Article
Ergonomics
Ruici Zhang, Xiang Wen, Huanqiang Cao, Pengfei Cui, Hua Chai, Runbo Hu, Rongjie Yu
Summary: Identifying critical safety management drivers with high driver-level risks is essential for traffic safety improvement. Traditional modeling methods based on aggregated statistical characteristics are no longer feasible in the short-period driving data environment. Therefore, this study employed temporal variation characteristics of driving behavior to identify critical safety management drivers and improved model performance through the proposed traffic entropy index and deep learning models. The results showed that the proposed method outperformed traditional models in predicting individual crash occurrence probability.
ACCIDENT ANALYSIS AND PREVENTION
(2023)
Article
Green & Sustainable Science & Technology
Huacai Xian, Yujia Hou, Yu Wang, Shunzhong Dong, Junying Kou, Zewen Li
Summary: In this study, traffic heat maps were drawn using ArcGIS software based on vehicle GPS data of urban expressways in Jinan City. The relationship between risky driving behaviors and road types with traffic crashes was analyzed. An ordered logistic-based traffic safety evaluation model was established to predict the safety level of urban expressways, with an accuracy of 85.71% and good applicability. Results revealed that rapid deceleration was a significant influencing factor for crashes on urban expressways.
Article
Computer Science, Information Systems
Ryusei Kimura, Takahiro Tanaka, Yuki Yoshihara, Kazuhiro Fujikake, Hitoshi Kanamori, Shogo Okada
Summary: This paper focuses on estimating a driver's psychological characteristics using driving data, and develops a model to estimate cognitive function, psychological driving style, and workload sensitivity through machine learning and deep learning techniques. The experimental results show that the model can estimate a driver's cognitive function with high accuracy.
Article
Ergonomics
Ahmad Hassan, Chris Lee, Kenneth Cramer, Kathryn Lafreniere
Summary: By assessing driving behavior and characteristics of 55 drivers in 9 driving events, this study identified unique patterns and driver characteristics associated with aggressive driving. The findings recommend road safety policies be based on these results to reduce aggressive driving.
ACCIDENT ANALYSIS AND PREVENTION
(2023)
Article
Engineering, Civil
Bo Yang, Koichiro Inoue, Zhanhong Yan, Zheng Wang, Satoshi Kitazaki, Kimihiko Nakano
Summary: To ensure driving safety while using level 2 automated driving systems, it is important to understand the influence of these systems on driver behavior. Previous studies focused on drivers' reactions to emergency events, but it is still unclear how drivers interact with level 2 automated driving systems during normal conditions. In a driving simulator experiment, it was observed that drivers' attention levels, especially for the front areas, were significantly lower during level 2 automated driving compared to manual driving.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Engineering, Industrial
N. Zhang, M. Fard, J. Xu, J. L. Davy, S. R. Robinson
Summary: Driver drowsiness is a significant factor in serious motor vehicle accidents. This study found that specific frequencies of whole-body vibration can induce driver drowsiness and impair attention and driving performance. These findings provide evidence for establishing transportation safety standards.
APPLIED ERGONOMICS
(2024)
Article
Transportation Science & Technology
Junhua Wang, Wenxiang Xu, Ting Fu, Hongren Gong, Qiangqiang Shangguan, Anae Sobhani
Summary: This study focuses on modeling aggressive driving behavior using graph construction and finds that it outperforms traditional statistical methods. The time-to-collision variable is identified as the most significant factor.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Psychology, Applied
Seyed Iman Mohammadpour, Habibollah Nassiri
Summary: Aggressive driving, including behavioral, affective, and cognitive aspects, plays a significant role in driver performance and the occurrence of accidents. Overconfidence contributes to aggressive driving through aggressive thoughts, while also decreasing risk perception and driving performance. Understanding the role of cognition is crucial in preventing aggressive driving and its negative consequences.
