Transport and Telecommunication Journal最新文献_第6页

Laboratory Experiments on Soil Stabilization to Enhance Strength Parameters for Road Pavement 提高路面强度参数的土壤稳定室内试验

IF 1.4

Transport and Telecommunication Journal Pub Date : 2023-02-01 DOI: 10.2478/ttj-2023-0008

P. Lindh, Polina Lemenkova

{"title":"Laboratory Experiments on Soil Stabilization to Enhance Strength Parameters for Road Pavement","authors":"P. Lindh, Polina Lemenkova","doi":"10.2478/ttj-2023-0008","DOIUrl":"https://doi.org/10.2478/ttj-2023-0008","url":null,"abstract":"Abstract Clay soils can cause significant distress in road construction due to their low strength. Stabilizing such soil improve with binder agents prior to the geotechnical works can significantly its performance and ensure safety and stability of roads while exploitation. This research envisaged the use of five different binders (lime, energy fly ash, bio fly ash, slag, cement) as an additive stabilizing agents to improve the strength parameters of soil as required in engineering industry standards. The variations of strength was assessed using measurements of P-wave velocity of the elastic waves propagating through soil specimens stabilized by different combination of binders. Measurements were performed on 28th day of soil treatment. The best effects of added binders were noted in the following combinations: cement / energy fly ash / bio fly ash (P-waves >3100 m/s), followed by combination lime / energy fly ash / GGBFS (P-waves >2800 m/s) and cement / lime / energy fly ash (P-waves >2700 m/s). Adding lime is effective due to its fixation and chemical bond with particles. The study contributes to the industrial tests on soil strength for constructing roadbed.","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":"91129655","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}

引用次数: 3

Autonomous Cargo Bike Fleets – Approaches for AI-Based Trajectory Forecasts of Road Users 自动货运自行车车队——基于人工智能的道路使用者轨迹预测方法

IF 1.4

Transport and Telecommunication Journal Pub Date : 2023-02-01 DOI: 10.2478/ttj-2023-0006

Stefan Sass, Markus Höfer, Michael Schmidt, S. Schmidt

{"title":"Autonomous Cargo Bike Fleets – Approaches for AI-Based Trajectory Forecasts of Road Users","authors":"Stefan Sass, Markus Höfer, Michael Schmidt, S. Schmidt","doi":"10.2478/ttj-2023-0006","DOIUrl":"https://doi.org/10.2478/ttj-2023-0006","url":null,"abstract":"Abstract Automated cargo bikes are intended to complement public transportation in a sharing concept and provide an alternative transportation option for people and goods. In highly automated driving without a seated user, real-time trajectory prediction of other road users is crucial for collision avoidance with other motor vehicles or vulnerable road users (VRU). For this purpose, moving obstacles are detected by environmental sensors and classified and tracked using object detection and tracking algorithms. The current and past position data as well as environmental information are used to predict future positions. In this paper, we present several AI-based trajectory prediction models that are specifically suited for this use case. Our focus is not only on the accuracy of trajectory prediction, but additionally on a robust, real-time and practical application. We consider models that can predict the trajectories with position estimation or distributions for position estimation for each time step in the future. For this aim, we present generative network structures based on Conditional Variational Autoencoder (CVAE) in different variants. After training, the models are integrated into our production system and their computation time is determined on the hardware we use.","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":"83230880","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}

引用次数: 0

Assessment of the Interaction of the Logistics Company’s Information Technologies with the Technological Infrastructure 物流企业信息技术与技术基础设施互动评价

IF 1.4

Transport and Telecommunication Journal Pub Date : 2023-02-01 DOI: 10.2478/ttj-2023-0005

Darius Bazaras, Kristina Čižiūnienė, Kristina Vaičiūtė

{"title":"Assessment of the Interaction of the Logistics Company’s Information Technologies with the Technological Infrastructure","authors":"Darius Bazaras, Kristina Čižiūnienė, Kristina Vaičiūtė","doi":"10.2478/ttj-2023-0005","DOIUrl":"https://doi.org/10.2478/ttj-2023-0005","url":null,"abstract":"Abstract The continuously changing market of companies offering logistics services has challenged logistics organizations to adapt to the needs of service users and providers. Faster information processing offers new ways of communication with suppliers and optimization of distribution systems. Increasing information flows have an increasing potential to affect the management, structure, functioning and development of enterprises. The development of technological infrastructure and information technology systems by organizations allows service participants to exchange information, adjust information flows and restore relevant information through the use of technology, which poses new challenges for resource management. The level of innovation of production processes, productivity and the quality of products directly depends on the transmission of information and technology. To successfully work in the existing market, organizations must not only purchase IT systems or technologies but also constantly upgrade system applications, improve technologies or acquire brand-new IT systems. The article analyses the peculiarities of the impact of the development of technological infrastructure and its use as an instrument for the development of logistics organizations and quality assurance of logistics services. The quality research carried out allowed identifying the problems relating to technological infrastructure in logistics organizations.","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":"81826897","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}

