Accord, Volkswagen Passat. All of the listed vehicles have front wheel drive. The presented machine learning methods have been compared with each other. The best accuracy on the validation set was achieved by the artificial neural network (ANN). The study showed that asphalt condition classification is possible and the developed system fulfils its task.","PeriodicalId":44110,"journal":{"name":"Transport and Telecommunication Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91378200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends and Open Research Issues in Intelligent Internet of Vehicles","authors":"Shridevi Jeevan Kamble, Manjunath R. Kounte","doi":"10.2478/ttj-2023-0013","DOIUrl":"https://doi.org/10.2478/ttj-2023-0013","url":null,"abstract":"Abstract The evolution of vehicles has always been continuous with respect to growth in technology.The concept of the Internet of Vehicles (IoV) is the process of allowing vehicles to interact with each other to provide real-time information. This paper introduces the various aspects of IoV and their components. Despite the fact that there are more and more vehicles connected to the IoV, there are still many unknown issues and potentials that needs to be identified to carry out research. In order to identify and classify the current difficulties in implementing and using IoV in urban cities, various research publications on the topic were analysed in this paper. The limitations of the Internet of Vehicular technology are also described. Additionally, a number of current and potential remedies that address the highlighted problems were briefly covered. The background information and reasons for evolving heterogeneous vehicular networks are thoroughly reviewed in this research. Also highlights the key technologies of IoV, network architecture and comparison of IoV architecture models with focus on different communication models The most modern IoV enabling technologies are also highlighted, along with environmental scope of intelligent internet of vehicles. Finally, the paper has reviewed the open research issues of Intelligent IoV such as Poor Connectivity of on road vehicles and Stability, Hard delay constraints, High reliability requirements, high scalability, Security and privacy, etc. and related solutions.","PeriodicalId":44110,"journal":{"name":"Transport and Telecommunication Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90885907","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Railway Transport Adaptation Strategies to Climate Change at High Latitudes: A Review of Experience from Canada, Sweden and China","authors":"E. Kostianaia, A. Kostianoy","doi":"10.2478/ttj-2023-0016","DOIUrl":"https://doi.org/10.2478/ttj-2023-0016","url":null,"abstract":"Abstract Impact of climate change on railway transport manifests in a variety of consequences, such as rail buckling, rail flooding, expansion of swing bridges, overheating of electrical equipment and its damage, bridge scour, failure of earthworks, ground settlement, pavement deterioration, damage to sea walls, coastal erosion of tracks and earthworks, and an increased number of railway accidents in general. Such impacts can cause considerable disruption of railway operations and lead to substantial financial expenses for repair of the railway infrastructure. Therefore, it is crucial to include adaptation strategies already in the design phase of the railway construction to ensure stability and integrity of the railway operations. This paper provides a literature review of adaptation considerations in Canada, China and Sweden and discusses climate change challenges that these countries face in their railway systems. In conclusion, the authors provide recommendations for adaptation approaches based on the reviewed international experience which can be useful for policymakers and managers of railway companies.","PeriodicalId":44110,"journal":{"name":"Transport and Telecommunication Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86346962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast Timeline Based Multi Object Online Tracking","authors":"Martin Hünermund, Maik Groneberg, Nils Brauckmann","doi":"10.2478/ttj-2023-0007","DOIUrl":"https://doi.org/10.2478/ttj-2023-0007","url":null,"abstract":"Abstract Fast state-of-the-art multi-object-tracking (MOT) schemes, such as reported in challenges MOT16 and Mot20, perform tracking on a single sensor, often couple tracking and detection, support only one kind of object representation or don’t take varying latencies and update rates into account. We propose a fast generic MOT system for use in real world applications which is capable of tracking objects from different sensor / detector types with their respective latencies and update rates. An SORT inspired online tracking scheme is extended by time awareness using timelines as unifying concept. The system supports different object, sensor and filter and tracking types by modularizing and generalizing the online tracking scheme, while ensuring high performance using an efficient data-oriented C++-template-based implementation. Using the proposed system we achieve, with comparable evaluation metrics, framerates up to ten times higher than the fastest MOT schemes publicly listed for the axis-aligned bounding-box tracking challenges MOT17 and MOT20.","PeriodicalId":44110,"journal":{"name":"Transport and Telecommunication Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76572169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Utilizing