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Hindawi Journal of Advanced Transportation Volume 2023, Article ID 7664577, 13 pages https://doi.org/10.1155/2023/7664577 Research Article Incorporating the Theory of Planned Behaviour into Distracted Driving: Influencing Factors and Intervention Effects L. Zhang, J. S. Peng , Z. Song, Z. B. Fan, X. H. Yang, Q. W. Kong, and L. Zhou School of Trafc and Transportation, Chongqing Jiaotong University, Chongqing 400074, China Correspondence should be addressed to J. S. Peng; firstname.lastname@example.org Received 13 October 2022; Revised 13 March 2023; Accepted 17 April 2023; Published 2 May 2023 Academic Editor: Nirajan Shiwakoti Copyright © 2023 L. Zhang et al. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Tis study focuses on the psychological characteristics and empirically tests of the factors infuencing distracted driving be- haviours. Tis information is used as a reference for an intervention on dangerous driving behaviours. First, a distracted driving scale is constructed based on the theory of planned behaviour (TPB). Te questionnaires are distributed in Chongqing, China, and 321 completed questionnaires are obtained. Data are analyzed using mean-variance analysis, one-way ANOVA, T-test, and multivariate test by SPSS 26.0 to determine the signifcance of distracted behaviours and demographic variables. We use a structural equation model to determine the path coefcients of each latent variable. Finally, we select the drivers with high tendency of distraction from the results of the questionnaires, conduct a four-stage rational emotional behaviour therapy (REBT) experiment, and use a repeated measures ANOVA analysis to test the validity and persistence of the intervention method. Results show that subjective norm is the most infuential psychological factor. Tere are signifcant diferences between the experimental group (2.38, SD � 0.41) and the control group (2.89, SD � 0.40) in the scores of distractions. Tis indicates that the distracted behaviour intervention achieves adequate validity and consistency. Educational research on distracted driving behaviour can help identify and correct drivers with high distraction tendency. on distracted behaviour among drivers. By looking for the 1. Introduction signifcant internal causes, we can efectively intervene the Technological progress in the automobile industry and the occurrence of distracted driving behaviour. application of advanced safety equipment has greatly im- Distracted driving is a hot research topic in the feld of proved vehicle safety . Standardization of road design and road trafc safety in recent years. In this study, the core construction technology has further contributed to this collection of Web of Science is used as the database, and improvement . An indispensable link in the driver- a total of 3884 papers from 2002 to 2022 are obtained. Te vehicle-road coupling system, statistics have shown that tendency of published papers is shown in Figure 1. Previous drivers are the most active and unstable factor, and their bad studies explored the infuence of distracted driving behav- driving behaviours are the primary cause of frequent trafc iour on road safety from the aspects of driver gaze behaviour, accidents [3, 4]. In recent years, the increase in road trafc saccade behaviour, vehicle speed, and trajectory [8–11]. fow density and information on various road signs have Other studies had made comprehensive reviews of distracted increased drivers’ cognitive load. Additionally, the extensive driving behaviour such as cause, frequency, and detection use of smart phones and multimedia information systems methods [12–14]. have further consumed drivers’ limited attention resources Causes of distracted driving behaviour can be divided , which may lead to a decline in driving performance into visual distractions such as reading mobile phone levels or an increase in accident risk [6, 7]. Terefore, in messages and roadside objects [15, 16], cognitive distractions order to reduce the accident risk caused by distracted driving such as “daydreaming” and Bluetooth conversations [17, 18], behaviour, it is necessary to conduct a questionnaire study and physical distractions such as sending text messages, 2 Journal of Advanced Transportation After fnding the factors that infuence distracted driving behaviour, appropriate methods are needed to intervene the 426 above factors to reduce the tendency of distracted behaviour. Behavioural psychology has recommended several theo- retical methods for conducting intervention experiments which explore the mechanism of how diferent demographic 245 and psychological factors infuence distracted driving be- 212 214 haviour . Brewster et al.  used the conscious awareness of intentions which can weaken the efect of habit, as an intervention in drivers’ speeding behaviour. By 100 85 74 75 summarizing the main results of the above studies, it is 30 30 31 found that most of the current studies focus on the frequency and infuencing factors of distracted driving behaviour, but few studies carry out targeted educational intervention based 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 on the results of the infuencing factors. Terefore, in