摘要In recent years, defensive driving technology has become an important research direction for road transport safety risk prevention and control. However, the research on defensive driving at home and abroad focuses on the feasibility, judgment, and recognition of defensive driving, and a quantitative research on the relationship between defensive driving training and driver emotional intelligence remains lacking. A multiple linear regression model to represent the relationship between defensive driving training and driver emotional intelligence improvement is proposed. This model is divided into two stages to describe the linear relationship between the five independent variables of driver psychology, physiology, emotion, driving intelligence, and learning time and the dependent variables of defensive driving training effectiveness. An F-test is carried out to verify the linear characteristics of the model. Results show that the average error probability of the linear impact of the five independent variables on the dependent variables is 0.047 in the two stages, which meets the requirements of multiple linear regression. The results prove that the proposed model is effective. Analysis of the model indicates that defensive driving training has two distinct characteristics on the improvement of driver emotional intelligence. In the initial stage, defensive driving training has the highest correlation with indicators related to driver agility. In the second stage, defensive driving training is highly correlated with indicators related to the psychological and emotional control of drivers.
Abstract：In recent years, defensive driving technology has become an important research direction for road transport safety risk prevention and control. However, the research on defensive driving at home and abroad focuses on the feasibility, judgment, and recognition of defensive driving, and a quantitative research on the relationship between defensive driving training and driver emotional intelligence remains lacking. A multiple linear regression model to represent the relationship between defensive driving training and driver emotional intelligence improvement is proposed. This model is divided into two stages to describe the linear relationship between the five independent variables of driver psychology, physiology, emotion, driving intelligence, and learning time and the dependent variables of defensive driving training effectiveness. An F-test is carried out to verify the linear characteristics of the model. Results show that the average error probability of the linear impact of the five independent variables on the dependent variables is 0.047 in the two stages, which meets the requirements of multiple linear regression. The results prove that the proposed model is effective. Analysis of the model indicates that defensive driving training has two distinct characteristics on the improvement of driver emotional intelligence. In the initial stage, defensive driving training has the highest correlation with indicators related to driver agility. In the second stage, defensive driving training is highly correlated with indicators related to the psychological and emotional control of drivers.
祁晓峰. Multiple Linear Regression Model of Defensive Driving Technology and Driver Emotional Intelligence Improvement[J]. Journal of Highway and Transportation Research and Development, 2020, 14(2): 102-110.
QI Xiao-feng. Multiple Linear Regression Model of Defensive Driving Technology and Driver Emotional Intelligence Improvement. Journal of Highway and Transportation Research and Development, 2020, 14(2): 102-110.
[1] BEBINOV S, KAPRALOV S. Innovative Means for Enhancing of Vehicle Drivers' Reliability[J]. Transportation Research Procedia, 2017, 20:41-46. [2] NETO J P L, LYRA M L, DA SILVA C R. Phase Coexistence Induced by a Defensive Reaction in a Cellular Automaton Traffic Flow Model[J]. Physica A:Statistical Mechanics and Its Applications, 2011, 390(20):3558-3565. [3] HUANG J L, FORD J K. Driving Locus of Control and Driving Behaviors:Inducing Change through Driver Training[J]. Transportation Research Part F:Traffic Psychology and Behaviour, 2012, 15(3):358-368. [4] RIZEANU S, GATEJ E R, CIOLACU M V. Personal Development through Defensive Driving Techniques:Implications in the Field of Emotional Intelligence Regarding the Age Factor[J]. American Research Journal of Geriatrics and Aging, 2017,1(7):1-7. [5] KNIPLING R R, HICKMAN J S, BERGOFFEN G. Effective Commercial Truck and Bus Safety Management Techniques[M]. Washington, D.C.:Transportation Research Board, 2003. [6] CHARISMA Y M T, EKAWATI E, WIDJASENA B. Faktor-faktor Yang Berhubungan Dengan Defensive Driving Pada Pengemudi Bus Rapid Transit (BRT) Trans Semarang Koridor II, III, Dan VI[J]. Journal Kesehatan Masyarakat (e-Journal), 2019, 7(1):365-373. [7] MIHAI C, IONIǍ F. Transportation Cost Reduction and Competitive Advantage through Defensive Driving Programs[C]//Proceedings of the International Management Conference. Bucharest:Academy of Economic Studies, 2013:88-94. [8] HABIBOVIC A, TIVESTEN E, UCHIDA N, et al. Driver Behavior in Car-to-pedestrian Incidents:An Application of the Driving Reliability and Error Analysis Method (DREAM)[J]. Accident Analysis & Prevention, 2013, 50:554-565. [9] HOU Pei-guo,YAN Cheng-hao, YU Han-wei, et al. One-dimensional Cellular Automaton Traffic Flow Model Based on Defensive Driving[J]. Journal of Beijing Jiaotong University,2019,43(6):62-66. (in Chinese) [10] HE Peng-lin, ZHANG Yu, CHEN Zhi-yu, et al. Driver Defensive Driving Skill Judgment System and Vehicle; China, CN207416817U[P]. 2018-05-29. (in Chinese) [11] BROMBACHER P, MASINO J, FREY M, et al. Driving Event Detection and Driving Style Classification Using Artificial Neural Networks[C]//2017 IEEE International Conference on Industrial Technology (ICIT). Toronto:IEEE, 2017:997-1002. [12] GOLEMAN D. Emotional Intelligence:Why It Can Matter More Than IQ[M]. London:Bloomsbury,1995. [13] LAI P Y, DOW C R, CHANG Y Y. Rapid-response Framework for Defensive Driving Based on Internet of Vehicles Using Message-oriented Middleware[J]. IEEE Access, 2018, 6:18548-18560. [14] SPARROW S D, LEWIS C E, KNIGHT C W. Soil Quality Response to Tillage and Crop Residue Removal under Subarctic Conditions[J]. Soil and Tillage Research, 2006, 91(1/2):15-21. [15] STEIN S J, BOOK H E. The EQ Edge:Emotional Intelligence and Your Success[M]. Washington, D.C.:John Wiley & Sons, 2011. [16] REN Xiu-wei. Quantitative Model of Blindness Women's Driving Emotional Intelligence Based on Dimensional Theory[D]. Wuhan:Wuhan University of Technology, 2014. (in Chinese) [17] YU Tai-chu. Research on Test Method of Vehicle Drivers' Hazard Perception Ability[D]. Beijing:People's Public Security University of China,2019. (in Chinese) [18] STEIMETZ S S C. Defensive Driving and the External Costs of Accidents and Travel Delays[J]. Transportation Research Part B:Methodological, 2008, 42(9):703-724. [19] SCHUTTE N S, MALOUFF J M, HALL L E, et al. Development and Validation of a Measure of Emotional Intelligence[J]. Personality and Individual Differences,1998, 25:167-177. [20] LIU Wei-ming, GUAN Li-ping, YI Xiang-yuan. Prediction of Incident Duration Based on Multiple Regression Analysis[J]. Journal of Highway and Transportation Research and Development,2005(11):130-133. (in Chinese)
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2020, Vol. 14 
