{"id":3480,"date":"2024-04-29T06:19:43","date_gmt":"2024-04-29T06:19:43","guid":{"rendered":"https:\/\/tttp-au.com\/?p=3480"},"modified":"2024-04-29T06:22:08","modified_gmt":"2024-04-29T06:22:08","slug":"application-of-a-new-model-for-fatigue-identification-of-commercial-vehicles-drivers","status":"publish","type":"post","link":"https:\/\/tttp-au.com\/index.php\/2024\/04\/29\/application-of-a-new-model-for-fatigue-identification-of-commercial-vehicles-drivers\/","title":{"rendered":"Application of a new Model for Fatigue Identification of Commercial Vehicles Drivers"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3480\" class=\"elementor elementor-3480\" data-elementor-settings=\"{&quot;ha_cmc_init_switcher&quot;:&quot;no&quot;}\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e232701 elementor-section-height-min-height elementor-section-full_width elementor-hidden-tablet elementor-hidden-mobile elementor-section-height-default elementor-section-items-middle wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no\" data-id=\"e232701\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cb97594\" data-id=\"cb97594\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a18c791 elementor-widget__width-inherit ha-has-bg-overlay elementor-widget elementor-widget-heading\" data-id=\"a18c791\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Vol. 4 No. 1-2 (2019): TTTP - APEIRON<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b2fff26 elementor-widget elementor-widget-heading\" data-id=\"b2fff26\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><b>Jelica Davidovi\u0107, Dalibor Pe\u0161i\u0107, Boris Anti\u0107<b><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-52044bb elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"52044bb\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1c82606 elementor-widget elementor-widget-heading\" data-id=\"1c82606\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Application of a new Model for Fatigue Identification of\nCommercial Vehicles Drivers\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c297c9e elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"c297c9e\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-21d12a9 e-grid e-con-full wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-parent\" data-id=\"21d12a9\" data-element_type=\"container\" data-settings=\"{&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t<div class=\"elementor-element elementor-element-0209b05 elementor-widget elementor-widget-heading\" data-id=\"0209b05\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Original scientific paper<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fd3ecf0 elementor-widget elementor-widget-heading\" data-id=\"fd3ecf0\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">DOI: 10.7251\/JTTTP1901005D<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2f3b63f elementor-align-center elementor-widget elementor-widget-button\" data-id=\"2f3b63f\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/tttp-au.com\/wp-content\/uploads\/2024\/04\/Pages-from-TTTP-Vol-4_No-1-2.pdf\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download Article PDF<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cb7c4cb elementor-widget__width-inherit elementor-widget elementor-widget-heading\" data-id=\"cb7c4cb\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-61aebe3 elementor-widget__width-inherit elementor-widget elementor-widget-text-editor\" data-id=\"61aebe3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>For decades, around the world is developing a fatigue detection system to alert drivers when they reach the state of fatigue that threatens them in tra\ufb03c. Most of the research on the impact of fatigue on drivers based on driving simulators mainly because it is a controlled environment, cheap and safe approach. Since the nineties of the last century, many surveys were conducted in which the survey method was applied, while examining the subjective attitudes of drivers about the impact of fatigue on tra\ufb03c safety. The beginning of the 21st century is characterized by the development of a fatigue detec- tion system based on modern technologies, and a number of experiments were conducted. However, it not yet in use tools that can be easily detected drivers fatigue, in order to respond quickly and prevent them from operating the vehicle in such condition.<\/p><p>The aim of this paper is to demonstrate the importance and implementation of a new fatigue identifica- tion model for commercial vehicle drivers in selected transport companies. Based on the results of this research, it is possible to determine which company is the safest from the aspect of fatigue, which is least safe. Also, the analysis of the results can determine which influencing factor is \u201cthe weakest link\u201d among the drivers in the transport company, or where to direct measures in order to improve the road safety of the company, and therefore the local community.