order Years to improve the defciencies of existing research in the feld of Figure 1: Annual number of articles published in web of science distracted driving behaviour, we conduct the intervention from 2002 to 2022. experiment based on cognitive behaviour therapy (CBT). CBT has proposed that behavioural intention is not directly changing the car settings, and so on [9, 19]. In contrast to determined by events but by people’s cognition of the events visual and physical distractions, cognitive distractions may themselves. Terefore, this method helps people gradually occur when a driver’s gaze is always on the road, which realize their false beliefs and cognitions involved in past bad makes it covert and hard to detect . Terefore, exploring behaviour through debate. Ten, false beliefs are replaced the characteristics and psychological causes of driving dis- with proper beliefs to minimize the bad behaviour caused by traction may help improve the current research in the feld false cognition. Among the common cognitive behavioural [20, 21]. therapies, REBT is the most widely used and accepted Schroeder et al.  analyzed the characteristics of behavioural intervention technique . Terefore, by American drivers’ distracted behaviours to understand the measuring the availability and reliability of various methods underlying psychological mechanism. Talking among pas- of training, REBT is fnally selected as the intervention sengers and drivers, adjusting the radio, eating, and making method. a phone call are researched and ranked through a ques- Te objectives of this study are to (i) develop a distracted tionnaire survey to describe the occurrence regularity of driving scale based on the TPB as a research framework, (ii) distracted driving behaviour. However, the risk perception use structural equation modeling to analyze the path co- behind such distracted behaviour is not measured. To efcients of latent variables, and (iii) conduct a distraction overcome this shortcoming, Prat et al.  used a semi- intervention experiment based on REBT to evaluate the structured scale and found that drivers could fully perceive intervention’s efectiveness and sustainability. only one dangerous behaviour and ignored other distracted behaviours. Rather than analyzing a single psychological 2. Methods factor infuencing distracted driving behaviour, Chen et al.  verifed the validity of a research framework of planned In order to carry out investigation and intervention on behaviour theory to analyze multiple factors that infuence distracted driving behaviour, this study was divided into two distracted driving behaviour, specifcally age, gender, atti- stages: building a distracted driving scale based on TPB, tude, and prescriptive norms. However, owing to the dif- analyzing behavioural and psychological characteristics, and ferences in culture and trafc rules in diferent countries, the fnding out the factors that afect distracted driving be- applicability of the above theoretical results needs to be haviour were presented in the frst stage. Drivers with high studied in an environment with Chinese trafc distraction tendency were divided into the experimental characteristics. group and the control group. Te experimental group was In order to further elucidate the mechanism of distracted conducted a distraction intervention experiment based on driving behaviour, a theoretical framework for this behav- REBT, and the intervention efect was evaluated in the iour needs to be constructed. Te TPB can well predict and second stage. explain the internal causes of behaviour through latent variables such as attitude, subjective norms, and perceived behavioural control . It has been widely used in felds 2.1. Scale Items’ Development. Tis study collected original such as agricultural production , transit trip , distracted behaviours by interviewing eight drivers with the consumption habit , and ftness exercise  et al. use of self-reports and determined the classifcation of Terefore, based on the research framework of planned distraction types by consulting fve experts. We designed the behaviour theory, this study constructs a driving distraction driving distraction scale based on the following fve steps. questionnaire and tries to fnd out the important factors that Step 1: based on the research framework of classic TPB , afect distraction behaviour. the driving distraction scale was divided into 5 subscales, as Annual number of articles Journal of Advanced Transportation 3 drivers could further understand the dangers of distracted shown in Table 1. Step 2: the distracted behaviours were extracted from the interviews and literature studies [23, 24], driving and generate self-correction ability (see Table 2 for details). and the contents were modifed and supplemented. Step 3: the distracted behaviours were refned by operationalization. Step 4: the driving distraction scale was given a title, re- 2.3. Participants. To ensure the sufciently representative sponse instructions, and acknowledgments. Step 5: ambig- samples, 361 questionnaires were selected in the frst study uous items were deleted or modifed through a small-scale stage. As a megacity in China, Chongqing contains diverse trial investigation. Each item was