Validation of Malaysian Driving Style Self-Assessment with Observational Road Study

IF 1 Q4 ENGINEERING, MECHANICAL
None M.Z.A. Kamaludin, Juffrizal Karjanto, None N. Muhammad, None N. Md. Yusof, None M.Z. Hassan, None A.F.H. Zulkifli, None Z.M. Jawi, None A.A. Ab Rashid
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Abstract

Along with the increasing application of a self-assessment questionnaire to recognize the driving style, questions have been raised about the possibility of reporting bias because of the driver’s misjudgment. A hazy reference point was the cause since drivers lack objective input directly tied to their driving behaviour when answering a self-assessment questionnaire. This study aims to validate the results of a driving style self-assessment questionnaire with road studies for Malaysian drivers. A total of 20 drivers’ driving styles were recorded and evaluated by the two designated observers. The driver completed the driving self-evaluation questionnaire (18 items) after finishing the two designated routes (familiarization and experiment route). In comparison, the observer evaluated the driver by using two forms: 1) a driver evaluation questionnaire (18 items) after the experiment and 2) an on-road driver observation form (25 items) during the experiment. Inference statistics analyzed the data using regression, Pearson correlation, Wilcoxon z-value, Analysis of Variance (ANOVA), and reliability test. For the reliability test, the questionnaire illustrates the consistency and reliability of the set questionnaire used in this study, ranging from acceptable to good. However, the result shows no significant correlations between driver and observer with on-road driver observation forms for error/violation during driving activities. Besides, there were significant differences between the driver and observer regarding driving style evaluation. The over-positive appraisal was higher among drivers with higher error/violation scores and with the ones that the observer evaluated. The theoretical and practical significance of the self-driving questionnaire is addressed.
马来西亚驾驶风格自评的观察性道路研究验证
随着人们越来越多地使用自我评估问卷来识别驾驶风格,人们开始质疑由于驾驶员的误判而导致报告偏差的可能性。一个模糊的参考点是原因,因为司机在回答自我评估问卷时缺乏与他们的驾驶行为直接相关的客观输入。本研究旨在验证驾驶风格自我评估问卷的结果与道路研究的马来西亚司机。共有20名驾驶员的驾驶风格被两名指定的观察员记录和评估。驾驶员在完成两条指定路线(熟悉路线和实验路线)后完成驾驶自我评价问卷(共18项)。观察员对驾驶员的评价采用两种形式:1)实验结束后的驾驶员评价问卷(18项)和2)实验期间的道路驾驶员观察表(25项)。推断统计采用回归、Pearson相关、Wilcoxon z值、方差分析(ANOVA)和信度检验对数据进行分析。对于信度检验,问卷说明了本研究所使用的调查问卷的一致性和信度,从可接受到良好。然而,结果显示驾驶员和观察者之间不存在显著的相关性,道路驾驶员在驾驶活动中的错误/违规观察表。此外,驾驶人和观察者在驾驶风格评价上存在显著差异。在错误/违规得分较高的司机和观察者评价的司机中,过度积极的评价更高。论述了自驾问卷的理论意义和现实意义。
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来源期刊
CiteScore
2.40
自引率
10.00%
发文量
43
审稿时长
20 weeks
期刊介绍: The IJAME provides the forum for high-quality research communications and addresses all aspects of original experimental information based on theory and their applications. This journal welcomes all contributions from those who wish to report on new developments in automotive and mechanical engineering fields within the following scopes. -Engine/Emission Technology Automobile Body and Safety- Vehicle Dynamics- Automotive Electronics- Alternative Energy- Energy Conversion- Fuels and Lubricants - Combustion and Reacting Flows- New and Renewable Energy Technologies- Automotive Electrical Systems- Automotive Materials- Automotive Transmission- Automotive Pollution and Control- Vehicle Maintenance- Intelligent Vehicle/Transportation Systems- Fuel Cell, Hybrid, Electrical Vehicle and Other Fields of Automotive Engineering- Engineering Management /TQM- Heat and Mass Transfer- Fluid and Thermal Engineering- CAE/FEA/CAD/CFD- Engineering Mechanics- Modeling and Simulation- Metallurgy/ Materials Engineering- Applied Mechanics- Thermodynamics- Agricultural Machinery and Equipment- Mechatronics- Automatic Control- Multidisciplinary design and optimization - Fluid Mechanics and Dynamics- Thermal-Fluids Machinery- Experimental and Computational Mechanics - Measurement and Instrumentation- HVAC- Manufacturing Systems- Materials Processing- Noise and Vibration- Composite and Polymer Materials- Biomechanical Engineering- Fatigue and Fracture Mechanics- Machine Components design- Gas Turbine- Power Plant Engineering- Artificial Intelligent/Neural Network- Robotic Systems- Solar Energy- Powder Metallurgy and Metal Ceramics- Discrete Systems- Non-linear Analysis- Structural Analysis- Tribology- Engineering Materials- Mechanical Systems and Technology- Pneumatic and Hydraulic Systems - Failure Analysis- Any other related topics.
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