TRANSPORTATION RESEARCH PART F-TRAFFIC PSYCHOLOGY AND BEHAVIOUR
(2021)
Article
Computer Science, Cybernetics
Xiaoning Zhao, Yuefeng Du, Lichao Yang, Enrong Mao, Dafang Guo, Zhongxiang Zhu
Summary: In recent years, driver behavior and driving performance have been identified as the main causes of increasing agricultural tractor accidents. However, the influence of environmental factors on driver behavior and driving safety for tractors remains unclear. This article investigated the effects of road type and in-vehicle information system (IVIS) task type on driver behavior and driving performance. Results showed that both road type and IVIS task type significantly affected drivers' behavior and driving performance. This study contributes to the design of road and tractor IVIS systems, as well as research on driver behavior and safety.
INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION
(2023)
Article
Environmental Sciences
Ward Ahmed Al-Hussein, Wenshuang Li, Lip Yee Por, Chin Soon Ku, Wajdi Hamza Dawod Alredany, Thanakamon Leesri, Huda Hussein MohamadJawad
Summary: This study examined the effect of COVID-19 on driving behavior using naturalistic driving data. Results showed that drivers committed more violations during the COVID-19 lockdown, with young drivers being most affected. The study also identified locations with the highest number of speeding offenses and provided recommendations on how to improve traffic safety in those areas and manage traffic during future lockdowns.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2022)
Review
Ergonomics
Zhizhuo Su, Roger Woodman, Joseph Smyth, Mark Elliott
Summary: This systematic review analyzed 31 reports with 34 primary studies on aggressive driving and found significant differences in vehicle speed, lateral control, and driving errors between aggressive driving and driving in the control group. The meta-analysis showed that aggressive driving had a significantly faster speed of 5.32 km/h and 2.51 times more driving errors compared to the control group.
ACCIDENT ANALYSIS AND PREVENTION
(2023)
Article
Environmental Sciences
Sarah Najm Abdulwahid, Moamin A. Mahmoud, Nazrita Ibrahim, Bilal Bahaa Zaidan, Hussein Ali Ameen
Summary: Driving behavior is a crucial factor in road accidents, with aggressive driving being the leading cause. Evaluating driving data has become a significant focus of research, aiming to detect dangerous and aggressive driving profiles. This study utilizes data collected from motorcyclists and smartphone apps to identify these driving patterns, providing valuable insights for improving road safety.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2022)
Article
Ergonomics
Yujun Jiao, Xuesong Wang, David Hurwitz, Gengdan Hu, Xiaoyan Xu, Xudong Zhao
Summary: The Manchester Driver Behavior Questionnaire (DBQ) is a widely used measure of aberrant driving behaviors. This study adjusted the existing DBQ items based on observations from a naturalistic driving study in Shanghai, China, and provides a method for future modifications of the questionnaire.
ACCIDENT ANALYSIS AND PREVENTION
(2023)
Article
Ergonomics
Pnina Gershon, Sean Seaman, Bruce Mehler, Bryan Reimer, Joseph Coughlin
Summary: This study assesses how drivers use automation in real-world driving, identifies driver and system-initiated Transfers of Control, and provides a taxonomy to capture driver behaviors associated with automation disengagement.
ACCIDENT ANALYSIS AND PREVENTION
(2021)
Article
Ergonomics
Mariane Bobermin, Sara Ferreira, Carlos Jose Campos, Joao Miguel Leitao, Daniel Sergio Presta Garcia
Summary: This study investigates the effects of road-driver interaction on driving performance and the effectiveness of three countermeasures. The results show that the drivers' behavioral history plays an important role in the differences observed in driving performance. It was also found that drivers react differently to the countermeasures, indicating a need for modifications in the study design.
ACCIDENT ANALYSIS AND PREVENTION
(2023)
Article
Energy & Fuels
Emilia M. Szumska, Rafal Jurecki
Article
Energy & Fuels
Emilia M. Szumska, Rafal S. Jurecki
Summary: This study investigates the impact of various parameters on a battery's depth of discharge in electric vehicles through simulation. The results suggest that driving route has the most significant effect on energy consumption. Operating conditions play a crucial role in determining the energy life and ultimately the range of an electric vehicle.