presented on a 5-point trafc scenes and drivers. We conducted our study in Nan’an Likert scale . After the preliminary deletion of several District of Chongqing and randomly selected drivers to items, a validity test was performed by SPSS 26.0. Te Kaiser- complete the questionnaire survey. After excluding invalid Meyer-Olkin (KMO) fgure was 0.809, and the results of questionnaires such as those with missing answers, a total of Mauchly’s test of sphericity were signifcant (p < 0.001), 321 valid questionnaires were collected, a response rate of indicating that the driving distraction scale has a good 88.9%. Te samples included 208 men and 113 women. validity . Additionally, the number of professional drivers (those who drive for business) in this survey was relatively small (29), with most being nonprofessional drivers (292). Drivers of all 2.2. Intervention Procedure. For the process of the in- ages were evenly distributed, ranging from 18 to 57 years old, tervention experiment, the experimental site was the Trafc with an average age of 34.8 years and a standard deviation of Safety Education School of the Motor Vehicle Drivers As- 10.1 years. Te number of driving experience ranged from sociation in Nan’an District, Chongqing, China. Te 40 1 year to 31 years, with an average of 6.84 years and a stan- drivers participating in the intervention were divided equally dard deviation of 6.5 years. into the experimental group and the control group, and the In the second stage, with the assistance of the trafc demographic variables of the two groups were similar. First, police department, 40 drivers were selected from the frst before the implementation of the education intervention stage of 321 ordinary drivers, as intervention experiment course, both groups of drivers completed the frst ques- participants. Compared with the ordinary drivers, the 40 tionnaire. Second, according to the intervention course’s drivers with a total of 12 points were deducted from their characteristics of psychological diagnosis stage, compre- driver’s license. Such drivers are usually assumed to have hension stage, working through stage, and reeducation stage, a higher tendency to be distracted and need more trafc the drivers in the experimental group received four edu- safety education. cational interventions within a week (they were set on Monday, Wednesday, Friday, and Sunday), with each course 3. Results of intervention time lasting an hour (10 : 00 AM-11 : 00 AM). Subsequently, the experimental group completed the second 3.1. Demographics and Descriptive Variables. A total of 321 questionnaire, while the control group flled the second valid questionnaires were collected in this study. Please refer questionnaire without going through the courses. Finally, in to Table 3 for more information about the participants. order to verify the sustainability of the intervention efect, all Regarding driving time per day, 82% of drivers drive for less participants were contacted again half a month after the than two hours a day. With the increase of age, the frequency educational intervention and flled in the questionnaire for of drivers driving every week has increased from sometimes the third time. Te intervention experimental process is driving to frequent driving. According to the statistics of shown in Figure 2. driver’s license point deduction, 89% of drivers were For the content of intervention experiment, according to deducted less than 6 points, and 76% less than 3 points. In the preset requirements of REBT experience, the in- addition, driving experience increase with age, with an tervention process was divided into four courses. In the frst average of 6.84 years for all drivers. lesson, the questionnaire data of each driver were analyzed, and the participants were pointed out through PowerPoint (PPT) pages about the common distractions in the driving 3.2. Tendency of Distracted Driving Behaviours. As seen in process, the impact of distractions on normal driving, and Table 4, the average value represents the tendency of each the accidents caused by distractions so that the drivers could distraction behaviour, from 1 (never occur) to 5 (always realize the high incidence and harmfulness of distractions. In occur). Cognitive distractions have the highest frequency the second lesson, we would analyze the risk perception and (2.79, SD � 1.274) of the three types of distractions during psychological motivation of drivers when they were dis- driving. In this distraction type, “chatting with other people tracted so that drivers could understand the mechanism of in the car” has the highest frequency (3.43, SD � 1.301). Te distraction. In the third lesson, gradually teaching correct highest frequency may be because drivers generally believe driving knowledge to the participants would enable them to that this behaviour has little infuence on driving in their recognize and challenge their incorrect cognition, changed daily driving scenarios. As a common type of distraction, their interpretation of distracted driving behaviours, and physical distractions are ranked second in frequency, with an transformed the bad behaviours into proper ones. In the average of 2.21 (SD � 1.131). At the same time, “adjusting the