Voip Packet Header’s Fields to Save the Bandwidth","authors":"Mosleh M. Abualhaj, A. Abu-Shareha, Sumaya N. Al-Khatib","doi":"10.2478/ttj-2023-0004","DOIUrl":"https://doi.org/10.2478/ttj-2023-0004","url":null,"abstract":"Abstract Voice over IP (VoIP) is widely utilized by organizations, schools, colleges, and so on. Nevertheless, VoIP numerous challenges that hinder its spread. One of the significant challenges is the poor exploit of the VoIP technology network bandwidth (BW), caused by the huge preamble of the VoIP packet. This paper suggests a novel methodology to manage this huge preamble overhead challenge. The proposed methodology is named runt payload VoIP packet (RPV). The core principle of the RPV methodology is to reemploy and exploit the VoIP packet preamble’s data (fields) that are superfluous by VoIP technology, especially for unicast IP voice calls. Generally, those fields will be used to convey the VoIP packet payload. Consequently, diminish or zero the length of the payload and, therefore, spare the BW. The results of the investigation into the suggested RPV methodology indicated significant enhancement in the BW exploitation of VoIP technology. For instance, the saved BW in the examined environment with the LPC codec came to up to 25.9%.","PeriodicalId":44110,"journal":{"name":"Transport and Telecommunication Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89651780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of Spatio-Temporal Granularity on Demand Prediction for Deep Learning Models","authors":"Ken Koshy Varghese, Sajjad Mahdaviabbasabad, Guido Gentile, Mohamed Eldafrawi","doi":"10.2478/ttj-2023-0003","DOIUrl":"https://doi.org/10.2478/ttj-2023-0003","url":null,"abstract":"Abstract Advances in machine learning technology and the availability of big data from GPS systems have led to the development of effective methods for modelling transportation demand and forecasting the future. Most previous research concentrated on demand prediction using a variety of machine learning and deep learning models that took into account spatial and temporal relationships. This paper investigates the impact of spaces and time granularity for a Spatio-temporal demand modelling framework. Using taxi demand data from New York City, our study compares the prediction performance of deep learning models such as Long Short-Term Memory (LSTM), Convolution Neural Networks (CNN) and Temporal-Guided Networks (TGNet), modelled with a grid-based tessellation strategy. The findings of this study could assist researchers in better understanding how the granularity of space and time helps deep learning models perform better for demand forecasting problems.","PeriodicalId":44110,"journal":{"name":"Transport and Telecommunication Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91326842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Machine Learning Techniques to Incorporate Social Priorities in Traffic Monitoring in a Junction with a Fast Lane","authors":"O. Barzilai, Havana Rika, Nadav Voloch, Maor Meir Hajaj, O. L. Steiner, N. Ahituv","doi":"10.2478/ttj-2023-0001","DOIUrl":"https://doi.org/10.2478/ttj-2023-0001","url":null,"abstract":"Abstract Traffic lights monitoring that considers only traffic volumes is not necessarily the optimal way to time the green/red allocation in a junction. A “smart” allocation should also consider the necessities of the vehicle’s passengers and the needs of the people those passengers ought to serve. This paper deals with a “smart” junction, where several cars approach the intersection from different directions and a traffic light is set to comply to a sequence of time intervals of red and green lights in each direction. The novel approach presented here is based not only on traffic congestion parameters, but also on the social and economic characteristics of the passengers (e.g. a handicapped person, a medical doctor, an employee who is extremely required in a certain organization due to an emergency situation). This paper proposes to enhance the smart junction with a fast lane, which has a flexible entry permit based on social and economic criteria. Machine learning (specifically, Reinforcement Learning (RL)) is added to the junction’s algorithm with the aim of optimizing the social utility of the junction. For the purposes of this study, the utility of the junction is defined by the total social and economic potential benefits given a certain red/green time allocation is set. This is defined as the measure of the reward function which contains positive factors for vehicles which crossed the junction or advanced their position and a negative factor for vehicles which remains in their positions. In addition, a weight value for the vehicles with high priority is also part of the equation. A simplified version of the smart junction has been used, serving as a model for incorporating RL into the “smart’ junction with Fast Lane (FL). Specifically, the Q-Learning algorithm is used to maximize the reward function. Simulation results show that prioritizing high priority vehicles via FL is influenced by the weights and factors given to the reward components. Farther research should enhance the “Smart” junction with FL to a more complex and realistic one using a varying amount of vehicles crossing the junction.","PeriodicalId":44110,"journal":{"name":"Transport and Telecommunication Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87208604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}