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Fu R, Zhao X, Li Z, Zhao C, Wang C. Evaluation of the visual-manual resources required to perform calling and navigation tasks in conventional mode with a portable phone and in full- touch mode with an embedded system. ERGONOMICS 2023; 66:1633-1651. [PMID: 36533714 DOI: 10.1080/00140139.2022.2160496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 12/14/2022] [Indexed: 06/17/2023]
Abstract
This study investigates the differences in a driver's visual-manual behaviour when performing secondary tasks while driving under the full-touch mode (FTM) and the conventional mode (CM). To this end, 30 participants were recruited to perform secondary tasks while driving two vehicles equipped with different HMI system interaction modes. The results show that compared to the CM, in the FTM, fewer visual-manual resources are required to perform the calling task, but for the navigation task, this requirement is higher. Additionally, in both modes, the driver exhibited self-regulation visual-manual behaviour when performing secondary tasks as the driving speed increased. However, the effect of the driving speed on visual-manual behaviour was greater in the FTM than in the CM. The main limitation of this study is that the effect of the difference between the two experimental vehicles on the findings was not considered, however, this does not affect the generalisation of the findings. Practitioner summary: Potential applications of this study include improving drivers' knowledge about the effect of performing secondary tasks in different modes on driving safety, and this study also provides useful insights human-machine co-driving systems to develop user-friendly control strategies and for automotive companies to improve the full-touch interactive mode for automotive companies.
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Affiliation(s)
- Rui Fu
- School of Automobile, Chang'an University, Xi'an, China
| | - Xia Zhao
- School of Automobile, Chang'an University, Xi'an, China
| | - Zhao Li
- School of Automobile, Chang'an University, Xi'an, China
| | - Chen Zhao
- School of Automobile, Chang'an University, Xi'an, China
| | - Chang Wang
- School of Automobile, Chang'an University, Xi'an, China
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Alam MR, Batabyal D, Yang K, Brijs T, Antoniou C. Application of naturalistic driving data: A systematic review and bibliometric analysis. ACCIDENT; ANALYSIS AND PREVENTION 2023; 190:107155. [PMID: 37379650 DOI: 10.1016/j.aap.2023.107155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 03/19/2023] [Accepted: 06/04/2023] [Indexed: 06/30/2023]
Abstract
The application of naturalistic driving data (NDD) has the potential to answer critical research questions in the area of driving behavior assessment, as well as the impact of exogenous and endogenous factors on driver safety. However, the presence of a large number of research domains and analysis foci makes a systematic review of NDD applications challenging in terms of information density and complexity. While previous research has focused on the execution of naturalistic driving studies and on specific analysis techniques, a multifaceted aggregation of NDD applications in Intelligent Transportation System (ITS) research is still unavailable. In spite of the current body of work being regularly updated with new findings, evolutionary nuances in this field remain relatively unknown. To address these deficits, the evolutionary trend of NDD applications was assessed using research performance analysis and science mapping. Subsequently, a systematic review was conducted using the keywords "naturalistic driving data" and "naturalistic driving study data". As a result, a set of 393 papers, Published between January 2002-March 2022, was thematically clustered based on the most common application areas utilizing NDD. the results highlighted the relationship between the most crucial research domains in ITS, where NDD had been incorporated, and application areas, modeling objectives, and analysis techniques involving naturalistic databases.
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Affiliation(s)
- Md Rakibul Alam
- Chair of Transportation Systems Engineering, Technical University of Munich, Munich, Germany.
| | - Debapreet Batabyal
- Chair of Transportation Systems Engineering, Technical University of Munich, Munich, Germany
| | - Kui Yang
- Chair of Transportation Systems Engineering, Technical University of Munich, Munich, Germany
| | - Tom Brijs
- Transportation Research Institute, Hasselt University, Belgium
| | - Constantinos Antoniou
- Chair of Transportation Systems Engineering, Technical University of Munich, Munich, Germany
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Baikejuli M, Shi J, Qian Q. Mobile phone use among truck drivers: The application and extension of the theory of planned behavior. ACCIDENT; ANALYSIS AND PREVENTION 2023; 179:106894. [PMID: 36370511 DOI: 10.1016/j.aap.2022.106894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 10/10/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
Commercial truck drivers are particularly exposed to the risks associated with distracted driving, especially with mobile phone use while driving (MPWD), due to their higher driving exposure (DE) (e.g., high driving frequency, long driving hours and distance). However, despite being identified as one of the major causes in truck crashes, truck drivers' MPWD behavior has received little attention. In the current work, the theory of planned behavior (TPB), extended with DE, was applied to explore the determinants of MPWD among commercial truck drivers in China and examine the correlations between drivers' DE and psychological factors. We conducted an Internet survey and collected 420 valid questionnaires, which measured truck drivers' 5 standard TPB variables, DE and demographics. Structural equation modelling was used to analyze the data from the survey. The results showed strong support for the application of the proposed TPB model in explaining truck drivers' MPWD behavior. Specifically, truck drivers' behavioral intention (BI) had the greatest direct positive effect on MPWD behavior, while perceived behavioral control (PBC) had no direct positive effect. Moreover, PBC, attitude (ATT) and DE were significantly and positively associated with BI, while subjective norm was insignificant. As expected, DE has significant positive effects on truck drivers' psychological factors underlying MPWD behavior, especially on ATT and PBC, indicating that truck drivers with higher DE tend to have more positive attitudes toward MPWD and feel more confident about performing this risky behavior. These results may have notable practical implications in providing theoretical support for management and intervention of commercial truck drivers.