fourth lesson, the proportion of distracted driving behav- air conditioner temperature and window” is the second most iours and their corresponding risk levels were listed so that frequent distracted behaviour (3.31, SD � 1.206). Te least 4 Journal of Advanced Transportation Table 1: Framework and content of the distracted driving scale. Group Subscale name Content or explanation Demographic information D D Personal details Driving information D Attention characteristic information D B Past distracted behaviour Te frequency of various types of distracted behaviours over a period of time Individually perceived social or other pushes or hindrances to a distracted S Subjective norm behaviour Individual perceived ease or difculty of manipulation when a distracted behaviour P Perceived behavioural control occurs A Distracted behaviour attitude An individual’s perceived liking for the outcome of a distracted behaviour Journal of Advanced Transportation 5 Participants Frist fill in questionnaires Random assignment Experimental group A-B-C-D Intervention experiment Analysis of Explore the motivation questionnaire results of distracted behavior Control group Psychodiagnostic stage Comprehension stage Recognize the risks of Debate the dangers of distracted behaviors distracted driving Reeducation stage Working through stage Immediately Immediately Second fill in questionnaires After half a month Third fill in questionnaires Figure 2: Intervention experimental process. Table 2: Intervention courses’ content. Course number Course stage Course content Collect distracted driving scale data, analyze the corresponding driver scale score 1 Psychological diagnosis stage results and the infuence coefcient of psychological factors, and extract the drivers’ unreasonable beliefs Systematically display common distracted driving behaviours and explore the 2 Comprehension stage motivation that induces such behaviours and understand the emotional state and risk perception of people who engaged in distracted driving Teaching sessions to make drivers aware of the dangers of engaging in distracted 3 Working through stage driving, pointing out their unreasonable beliefs about such driving, and inculcating safe driving concepts to them Using data and pictures to build a deep understanding of all types of distracted driving behaviours and their corresponding potential risks, further enabling drivers 4 Reeducation stage to have self-dialectic and error-correcting abilities about distracted driving behaviours frequent distraction type is visual distraction (2.11, total distractions gradually increased from the 20–27 to SD � 1.131); “answering an acquaintance’s phone on the 28–35 age group. However, for physical distractions, the highway (hand-hold)” is the least frequent of all kinds of largest mean value appears in the youngest age group and distracted behaviours, with a score of only 1.51 (SD � 0.979). then decreases in the other age groups. In statistical methods, one-way analysis of variance (ANOVA) is to test whether the mean value of a dependent variable in multiple groups of samples afected by a single 3.3. Path Coefcient from Latent Variables to Distractions. factor has signifcant diference . Terefore, the ANOVA To further quantitatively demonstrate the infuence of the method is used to analyze the relationship between drivers’ relationships among the psychological factors in the age and drivers’ distracted behaviours. Similarly, the average questionnaire, a structural equation model was introduced value represents the tendency of each distraction behaviour. to quantify the various latent variables. In addition, per- Tat is, whether there are signifcant diferences in the sonal details were added to jointly explain the distracted tendency of drivers of diferent age groups to engage in behaviour and the assumptions H1–H4 were made in various distractions. Te results are presented in Table 5. Figure 3. Te structural equation model of data included With an age interval of seven years, the drivers are divided the fve latent variables of the driver’s personal details, into four groups, and the diferences in cognitive distrac- distractions attitude, subjective norms, perceived behav- tions, visual distractions, physical distractions, and total ioural control, and distracted behaviours. Each latent distracted behaviours of drivers in diferent age groups were variable corresponded to several items as observed vari- investigated. Cognitive distractions, visual distractions, and ables . To facilitate the corresponding analysis in Amos 6 Journal of Advanced Transportation Table 3: Demographics and driving information statistics for each age group. Penalty points Frequency per Driving time Driving experience Age group N degree mean week mean per day mean (SD) (SD) (SD) mean (SD) 18–25 43 2.48 (1.39) 1.40 (0.92) 1.87 (0.80) 1.61 (0.70) 26–35 57 5.67 (3.66) 1.43 (0.72) 2.76 (1.16) 1.67 (0.89) Male 36–45 59 9.71 (6.35) 1.68 (0.93) 2.97 (0.98) 2.09 (1.07) 46–57 49 15.39 (8.83) 1.35 (0.79) 2.96 (0.95) 2.22 (1.26) 18–25 24 1.87 (1.68) 1.20 (0.76) 1.24 (0.80) 1.10 (0.70) 26–35 33 3.45 (3.67) 1.25 (0.76) 1.94 (1.17) 1.44 (0.85) Female 36–45 33 8.88 (6.10) 1.56 (0.99) 2.69 (0.95) 1.81 (1.07) 46–57 23 6.08 (8.01) 1.31 (0.98) 