<\/p><p>The study included five transport companies in Serbia, three of which are engaged in the carriage of passengers, and two transport goods. The survey used the survey method, the face face model, and 265 drivers of commercial vehicles participated, 16.6% of whom were found fatigued before the start of the shift.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5c5f19e elementor-widget elementor-widget-heading\" data-id=\"5c5f19e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Keywords : road safety, fatigue, model for fatigue identification, commercial vehicle drivers.\n\n\n \n\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6dd494a wpr-logo-position-center elementor-widget elementor-widget-wpr-logo\" data-id=\"6dd494a\" data-element_type=\"widget\" data-widget_type=\"wpr-logo.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\n\t\t\t<div class=\"wpr-logo elementor-clearfix\">\n\n\t\t\t\t\t\t\t\t<picture class=\"wpr-logo-image\">\n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/tttp-au.com\/wp-content\/uploads\/2024\/03\/cc-by-1.png\" alt=\"\">\n\n\t\t\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/tttp-au.com\/\"><\/a>\n\t\t\t\t\t\t\t\t\t<\/picture>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/tttp-au.com\/\"><\/a>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cb049d1 elementor-widget elementor-widget-shortcode\" data-id=\"cb049d1\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\"><div class=\"post-views content-post post-3480 entry-meta load-static\">\r\n\t\t\t\t<span class=\"post-views-icon dashicons dashicons-chart-bar\"><\/span> <span class=\"post-views-label\">Post Views:<\/span> <span class=\"post-views-count\">573<\/span>\r\n\t\t\t<\/div><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-511d0df elementor-section-height-min-height elementor-section-full_width elementor-hidden-mobile elementor-hidden-desktop elementor-hidden-laptop elementor-section-height-default elementor-section-items-middle wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no\" data-id=\"511d0df\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0acee9c\" data-id=\"0acee9c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c0e98b3 elementor-widget__width-inherit ha-has-bg-overlay elementor-widget elementor-widget-heading\" data-id=\"c0e98b3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Vol. 26 No. 2 (2023): JITA - APEIRON<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e6b43c5 elementor-widget elementor-widget-heading\" data-id=\"e6b43c5\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><strong>Igor Shubinsky, <\/strong>\n<strong> Alexey Ozerov<\/strong><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-68f1849 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"68f1849\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-186a6b2 elementor-widget elementor-widget-heading\" data-id=\"186a6b2\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7b2f501 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"7b2f501\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5027126 elementor-widget__width-initial elementor-widget elementor-widget-heading\" data-id=\"5027126\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Original scientific paper<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4a5312c elementor-widget__width-initial elementor-widget elementor-widget-heading\" data-id=\"4a5312c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">DOI: <a href=\"http:\/\/localhost\/mytestingsite\/wp-content\/uploads\/2024\/02\/PVTC-Technical-Requirements.pdf\" target=\"_blank\" rel=\"noopener\">https:\/\/doi.org\/10.7251\/JIT2302061S<\/a><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8d7a912 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"8d7a912\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/tttp-au.com\/wp-content\/uploads\/2024\/03\/10430-Article-Text-23890-1-10-20240103.pdf\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download Article PDF<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-580ffd3 elementor-widget__width-inherit elementor-widget elementor-widget-heading\" data-id=\"580ffd3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-211717e elementor-widget__width-inherit elementor-widget elementor-widget-text-editor\" data-id=\"211717e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers\u2019 expenditures.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-62b72c3 elementor-widget elementor-widget-heading\" data-id=\"62b72c3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f4dcd16 wpr-logo-position-center elementor-widget elementor-widget-wpr-logo\" data-id=\"f4dcd16\" data-element_type=\"widget\" data-widget_type=\"wpr-logo.