Editorial Material
Energy & Fuels
Marek Guzek, Rafal S. Jurecki, Wojciech Wach
Article
Energy & Fuels
Rafal Jurecki, Tomasz Stanczyk, Mateusz Ziubinski
Summary: This study aims to develop a method for parametric assessment of driver behavior and analyze the influence of road type on driver maneuvers. The results confirm that road type has a significant impact on the structure of driver maneuvers.
Article
Energy & Fuels
Emilia M. M. Szumska, Rafal Jurecki
Summary: This study analyzed the energy recovered during braking in different driving conditions and found that energy recovery levels were highest in urban driving conditions, accounting for 20% of the total trip energy. Similar levels of recovered energy were recorded in driving conditions of different intensities caused by trips at different times of the day, and the type of road on which the electric vehicle drives also has a significant impact on energy recovery levels.
Article
Chemistry, Multidisciplinary
Rafal S. Jurecki, Tomasz L. Stanczyk
Summary: This article provides a brief description of mathematical driver models, highlighting the lack of fully satisfactory models for analyzing drivers' behavior in emergencies. It presents a concept of model for driver's defensive maneuvers, specifically avoiding obstacles and braking. The model uses the method of artificial potential fields enriched with the concept of safety zones around the vehicle and obstacles of different shapes. The proposed model has potential applications in accident reconstruction programs and future driver assistance systems.
APPLIED SCIENCES-BASEL
(2023)
Article
Energy & Fuels
Jerzy Jackowski, Pawel Posuniak, Karol Zielonka, Rafal Jurecki
Summary: An analysis of energy-absorbing structures for rear underrun protective devices (RUPD) of motor trucks has been conducted. These structures can effectively reduce the impact loads on car occupants and enhance passive safety. Despite some limitations related to type-approval regulations in the European market, these energy-absorbing structures are relatively inexpensive and easily implementable in motor trucks.
Article
Chemistry, Analytical
Damian Frej, Pawel Grabski, Rafal S. Jurecki, Emilia M. Szumska
Summary: This paper investigates the longitudinal acceleration of city buses and coaches, and finds that it is significantly affected by road conditions and surface type. The study presents the longitudinal acceleration values obtained from continuous and long-term registration of vehicle traffic parameters. The results show that the maximum deceleration recorded during real traffic conditions is much lower than the values obtained from sudden braking maneuvers. Additionally, the maximum positive acceleration values recorded during acceleration maneuvers were slightly higher than the values obtained from rapid acceleration tests on the track.
Article
Engineering, Multidisciplinary
Emilia Szumska, Rafal Jurecki, Marek Pawelczyk
EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY
(2020)
Article
Engineering, Marine
Rafal S. Jurecki
SCIENTIFIC JOURNALS OF THE MARITIME UNIVERSITY OF SZCZECIN-ZESZYTY NAUKOWE AKADEMII MORSKIEJ W SZCZECINIE
(2019)
Proceedings Paper
Engineering, Electrical & Electronic
K. Ludwinek, R. Jurecki, M. Jaskiewicz, E. Szumska, M. Sulowicz
2018 XI INTERNATIONAL SCIENCE-TECHNICAL CONFERENCE AUTOMOTIVE SAFETY
(2018)
Proceedings Paper
Engineering, Electrical & Electronic
Rafal Chatys, Alexander Panich, Rafal S. Jurecki, Martins Kleinhofs
2018 XI INTERNATIONAL SCIENCE-TECHNICAL CONFERENCE AUTOMOTIVE SAFETY
(2018)
Article
Business
Milos Poliak, Adela Poliakova, Michaela Mrnikova, Patricia Simurkova, Marek Jaskiewicz, Rafal Jurecki
JOURNAL OF COMPETITIVENESS
(2017)
Article
Engineering, Industrial
Rafal Jurecki, Edward Pokropinski, Dariusz Wieckowski, Lukasz Zoladek
MANAGEMENT SYSTEMS IN PRODUCTION ENGINEERING
(2017)