2.77 (1.00) 1.46 (1.17) Overall 18–57 321 6.84 (6.54) 1.41 (0.85) 2.42 (1.11) 1.71 (1.00) a b Frequency per week: 1 (seldom), 2 (sometimes), 3 (often), and 4 (always). Te maximum number of penalty points for a driving license is 12 per year in c d China. Penalty points degree from 1 (0∼3 points) to 4 (9∼12 points). Driving time per day: 1 (0∼1 hours), 2 (1∼2 hours), 3 (2∼3 hours), and 4 (3+ hours). Driving experience is the number of years that a driver has obtained driving qualifcation. Table 4: Driving distraction degree of drivers. Number Items Mean (SD) Cognitive distractions 2.79 (1.274) B Tinking about the minutiae of work or life 2.61 (1.152) B Answering calls from family, friends, or colleagues on city roads 2.87 (1.312) B Answering calls from family, friends, or colleagues on the highway 2.26 (1.331) B Talking to other people in the car 3.43 (1.301) Visual distractions 2.11 (1.161) B Being drawn to a billboard or object on the side of the road 2.36 (1.095) B Being drawn to pedestrians or events 1.90 (0.933) B Checking mobile phone messages 1.86 (1.026) B Answering mobile phone messages 1.54 (0.909) B Switching songs, radio, and other vehicle entertainment systems 2.89 (1.310) Physical distractions 2.21 (1.131) B Answering calls from acquaintances on city roads (hand-held) 1.78 (1.251) B Answering calls from acquaintances on the highway (hand-held) 1.51 (0.979) B Answering mobile phone messages 1.54 (0.909) B Adjusting air conditioning temperature and windows 3.31 (1.206) B Switching songs, radio, and other vehicle entertainment systems 2.89 (1.310) Overall 2.41 (1.335) Table 5: One-way ANOVA table of distraction driving behaviour in diferent age groups. 20∼27 28∼35 36∼43 44∼55 N � 113, M N � 62, M N � 64, M N � 82, M F p (SD) (SD) (SD) (SD) Cognition 2.78 (0.98) 3.12 (0.97) 2.98 (1.18) 2.42 (0.93) 3.832 0.011 ∗∗ Vision 2.41 (0.77) 2.63 (0.86) 2.23 (0.87) 2.00 (0.59) 5.158 0.002 ∗∗ Physical 2.49 (0.79) 2.04 (0.67) 2.06 (0.64) 2.06 (0.65) 5.531 0.001 ∗∗ Overall 2.56 (0.79) 2.83 (0.83) 2.53 (0.95) 2.17 (0.66) 4.803 0.003 ∗ ∗∗ p< 0.05 and p< 0.01. 26.0 (structural equation modeling software), it was nec- index after the two modifcations is shown in Table 6. essary to encode each latent variable and observation Finally, a path graph that meets the requirements of the ft variable with symbols . Te coded item data were index was formed. entered into the structural equation model (Figure 4) for After the structure equation model and path modifca- path ftting, and the path was modifed twice according to tions were determined, model path parameter estimation the ft index. It was added a path from A12 to P12 and set was conducted and the results with standardized estimation the paths from P to P7 and P to P8 as the same value. Te ft are shown in Table 7. Journal of Advanced Transportation 7 Personal Details H1 Subjective Norm H2 Distracted Behaviours Perceived H3 Behavioural Control H4 BehaviourAttitude Figure 3: Assumption diagram of the latent variable relationship. e46 e47 e54 e45 e53 e56 B2 B4 B14 B1 B12 B15 A1 e44 e14 S1 .66 .58 .78 .71 .62 .78 e57 S2 A2 e43 .57 Past Distracted .68 Behaviour .76 .76 .65 .13 e55 S5 A3 e42 .54 .76 e55 .39 Distracted Subjective Norm Behaviour Attitude A11 e20 S8 .15 .11 e36 .53 .61 .08 .48 .14 .74 .58 e35 e21 S10 A12 e22 S11 A13 e34 Perceived Personal Details Behavioral Control .65 .58 .11 .07 -.09 .69 .79 P1 P2 P3 P10 P12 P13 D Attention Demographic Driving Information Characteristic Information Information e33 e32 e31 e58 e24 e23 D1 D2 D3 D4 D5 D6 D7 D8 D9 e1 e2 e3 e4 e5 e6 e7 e8 e9 Figure 4: Structure of the psychological factor model of distracted driving behaviours. 3.4. Intervention Efect experimental group, and the diference values after training were −0.50 (cognitive distraction), −0.45 (visual distraction), 3.4.1. Te Efectiveness of Intervention Training. Te second and −0.51 (physical distraction). In addition, the maximum questionnaire survey was conducted immediately after the and minimum scores of each behaviour also decreased in course training. Te score distribution of distracted be- varying degrees, and the distribution range of the experi- haviours of the drivers in the experimental group and the mental group was signifcantly smaller than that of the control group is plotted in Figure 5. It can be seen that the control group, indicating that the score distribution of the average score of distracted behaviours was lower in the experimental group was more concentrated. 