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\n\t\t\t<div class=\"wpr-logo elementor-clearfix\">\n\n\t\t\t\t\t\t\t\t<picture class=\"wpr-logo-image\">\n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/tttp-au.com\/wp-content\/uploads\/2024\/03\/cc-by-1.png\" alt=\"\">\n\n\t\t\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/tttp-au.com\/\"><\/a>\n\t\t\t\t\t\t\t\t\t<\/picture>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/tttp-au.com\/\"><\/a>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0f73093 elementor-widget elementor-widget-shortcode\" data-id=\"0f73093\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\"><div class=\"post-views content-post post-3480 entry-meta load-static\">\r\n\t\t\t\t<span class=\"post-views-icon dashicons dashicons-chart-bar\"><\/span> <span class=\"post-views-label\">Post Views:<\/span> <span class=\"post-views-count\">573<\/span>\r\n\t\t\t<\/div><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2d980b8 elementor-section-height-min-height elementor-section-full_width elementor-hidden-tablet elementor-hidden-desktop elementor-hidden-laptop elementor-section-height-default elementor-section-items-middle wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no\" data-id=\"2d980b8\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d772afa\" data-id=\"d772afa\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5597455 elementor-widget__width-inherit elementor-widget-mobile__width-inherit ha-has-bg-overlay elementor-widget elementor-widget-heading\" data-id=\"5597455\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Vol. 26 No. 2 (2023): JITA - APEIRON<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f3e70ee elementor-widget elementor-widget-heading\" data-id=\"f3e70ee\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><strong>Igor Shubinsky, <\/strong>\n<strong> Alexey Ozerov<\/strong><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b0a72e2 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"b0a72e2\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-87a3678 elementor-widget elementor-widget-heading\" data-id=\"87a3678\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-96936f4 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"96936f4\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f04febc elementor-widget__width-initial elementor-widget-mobile__width-inherit elementor-widget elementor-widget-heading\" data-id=\"f04febc\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Original scientific paper<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-389228b elementor-widget__width-initial elementor-widget-mobile__width-inherit elementor-widget elementor-widget-heading\" data-id=\"389228b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">DOI: <a href=\"http:\/\/localhost\/mytestingsite\/wp-content\/uploads\/2024\/02\/PVTC-Technical-Requirements.pdf\" target=\"_blank\" rel=\"noopener\">https:\/\/doi.org\/10.7251\/JIT2302061S<\/a><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-73b8917 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"73b8917\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/tttp-au.com\/wp-content\/uploads\/2024\/03\/10430-Article-Text-23890-1-10-20240103.pdf\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download Article PDF<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-80faf79 elementor-widget__width-inherit elementor-widget elementor-widget-heading\" data-id=\"80faf79\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8cefffd elementor-widget__width-inherit elementor-widget elementor-widget-text-editor\" data-id=\"8cefffd\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers\u2019 expenditures.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0c5e44b elementor-widget elementor-widget-heading\" data-id=\"0c5e44b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dee52ab wpr-logo-position-center elementor-widget elementor-widget-wpr-logo\" data-id=\"dee52ab\" data-element_type=\"widget\" data-widget_type=\"wpr-logo.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\n\t\t\t<div class=\"wpr-logo elementor-clearfix\">\n\n\t\t\t\t\t\t\t\t<picture class=\"wpr-logo-image\">\n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/tttp-au.com\/wp-content\/uploads\/2024\/03\/cc-by-1.png\" alt=\"\">\n\n\t\t\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/tttp-au.com\/\"><\/a>\n\t\t\t\t\t\t\t\t\t<\/picture>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/tttp-au.com\/\"><\/a>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5cb9871 elementor-widget elementor-widget-shortcode\" data-id=\"5cb9871\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\"><div class=\"post-views content-post post-3480 entry-meta load-static\">\r\n\t\t\t\t<span class=\"post-views-icon dashicons dashicons-chart-bar\"><\/span> <span class=\"post-views-label\">Post Views:<\/span> <span class=\"post-views-count\">573<\/span>\r\n\t\t\t<\/div><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Vol. 4 No. 1-2 (2019): TTTP &#8211; APEIRON Jelica Davidovi\u0107, Dalibor Pe\u0161i\u0107, Boris Anti\u0107 Application of a new Model for Fatigue Identification of Commercial Vehicles Drivers Original scientific paper DOI: 10.7251\/JTTTP1901005D Download Article PDF Abstract For decades, around the world is developing a fatigue detection system to alert drivers when they reach the state of fatigue that threatens them in tra\ufb03c. Most of the research on the impact of fatigue on drivers based on driving simulators mainly because it is a controlled environment, cheap and safe approach. Since the nineties of the