How to Reduce Work-Related Road Deaths? Driver Fatigue Monitoring - Case Study

被引:0
作者
Davidovic, Jelica [1 ]
Pesic, Dalibor [1 ]
Antic, Boris [1 ]
机构
[1] Univ Belgrade, Fac Transport & Traff Engn, Belgrade, Serbia
来源
PROMET-TRAFFIC & TRANSPORTATION | 2025年 / 37卷 / 01期
关键词
work related road deaths; fatigue; road safety; commercial vehicle drivers; road safety performance indicators; new fatigue identification model; NEW-ZEALAND; SLEEPINESS; CAFFEINE; DROWSINESS; BEHAVIORS; HABITS;
D O I
10.7307/ptt.v37i1.620
中图分类号
U [交通运输];
学科分类号
08 ; 0823 ;
摘要
Work-related road deaths are the leading cause of occupational death. These traffic accidents contribute to at least one quarter all work-related deaths. Key risk factors associated with driving for work are driver fatigue and speeding. Driver fatigue is the growing problem of the new era. Due to traffic exposure, commercial vehicles are identified as a particularly risky category. According to traffic accident data, depending on the country, the percentage of traffic accidents caused by driver fatigue ranges up to 40%. In this paper, we used a unique procedure for identifying fatigue based on eleven factors, using expert knowledge, budget allocation and the composite rank method. The case study was realised in the Republic of Serbia, which is a country with a huge professional drivers deficiency problem. The main objective of this paper is to present an approach to reducing work-related road deaths to reach vision zero, based on a model for identifying commercial vehicle driver fatigue before the drivers start their shift. The advantage of this model is that it does not distract the driver in any way while driving and is based on objective data. It does not require recording the driver with a camera or hooking up to an electrode to record heart or brain activity.
引用
收藏
页码:36 / 49
页数:14
相关论文
共 62 条
[1]  
Anund A., 2015, Countermeasures for Fatigue in Transportation: A Review of Existing Methods for Drivers on Road, Rail, Sea and in Aviation
[2]  
Association of transporters in Serbia, 2023, blog.klevercargo
[3]  
Broughton J, 2003, TRL Report TRL582
[4]  
Cameron M, 2004, INJURY PREV, V10, P255
[5]  
Carroll, 2003, P 2 INT DRIV S HUM F, P143, DOI [https://doi.org/10.17077/drivingassessment.1109, DOI 10.17077/DRIVINGASSESSMENT.1109]
[6]  
Charlton S., 2003, Analysis of fatigue levels in New Zealand taxi and local-route truck drivers
[7]  
Charlton SG, 2001, NEW ZEAL J PSYCHOL, V30, P32
[8]   Real-time driver fatigue detection system with deep learning on a low-cost embedded system [J].
Civik, Esra ;
Yuzgec, Ugur .
MICROPROCESSORS AND MICROSYSTEMS, 2023, 99
[9]   The impact of 7-hour and 11-hour rest breaks between shifts on heavy vehicle truck drivers' sleep, alertness and naturalistic driving performance [J].
Cori, Jennifer M. ;
Downey, Luke A. ;
Sletten, Tracey L. ;
Beatty, Caroline J. ;
Shiferaw, Brook A. ;
Soleimanloo, Shamsi Shekari ;
Turner, Sophie ;
Naqvi, Aqsa ;
Barnes, Maree ;
Kuo, Jonny ;
Lenne, Michael G. ;
Anderson, Clare ;
Tucker, Andrew J. ;
Wolkow, Alexander P. ;
Clark, Anna ;
Rajaratnam, Shantha M. W. ;
Howard, Mark E. .
ACCIDENT ANALYSIS AND PREVENTION, 2021, 159 (159)
[10]  
Cui J., 2021, arXiv, DOI [10.48550/arXiv.2106.00613, DOI 10.48550/ARXIV.2106.00613]