8 Journal of Advanced Transportation Table 6: Fit index distribution after twice modifcation. Value after frst Value after second Indicators name Reference range modifcation modifcation χ /α 2.281 2.050 0∼2.0 Goodness of ft index (GFI) 0.852 0.906 0.9∼1.0 Absolute ft index Adjusted goodness of ft index (AGFI) 0.883 0.915 0.9∼1.0 Root mean square of residual (RMR) 0.102 0.063 0∼0.05 Normative ftting index (NFI) 0.869 0.913 0.9∼1.0 Relative ft index Tucker-Lewis (TLI) 0.834 0.889 0.9∼1.0 Comparative ft index (CFI) 0.851 0.907 0.9∼1.0 behaviours in the experimental group and the control group Table 7: Structural equation model standardized path were compared in the three-time questionnaire survey. parameter table. Mauchly’s test of sphericity was applied, as shown in Table 9. Path Since the p values of the spherical test for the three types Path direction coefcient of distracted behaviours are all less than 0.05, multivariate Subjective Norm⟶ driving distractions 0.650 analysis of variance is required. Te analysis results are Perceived behavioural control⟶ driving ∗ shown in Tables 10–12 and in Figure 6. In fgure, time point 1 0.152 distractions is the frst survey, time point 2 is the second survey, and time Behaviour attitude⟶ driving distractions 0.134 point 3 is the third survey. Te results of the multivariate Demographic Information⟶ driving distractions 0.11 analysis of variance showed that the scores of the three types ∗ ∗∗ p< 0.05 and p< 0.01. of distractions in the intervention group were signifcantly diferent across the mentioned time points. Te results of interaction efect analysis between the time point and the intervention mode showed that the efects of intervention 4.5 tests at diferent time points were maintained. 4. Discussion 3.5 4.1. Questionnaire Validity and Reliability. To further ex- plore the psychological factors, it is necessary to carry out a targeted questionnaire survey on distracted driving be- haviour [21, 24, 37, 38]. As the research basis of the ques- 2.5 tionnaire survey, this study constructed a driving distraction scale with good reliability and validity in line with a Chinese cultural background, trafc scenes, and trafc regulations. Compared with the questionnaire by Prat et al. , the 1.5 Coginitive distraction Visual distraction Physical distraction driving distraction questionnaire established in this study incorporates more scene information to cover all three types Control group of distracted behaviours. Drivers could understand the Experimental group questionnaire contents more conveniently through using Figure 5: Evaluation of the efectiveness. plentiful scene descriptions, which could further improve the reliability and validity of the distracted driving ques- tionnaire. Additionally, through a small-scale preliminary 3.4.2. Te Persistence of Intervention Training. Half a month survey, some vague items were deleted, which made the after the end of intervention courses, we contacted the semantic expression of the driving distraction scale clearer drivers who had participated in the educational intervention and laid a good foundation for the later statistical analysis. and distributed 40 questionnaires as the third questionnaire survey. First, we statistically contrasted the scores of the three 4.2. Infuence of Demographic Variables. After the statistical psychological factors in the second questionnaire survey and analysis of the scale data, the analysis results of drivers’ the scores in the third survey. Te results of the paired demographic information and driving information show sample t-test are shown in Table 8. As can be seen from the that the scores of male drivers in the same age group are table, the diferences were not signifcant when p values were signifcantly higher than those of female drivers in terms of all greater than 0.05, which indicated that the belief cog- driving age, weekly driving frequency, and everyday driving nition of the drivers regarding distracted driving behaviour time, which is consistent with other relevant studies [4, 24]. had not changed during half a month. In terms of driver’s license point deduction, male drivers in Second, using the method of repeated measures ANOVA the same age group are deducted more points than female analysis, the scores of the three types of distraction drivers. Te reason for this result may be related to the fact Journal of Advanced Transportation 9 Table 8: TPB three factors’ T-test in the second questionnaire survey and third questionnaire survey. Second questionnaire Tird questionnaire Te amount survey M survey M T p of changes (SD) (SD) Subjective norm 3.04 (0.31) 3.01 (0.29) −0.03 0.102 0.48 Behaviour attitude 2.03 (0.29) 1.98 (0.26) −0.05 0.481 0.32 Perceived behavioural control 2.51 (0.40) 2.43 (0.38) −0.08 1.115 0.14 Table 9: Mauchly’s test of sphericity. Te Within Mauchly’s Approx. Measure df Sig. Greenhouse-Geisser Huynh-Feldt Lower-bound amount of subjects efect W chi-square changes Visual Time point 0.424 31.774 2 <0.001 0.634 0.664 0.500 −0.51 distraction Cognitive Time point 0.365 37.312 2 <0.001 0.612 0.638 0.500 −0.57 distraction Physical Time point 0.465 28.294 2 <0.001 0.652 0.683 0.500 −0.52 distraction Table 10: Visual distraction multivariate test. Efect Value F Sig. Pillai’s trace 0.537 21.473 <0.001 Wilks’ lambda 0.463 21.473 <0.001 Time point Hotelling’s trace 1.161 21.473 <0.001 Roy’s largest root 1.161 21.473 <0.001 Pillai’s trace 0.465 16.061 <0.001 Wilks’ lambda 0.535 16.061 <0.001 Time point∗ group Hotelling’s trace 0.868 16.061 <0.001 Roy’s largest root 0.868 16.061 <0.001 that male drivers are more aggressive in driving style and driving compared with older groups. Additionally, weekly more prone to speeding, illegal lane changing, and other driving frequency and time have a signifcant positive correlation with the frequency of physical distraction. Tis violations . Furthermore, it is worth noting that female drivers in the same age group have a larger ratio of variance indicates that drivers