last century, many surveys were conducted in which the survey method was applied, while examining the subjective attitudes of drivers about the impact of fatigue on tra\ufb03c safety. The beginning of the 21st century is characterized by the development of a fatigue detec- tion system based on modern technologies, and a number of experiments were conducted. However, it not yet in use tools that can be easily detected drivers fatigue, in order to respond quickly and prevent them from operating the vehicle in such condition. The aim of this paper is to demonstrate the importance and implementation of a new fatigue identifica- tion model for commercial vehicle drivers in selected transport companies. Based on the results of this research, it is possible to determine which company is the safest from the aspect of fatigue, which is least safe. Also, the analysis of the results can determine which influencing factor is \u201cthe weakest link\u201d among the drivers in the transport company, or where to direct measures in order to improve the road safety of the company, and therefore the local community. The study included five transport companies in Serbia, three of which are engaged in the carriage of passengers, and two transport goods. The survey used the survey method, the face face model, and 265 drivers of commercial vehicles participated, 16.6% of whom were found fatigued before the start of the shift. Keywords : road safety, fatigue, model for fatigue identification, commercial vehicle drivers. Vol. 26 No. 2 (2023): JITA &#8211; APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https:\/\/doi.org\/10.7251\/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers\u2019 expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA &#8211; APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https:\/\/doi.org\/10.7251\/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers\u2019 expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.<\/p>\n","protected":false},"author":1,"featured_media":534,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-3480","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Application of a new Model for Fatigue Identification of Commercial Vehicles Drivers - TTTP Traffic and Transport Theory and Practice Journal<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/tttp-au.com\/index.php\/2024\/04\/29\/application-of-a-new-model-for-fatigue-identification-of-commercial-vehicles-drivers\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Application of a new Model for Fatigue Identification of Commercial Vehicles Drivers - TTTP Traffic and Transport Theory and Practice Journal\" \/>\n<meta property=\"og:description\" content=\"Vol. 4 No. 1-2 (2019): TTTP &#8211; APEIRON Jelica Davidovi\u0107, Dalibor Pe\u0161i\u0107, Boris Anti\u0107 Application of a new Model for Fatigue Identification of Commercial Vehicles Drivers Original scientific paper DOI: 10.7251\/JTTTP1901005D Download Article PDF Abstract For decades, around the world is developing a fatigue detection system to alert drivers when they reach the state of fatigue that threatens them in tra\ufb03c. Most of the research on the impact of fatigue on drivers based on driving simulators mainly because it is a controlled environment, cheap and safe approach. Since the nineties of the last century, many surveys were conducted in which the survey method was applied, while examining the subjective attitudes of drivers about the impact of fatigue on tra\ufb03c safety. The beginning of the 21st century is characterized by the development of a fatigue detec- tion system based on modern technologies, and a number of experiments were conducted. However, it not yet in use tools that can be easily detected drivers fatigue, in order to respond quickly and prevent them from operating the vehicle in such condition. The aim of this paper is to demonstrate the importance and implementation of a new fatigue identifica- tion model for commercial vehicle drivers in selected transport companies. Based on the results of this research, it is possible to determine which company is the safest from the aspect of fatigue, which is least safe. Also, the analysis of the results can determine which influencing factor is \u201cthe weakest link\u201d among the drivers in the transport company, or where to direct measures in order to improve the road safety of the company, and therefore the local community. The study included five transport companies in Serbia, three of which are engaged in the carriage of passengers, and two transport goods. The survey used the survey method, the face face model, and 265 drivers of commercial vehicles participated, 16.6% of whom were found fatigued before the start of the shift. Keywords : road safety, fatigue, model for fatigue identification, commercial vehicle drivers. Vol. 26 No. 2 (2023): JITA &#8211; APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https:\/\/doi.org\/10.7251\/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers\u2019 expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA &#8211; APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https:\/\/doi.org\/10.7251\/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers\u2019 expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/tttp-au.com\/index.php\/2024\/04\/29\/application-of-a-new-model-for-fatigue-identification-of-commercial-vehicles-drivers\/\" \/>\n<meta property=\"og:site_name\" content=\"TTTP Traffic and Transport Theory and Practice Journal\" \/>\n<meta property=\"article:published_time\" content=\"2024-04-29T06:19:43+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-04-29T06:22:08+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/tttp-au.com\/wp-content\/uploads\/2024\/03\/cover_issue_949_en_US.