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Affiliation(s)
| | - Jing Shi
- Department of Civil Engineering, Tsinghua University, Beijing 100084, China.
| | - Qian Qian
- Department of Civil Engineering, Tsinghua University, Beijing 100084, China
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Xu W, Feng L, Ma J. Understanding the domain of driving distraction with knowledge graphs. PLoS One 2022; 17:e0278822. [PMID: 36490240 PMCID: PMC9733871 DOI: 10.1371/journal.pone.0278822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 11/25/2022] [Indexed: 12/13/2022] Open
Abstract
This paper aims to provide insight into the driving distraction domain systematically on the basis of scientific knowledge graphs. For this purpose, 3,790 documents were taken into consideration after retrieving from Web of Science Core Collection and screening, and two types of knowledge graphs were constructed to demonstrate bibliometric information and domain-specific research content respectively. In terms of bibliometric analysis, the evolution of publication and citation numbers reveals the accelerated development of this domain, and trends of multidisciplinary and global participation could be identified according to knowledge graphs from Vosviewer. In terms of research content analysis, a new framework consisting of five dimensions was clarified, including "objective factors", "human factors", "research methods", "data" and "data science". The main entities of this domain were identified and relations between entities were extracted using Natural Language Processing methods with Python 3.9. In addition to the knowledge graph composed of all the keywords and relationships, entities and relations under each dimension were visualized, and relations between relevant dimensions were demonstrated in the form of heat maps. Furthermore, the trend and significance of driving distraction research were discussed, and special attention was given to future directions of this domain.
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Affiliation(s)
- Wenxia Xu
- School of Automotive Studies, Tongji University, Shanghai, China
| | - Lei Feng
- School of Automotive Studies, Tongji University, Shanghai, China
| | - Jun Ma
- School of Automotive Studies, Tongji University, Shanghai, China
- College of Design and Innovation, Tongji University, Shanghai, China
- * E-mail:
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Wang X, Liu Q, Guo F, Fang S, Xu X, Chen X. Causation analysis of crashes and near crashes using naturalistic driving data. ACCIDENT; ANALYSIS AND PREVENTION 2022; 177:106821. [PMID: 36055150 DOI: 10.1016/j.aap.2022.106821] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 07/11/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
Understanding crash causation to the extent needed for applying countermeasures has always been a focus as well as a difficulty in the field of traffic safety. Previous research has been limited by insufficient crash data and analysis methods more suitable to single crashes. The use of crashes and near crashes (CNCs) and naturalistic driving studies can help solve the data problem, and use of pre-crash scenarios can identify the high-prevalence causes across multiple crashes of a given scenario. This study therefore proposes a two-stage crash causation analysis method based on pre-crash scenarios and a crash causation derivation framework that systematically categorizes and analyzes contributing factors. From the Shanghai Naturalistic Driving Study (SH-NDS), 536 CNCs were extracted, and were grouped into 23 different pre-crash scenarios based on the National Highway Traffic Safety Administration (NHTSA) pre-crash scenario typology. In-depth investigations were conducted, and CNCs sharing the same scenario were analyzed using the proposed framework, which identifies causation patterns based on the interaction of the framework's road user, vehicle, roadway infrastructure, and roadway environment subsystems. Through statistical analysis, the causation patterns and their contributing factors were compared for three common pre-crash scenarios of highest incidence: rear-end, lane change, and vehicle-pedalcyclist. Braking error in low-speed car following, following too closely, and non-driving-related distraction were important causes of rear-end scenarios. In lane change scenarios, the main causation patterns included illegal use of turn signals and dangerous lane changes as critical factors. Pedalcyclist scenarios were particularly impacted by visual obstructions, inadequate lanes for non-motorized vehicles, and pedalcyclists violating traffic regulations. Based on the identified causation patterns and their contributing factors, countermeasures for the three common scenarios are suggested, which provide support for safety improvement projects and the development of advanced driver assistance systems.