with higher driving profciency are in mean driving experience than male drivers, indicating more likely to participate in physical distraction, and such that the polarization of driving experience among female drivers usually think that the physical distraction has little drivers is more obvious than the male ones. adverse impact on themselves. Among all types of distracted driving behaviours, the Relevant studies in social psychology have shown that frequency of cognitive distraction (2.79) is the highest, human behaviours are infuenced by internal beliefs. comparing with physical distraction (2.21) and visual dis- Whether an individual will carry out a certain behaviour is traction (2.11). Cognitive distraction can be experienced ultimately determined by the mediating variable of behav- when a driver is keeping sight on the road ahead, and often ioural intention [40, 41]. A similar mechanism exists for there is no action with one’s hands or feet. Terefore, from driver distraction. In recent years, with the in-depth research and practice of relevant theories, an increasing number of the perspective of risk perception, the harmfulness of cog- nitive distraction is generally considered to be the lowest. In scholars believe that the TPB is an efective means to analyze particular, the frequency of “talking to other people in the the mechanisms of various behaviours [42, 43]. However, car” is the highest, so its potential risk cannot be ignored. only the three core elements of TPB (attitude, subjective Terefore, relevant trafc regulations have been established norms, and perceived behavioural control) were involved in in many regions of China to prohibit public transport drivers the path ftting in the preliminary test. Many ftting resists from talking to passengers. were not fully explained by these paths. To further improve By analyzing the infuence of various demographic in- the overall explanatory of the model, this study explores formation pieces on driving distractions, it is found that the multiple types of potential infuencing factors of the driving distraction scale. Relevant literature studies on attention driver’s age has a signifcant negative correlation with the distraction frequency. A possible reason for this result is that characteristic factors show that drivers with low attention younger drivers tend to operate multimedia devices during levels in daily life tend to be more distracted while driving. 10 Journal of Advanced Transportation 2.6 3.2 2.5 3.1 2.4 3.0 2.3 2.9 2.8 2.2 2.7 2.1 2.6 2.0 1 23 1 23 Time Point Time Point Control group Control group Experimental group Experimental group (a) (b) 2.8 2.7 2.6 2.5 2.4 2.3 2.2 1 23 Time Point Control group Experimental group (c) Figure 6: Analysis of persistence of the intervention efect. (a) Vision distraction. (b) Cognition distraction. (c) Physical distraction. Table 12: Physical distraction multivariate test. Table 11: Cognitive distraction multivariate test. Efect Value F Sig. Efect Value F Sig. Pillai’s trace 0.558 23.312 <0.001 Pillai’s trace 0.529 20.791 <0.001 Wilks’ lambda 0.471 20.791 <0.001 Wilks’ lambda 0.442 23.312 <0.001 Time point Time point Hotelling’s trace 1.260 23.312 <0.001 Hotelling’s trace 1.124 20.791 <0.001 Roy’s largest root 1.124 20.791 <0.001 Roy’s largest root 1.260 23.312 <0.001 Pillai’s trace 0.636 32.284 <0.001 Pillai’s trace 0.545 22.129 <0.001 Wilks’ lambda 0.455 22.129 <0.001 Wilks’ lambda 0.364 32.284 <0.001 Time point∗ group Time point∗ group Hotelling’s trace 1.745 32.284 <0.001 Hotelling’s trace 1.196 22.129 <0.001 Roy’s largest root 1.196 22.129 <0.001 Roy’s largest root 1.745 32.284 <0.001 Terefore, this study adds the variables of drivers’ attention 4.3. Improving of the Intervention Efect. Driving distraction behaviour is afected by diferent psychological character- characteristics to increase the explanatory of the model. However, according to the ftting results of the structural istics. Diferent driving styles, value orientations, and drivers’ own attributes will afect their distraction intention equation model, the path coefcient is −0.09, a very weak negative correlation which is not statistically signifcant. to varying degrees. At present, research on driver distraction behaviour mostly remains in the feld of explicit visual However, Tosi et al.  preliminarily confrmed that at- tention characteristics have a certain potential infuence on distraction detection rather than exploring the internal driving behaviour. psychological essence of the distraction. Tis study Estimated Marginal Means Estimated Marginal Means Estimated Marginal Means Journal of Advanced Transportation 11 important factor. In any case, in the driving training process, innovatively explores the internal mechanism of Chinese driver distraction behaviour from a psychological perspec- not only general education links such as driving skills but also the test links of relevant psychological scales need to be tive by exploring the psychological motivation behind the distraction phenomenon. Furthermore, in order to make the added, to comprehensively evaluating whether drivers have intervention experiment more efective, educational in- a potentially high risk of distraction. As the most important tervention