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"595\" \/>\n\t<meta property=\"og:image:height\" content=\"793\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/tttp-au.com\/index.php\/2024\/04\/29\/application-of-a-new-model-for-fatigue-identification-of-commercial-vehicles-drivers\/\",\"url\":\"https:\/\/tttp-au.com\/index.php\/2024\/04\/29\/application-of-a-new-model-for-fatigue-identification-of-commercial-vehicles-drivers\/\",\"name\":\"Application of a new Model for Fatigue Identification of Commercial Vehicles Drivers - 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TTTP Traffic and Transport Theory and Practice Journal","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/tttp-au.com\/index.php\/2024\/04\/29\/application-of-a-new-model-for-fatigue-identification-of-commercial-vehicles-drivers\/","og_locale":"en_US","og_type":"article","og_title":"Application of a new Model for Fatigue Identification of Commercial Vehicles Drivers - TTTP Traffic and Transport Theory and Practice Journal","og_description":"Vol. 4 No. 1-2 (2019): TTTP &#8211; APEIRON Jelica Davidovi\u0107, Dalibor Pe\u0161i\u0107, Boris Anti\u0107 Application of a new Model for Fatigue Identification of Commercial Vehicles Drivers Original scientific paper DOI: 10.7251\/JTTTP1901005D Download Article PDF Abstract For decades, around the world is developing a fatigue detection system to alert drivers when they reach the state of fatigue that threatens them in tra\ufb03c. Most of the research on the impact of fatigue on drivers based on driving simulators mainly because it is a controlled environment, cheap and safe approach. Since the nineties of the last century, many surveys were conducted in which the survey method was applied, while examining the subjective attitudes of drivers about the impact of fatigue on tra\ufb03c safety. The beginning of the 21st century is characterized by the development of a fatigue detec- tion system based on modern technologies, and a number of experiments were conducted. However, it not yet in use tools that can be easily detected drivers fatigue, in order to respond quickly and prevent them from operating the vehicle in such condition. The aim of this paper is to demonstrate the importance and implementation of a new fatigue identifica- tion model for commercial vehicle drivers in selected transport companies. Based on the results of this research, it is possible to determine which company is the safest from the aspect of fatigue, which is least safe. Also, the analysis of the results can determine which influencing factor is \u201cthe weakest link\u201d among the drivers in the transport company, or where to direct measures in order to improve the road safety of the company, and therefore the local community. The study included five transport companies in Serbia, three of which are engaged in the carriage of passengers, and two transport goods. The survey used the survey method, the face face model, and 265 drivers of commercial vehicles participated, 16.6% of whom were found fatigued before the start of the shift. Keywords : road safety, fatigue, model for fatigue identification, commercial vehicle drivers. Vol. 26 No. 2 (2023): JITA &#8211; APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https:\/\/doi.org\/10.7251\/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers\u2019 expenditures. Keywords: predictive analysis, maintenance, functional safety, Big Data, Data Science, risk indicators. Vol. 26 No. 2 (2023): JITA &#8211; APEIRON Igor Shubinsky, Alexey Ozerov Application of Artificial Intelligence Methods for the Prediction of Hazardous Failures Original scientific paper DOI: https:\/\/doi.org\/10.7251\/JIT2302061S Download Article PDF Abstract The availability of real-time data on the state of railway facilities and the state-of-the art technologies for data collection and analysis allow transition to the fourth generation maintenance. It is based on the prediction of the facility functional safety and dependability and the risk-oriented facility management. The article describes an approach to assessing the risks of hazardous facility failures using the latest digital data processing methods. The implementation of this approach will help set maintenance objectives and contribute to the efficient use of resources and the reduction of railway facility managers\u2019 expenditures. 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