引用次数: 0

Lidar and AI Based Surveillance of Industrial Process Environments 基于激光雷达和人工智能的工业过程环境监控

IF 1.4

Transport and Telecommunication Journal Pub Date : 2023-02-01 DOI: 10.2478/ttj-2023-0002

Maik Groneberg, Olaf Poenicke, Chirag Mandal, Nils Treuheit

引用次数: 0

Dynamic Traveling Route Planning Method for Intelligent Transportation Using Incremental Learning-Based Hybrid Deep Learning Prediction Model with Fine-Tuning 基于增量学习和微调的混合深度学习预测模型的智能交通动态出行路线规划方法

IF 1.4

Transport and Telecommunication Journal Pub Date : 2022-11-01 DOI: 10.2478/ttj-2022-0024

Shridevi Jeevan Kamble, Manjunath R. Kounte

{"title":"Dynamic Traveling Route Planning Method for Intelligent Transportation Using Incremental Learning-Based Hybrid Deep Learning Prediction Model with Fine-Tuning","authors":"Shridevi Jeevan Kamble, Manjunath R. Kounte","doi":"10.2478/ttj-2022-0024","DOIUrl":"https://doi.org/10.2478/ttj-2022-0024","url":null,"abstract":"Abstract 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).","PeriodicalId":44110,"journal":{"name":"Transport and Telecommunication Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90468156","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}

引用次数: 0

Smartphone-Based Recognition of Access Trip Phase to Public Transport Stops Via Machine Learning Models 基于智能手机的基于机器学习模型的公共交通站点访问阶段识别

IF 1.4

Transport and Telecommunication Journal Pub Date : 2022-11-01 DOI: 10.2478/ttj-2022-0022

Seyed Hassan Hosseini, Guido Gentile

{"title":"Smartphone-Based Recognition of Access Trip Phase to Public Transport Stops Via Machine Learning Models","authors":"Seyed Hassan Hosseini, Guido Gentile","doi":"10.2478/ttj-2022-0022","DOIUrl":"https://doi.org/10.2478/ttj-2022-0022","url":null,"abstract":"Abstract 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%.","PeriodicalId":44110,"journal":{"name":"Transport and Telecommunication Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73123726","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}

引用次数: 1

Relevance of Regulatory and Data Availability Issues to Transport and Logistics Processes, Based on the Insights of the Epicenter Project 监管和数据可用性问题与运输和物流过程的相关性，基于震中项目的见解

IF 1.4

Transport and Telecommunication Journal Pub Date : 2022-11-01 DOI: 10.2478/ttj-2022-0028

Darius Bazaras, Viktor Skrickij, A. Šakalys, Raimondas Šakalys

引用次数: 0

Ec-Funded Projects’ Lessons Learned In Earth Friendly Freight Transportation 环保项目在环保货运方面的经验教训

IF 1.4

Transport and Telecommunication Journal Pub Date : 2022-11-01 DOI: 10.2478/ttj-2022-0029

Irina Yatskiv (Jackiva), Thomas Brauner, Aleksejs Vesjolijs, V. Petrovs

{"title":"Ec-Funded Projects’ Lessons Learned In Earth Friendly Freight Transportation","authors":"Irina Yatskiv (Jackiva), Thomas Brauner, Aleksejs Vesjolijs, V. Petrovs","doi":"10.2478/ttj-2022-0029","DOIUrl":"https://doi.org/10.2478/ttj-2022-0029","url":null,"abstract":"Abstract The paper is based on the research project ePIcenter (www.ePIcenterproject.eu) 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. 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.","PeriodicalId":44110,"journal":{"name":"Transport and Telecommunication Journal","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73914628","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}

引用次数: 0

Highway Vehicles’ Overtaking Classification 公路车辆超车分类

IF 1.4

Transport and Telecommunication Journal Pub Date : 2022-11-01 DOI: 10.2478/ttj-2022-0023

Hamid Shimal Aldulaimi, Bassem Ben Hamed

引用次数: 0

Risk Assessment of the Operation of Aviation Maintenance Personnel Trained on Virtual Reality Simulators 基于虚拟现实模拟器的航空维修人员操作风险评估

IF 1.4

Transport and Telecommunication Journal Pub Date : 2022-11-01 DOI: 10.2478/ttj-2022-0026

Zharas Ainakulov, I. Pirmanov, K. Koshekov, N. Astapenko, I. Fedorov, D. Zuev, G. Kurmankulova

引用次数: 1