based on REBT was carried out for relevant infuencing factor, driver’s subjective norm needs to be paid psychological factors with a high path coefcient [33, 45, 46]. close attention to. Since subjective norm represents the Tis method has a better operability and allows us to psychological pressure outlined by the surrounding people conduct a centralized training intervention for 20 drivers in or society when drivers participate in distraction, the in- tervention course for subjective norm needs to start from the the form of courses, avoiding the psychological resistance of drivers involved in biomedical therapy. context level in the future to ensure the further improvement of the intervention efect. According to the evaluation of the intervention course’s efects, distraction attitude, subjective norms, and perceived Additionally, with the promotion of relevant national legislation, the majority of drivers have a certain un- behavioural control are signifcantly decreased, indicating that the educational intervention achieves a good validity. derstanding of the harm caused by distraction. However, However, the decline in the three factors is not consistent. many focus on the use of hand-held phone, ignoring that the Only the decline in attitudes exceeds 20%, reaching 21.1%, distraction behaviours found in this study are diverse. while the decline in the other two factors was less than 20%. Underestimating the harm caused by these behaviours will Tis result may be related to the shorter duration of the cause cognitive bias. Terefore, relevant cognitive in- intervention course. Terefore, in the future research, it is tervention courses must be included in driver training. necessary to explore the balance point of intervention design to ensure that the course validity and drivers’ acceptance of 4.4. Strengths and Limitations of the Research. Te use of intervention are of a high level. Additionally, from the per- a questionnaire base on the TPB further improved our spective of the sustainability of the intervention efect, the understanding of the psychological mechanism of driving three types of factors decrease by no more than 20%, but they distraction. Te educational intervention method adopted all exceed 15%, indicating that the persistence efect of the can well reduce the tendency of distracted driving behaviour. intervention is slightly weaker than the efectiveness. Because However, there are still some limitations in the current the evaluation of the persistence efect is only carried out at research. For example, in the analysis of the infuencing two weeks after the end of the intervention, there is a lack of factors of driving distraction, the existing latent variables do continuous tracking and comparison during longer periods. not fully explain driving distraction. In addition, in the Improving the driver’s cognitive level of distraction existing evaluation of the intervention efect, the sample size harm fundamentally reverses the driver’s distraction in- of divers is relatively small, the follow-up time is relatively tention. Te extraction of typical infuencing factors is short, and the long-term impact of educational intervention helpful for the distraction detection system to consider should be investigated in future research. psychological factors such as driver personality attributes and distraction traits as early warning correction parame- 5. Conclusion ters. Matching the diferences of individual attention traits to the personalized early warning model can correct the early Overall, this study innovatively explored the internal warning threshold, improve the accuracy of distraction mechanism of Chinese drivers’ distracted driving behaviour detection, and further expand the detection system from the and found that subjective norms are the most important explicit visual distraction level to the more implicit cognitive latent variables that afect the distraction. It showed the distraction feld. However, only three types of signifcant important infuence of social orientation on driving dis- psychological infuencing factors were identifed in this tractions. Other factors, such as attitude and perceived study. Relevant studies have added factors such as legal behavioural control, also signifcantly afect distraction norms and risk perception [17, 23], which constitute an behaviour. Tere are signifcant diferences in the tendency extended TPB to explain driver behaviour. Future research of distracted behaviour among diferent age groups. Te will need to examine diferent psychological factors to efectiveness and persistence of intervention training are in identify their impact on driver distraction. good performance, suggesting that educational intervention In the past, driver training was mostly guided by im- has promotional value. It efectively increased the drivers’ provements in driving skills. Driver cognition teaching awareness of the risks of distracted driving. Te research on mainly focused on familiarity with regulations and road the infuencing factors and intervention efects of distracted signs and did not target the correction of wrong driving driving behaviour can be applied to select drivers with habits . 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