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Affiliation(s)
- Xuesong Wang
- School of Transportation Engineering, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China; National Engineering Laboratory for Integrated Optimization of Road Traffic and Safety Analysis Technologies, 88 Qianrong Rd, Wuxi 214151, China.
| | - Qian Liu
- School of Transportation Engineering, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China
| | - Feng Guo
- Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, United States
| | - Shou'en Fang
- School of Transportation Engineering, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China
| | - Xiaoyan Xu
- School of Transportation Engineering, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China
| | - Xiaohong Chen
- School of Transportation Engineering, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China
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Wu P, Song L, Meng X. Temporal analysis of cellphone-use-involved crash injury severities: Calling for preventing cellphone-use-involved distracted driving. ACCIDENT; ANALYSIS AND PREVENTION 2022; 169:106625. [PMID: 35272221 DOI: 10.1016/j.aap.2022.106625] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 02/28/2022] [Accepted: 03/01/2022] [Indexed: 06/14/2023]
Abstract
With the popularity of smartphones and the increasing dependence on cellphones, cellphone-use-involved distracted driving has become a global traffic safety concern. Calling, texting, or watching videos while driving could have harmful impacts on driving abilities and increase crash-injury severities. To investigate the temporal stability and the heterogeneity of cellphone-involved crash injury severity determinants, a series of likelihood ratio tests and random parameters logit models with heterogeneity in means and variances are estimated. Cellphone-involved single-vehicle crash datasets of Pennsylvania from 2004 to 2019 are utilized. Marginal effects are also applied to investigate the impact of explanatory variables on injury severity outcomes. The results indicate an overall temporal instability of cellphone-involved crashes across different periods. However, driving without seatbelts and overturns are observed to produce relatively stable and positive influence on the increased injury severities of cellphone-involved crashes. Besides, it is noteworthy that a combination of cellphone usage with risky driving behaviors (aggressive driving, alcohol- or drug-related driving, speeding, or fatigue driving) significantly increase driver injury-severities. This finding highlights the necessity of identifying drivers with multiple risk-taking behaviors and enacting laws to prohibit these drivers from using cellphones while driving. Applications of smartphones provide another feasible approach to prevent using cellphones while driving. Insights and suggestions of this study would be valuable to mitigate the negative outcomes of cellphone-involved crashes and prevent the crashes caused by cellphone-involved distracted driving in the future.
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Affiliation(s)
- Peijie Wu
- School of Transportation Science and Engineering, Harbin Institute of Technology, 73 Huanghe Street, Nangang District, Harbin 150090, China.
| | - Li Song
- Department of Civil and Environmental Engineering, University of North Carolina at Charlotte, EPIC Building, Room 3366, 9201 University City Boulevard, Charlotte, NC 28223-0001, USA.
| | - Xianghai Meng
- School of Transportation Science and Engineering, Harbin Institute of Technology, 73 Huanghe Street, Nangang District, Harbin 150090, China.
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Liang OS, Yang CC. How are different sources of distraction associated with at-fault crashes among drivers of different age gender groups? ACCIDENT; ANALYSIS AND PREVENTION 2022; 165:106505. [PMID: 34844081 DOI: 10.1016/j.aap.2021.106505] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 11/18/2021] [Accepted: 11/19/2021] [Indexed: 05/16/2023]
Abstract
INTRODUCTION Distracted driving has been well researched, however the comparison between different age-gender groups on the impact of distracted driving has not been explored. Most crash analysis research does not distinguish driver responsibility, so the role that distractions has in at-fault crashes is unknown. Without distinguishing at-fault crashes from all-cause crashes, distracted driving's detrimental effects could be underestimated. OBJECTIVE This study aims to systematically assess the risk of at-fault crashes associated with different sources of distraction among six groups by driver age (Teens 16-19, Adults 20-64, Seniors 65+) and gender. METHODS Crashes where a study participant was deemed at fault were identified using human expert annotated variables from the Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study dataset. Generalized linear mixed models were performed to assess the adjusted odds ratios of 10 distraction types associated with the at-fault crashes while controlling for environmental factors. RESULTS The main findings are (1) The highest contributing distraction types in at-fault crashes were In-Cabin Objects, Mobile Device, External Scenes, and In-Vehicle Information Systems (IVIS) as indicated by their influence on multiple age-gender groups and the magnitude of odds ratios; (2) Teens and adults were more distraction-prone than seniors, although seniors had the greatest at-fault crash risks associated with In-Cabin Objects, Mobile Device, and IVIS; (3) Distractions impacted females and males similarly; (4) At-fault crashes were more likely to have the significant distraction types present than all-cause crashes. CONCLUSION This study adds to the limited literature on at-fault crashes particularly as it explores the role of driver demographics and distracted driving. Analyzing the risks of distracted driving by age-gender group shows that specific activities can be riskier for a certain population. The effects of distractions may be overlooked without fault determination. Distractions by external scenes, in-vehicle technologies, and in-cabin objects should not be overlooked, in addition to mobile device use.
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Affiliation(s)
- Ou Stella Liang
- College of Computing and Informatics, Drexel University, Philadelphia, PA 19104, USA
| | - Christopher C Yang
- College of Computing and Informatics, Drexel University, Philadelphia, PA 19104, USA.
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