Incorporating the Theory of Planned Behaviour into Distracted Driving: Influencing Factors and Intervention Effects

Zhang, L.; Peng, J. S.; Song, Z.; Fan, Z. B.; Yang, X. H.; Kong, Q. W.; Zhou, L.

doi:https://doi.org/10.1155/2023/7664577

Journal of Advanced Transportation

On this page

Abstract Introduction Methods Results Discussion Conclusion Data Availability Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2023 | Article ID 7664577 | https://doi.org/10.1155/2023/7664577

Incorporating the Theory of Planned Behaviour into Distracted Driving: Influencing Factors and Intervention Effects

L. Zhang,¹J. S. Peng,¹Z. Song,¹Z. B. Fan,¹X. H. Yang,¹Q. W. Kong,¹and L. Zhou¹

Academic Editor: Nirajan Shiwakoti

Received13 Oct 2022

Revised13 Mar 2023

Accepted17 Apr 2023

Published02 May 2023

Abstract

This study focuses on the psychological characteristics and empirically tests of the factors influencing distracted driving behaviours. This 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). The 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 significance of distracted behaviours and demographic variables. We use a structural equation model to determine the path coefficients 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 influential psychological factor. There are significant differences between the experimental group (2.38, SD = 0.41) and the control group (2.89, SD = 0.40) in the scores of distractions. This 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.

1. Introduction

Technological progress in the automobile industry and the application of advanced safety equipment has greatly improved vehicle safety [1]. Standardization of road design and construction technology has further contributed to this improvement [2]. An indispensable link in the driver-vehicle-road coupling system, statistics have shown that drivers are the most active and unstable factor, and their bad driving behaviours are the primary cause of frequent traffic accidents [3, 4]. In recent years, the increase in road traffic flow density and information on various road signs have increased drivers’ cognitive load. Additionally, the extensive use of smart phones and multimedia information systems have further consumed drivers’ limited attention resources [5], which may lead to a decline in driving performance levels or an increase in accident risk [6, 7]. Therefore, in order to reduce the accident risk caused by distracted driving behaviour, it is necessary to conduct a questionnaire study on distracted behaviour among drivers. By looking for the significant internal causes, we can effectively intervene the occurrence of distracted driving behaviour.

Distracted driving is a hot research topic in the field of road traffic safety in recent years. In this study, the core collection of Web of Science is used as the database, and a total of 3884 papers from 2002 to 2022 are obtained. The tendency of published papers is shown in Figure 1. Previous studies explored the influence of distracted driving behaviour on road safety from the aspects of driver gaze behaviour, saccade behaviour, vehicle speed, and trajectory [8–11]. Other studies had made comprehensive reviews of distracted driving behaviour such as cause, frequency, and detection methods [12–14].

Causes of distracted driving behaviour can be divided into visual distractions such as reading mobile phone messages and roadside objects [15, 16], cognitive distractions such as “daydreaming” and Bluetooth conversations [17, 18], and physical distractions such as sending text messages, changing the car settings, and so on [9, 19]. In contrast to visual and physical distractions, cognitive distractions may occur when a driver’s gaze is always on the road, which makes it covert and hard to detect [12]. Therefore, exploring the characteristics and psychological causes of driving distraction may help improve the current research in the field [20, 21].

Schroeder et al. [22] analyzed the characteristics of American drivers’ distracted behaviours to understand the underlying psychological mechanism. Talking among passengers and drivers, adjusting the radio, eating, and making a phone call are researched and ranked through a questionnaire survey to describe the occurrence regularity of distracted driving behaviour. However, the risk perception behind such distracted behaviour is not measured. To overcome this shortcoming, Prat et al. [23] used a semistructured scale and found that drivers could fully perceive only one dangerous behaviour and ignored other distracted behaviours. Rather than analyzing a single psychological factor influencing distracted driving behaviour, Chen et al. [24] verified the validity of a research framework of planned behaviour theory to analyze multiple factors that influence distracted driving behaviour, specifically age, gender, attitude, and prescriptive norms. However, owing to the differences in culture and traffic rules in different countries, the applicability of the above theoretical results needs to be studied in an environment with Chinese traffic characteristics.

In order to further elucidate the mechanism of distracted driving behaviour, a theoretical framework for this behaviour needs to be constructed. The TPB can well predict and explain the internal causes of behaviour through latent variables such as attitude, subjective norms, and perceived behavioural control [25]. It has been widely used in fields such as agricultural production [26], transit trip [27], consumption habit [28], and fitness exercise [29] et al. Therefore, based on the research framework of planned behaviour theory, this study constructs a driving distraction questionnaire and tries to find out the important factors that affect distraction behaviour.

After finding the factors that influence distracted driving behaviour, appropriate methods are needed to intervene the above factors to reduce the tendency of distracted behaviour. Behavioural psychology has recommended several theoretical methods for conducting intervention experiments which explore the mechanism of how different demographic and psychological factors influence distracted driving behaviour [30]. Brewster et al. [31] used the conscious awareness of intentions which can weaken the effect of habit, as an intervention in drivers’ speeding behaviour. By summarizing the main results of the above studies, it is found that most of the current studies focus on the frequency and influencing factors of distracted driving behaviour, but few studies carry out targeted educational intervention based on the results of the influencing factors. Therefore, in order to improve the deficiencies of existing research in the field of distracted driving behaviour, we conduct the intervention experiment based on cognitive behaviour therapy (CBT). CBT has proposed that behavioural intention is not directly determined by events but by people’s cognition of the events themselves. Therefore, this method helps people gradually realize their false beliefs and cognitions involved in past bad behaviour through debate. Then, false beliefs are replaced with proper beliefs to minimize the bad behaviour caused by false cognition. Among the common cognitive behavioural therapies, REBT is the most widely used and accepted behavioural intervention technique [32]. Therefore, by measuring the availability and reliability of various methods of training, REBT is finally selected as the intervention method.

The objectives of this study are to (i) develop a distracted driving scale based on the TPB as a research framework, (ii) use structural equation modeling to analyze the path coefficients of latent variables, and (iii) conduct a distraction intervention experiment based on REBT to evaluate the intervention’s effectiveness and sustainability.

2. Methods

In order to carry out investigation and intervention on distracted driving behaviour, this study was divided into two stages: building a distracted driving scale based on TPB, analyzing behavioural and psychological characteristics, and finding out the factors that affect distracted driving behaviour were presented in the first stage. Drivers with high distraction tendency were divided into the experimental group and the control group. The experimental group was conducted a distraction intervention experiment based on REBT, and the intervention effect was evaluated in the second stage.

2.1. Scale Items’ Development

This study collected original distracted behaviours by interviewing eight drivers with the use of self-reports and determined the classification of distraction types by consulting five experts. We designed the driving distraction scale based on the following five steps. Step 1: based on the research framework of classic TPB [25], the driving distraction scale was divided into 5 subscales, as shown in Table 1. Step 2: the distracted behaviours were extracted from the interviews and literature studies [23, 24], and the contents were modified and supplemented. Step 3: the distracted behaviours were refined by operationalization. Step 4: the driving distraction scale was given a title, response instructions, and acknowledgments. Step 5: ambiguous items were deleted or modified through a small-scale trial investigation. Each item was presented on a 5-point Likert scale [33]. After the preliminary deletion of several items, a validity test was performed by SPSS 26.0. The Kaiser-Meyer-Olkin (KMO) figure was 0.809, and the results of Mauchly’s test of sphericity were significant (), indicating that the driving distraction scale has a good validity [34].

2.2. Intervention Procedure

For the process of the intervention experiment, the experimental site was the Traffic Safety Education School of the Motor Vehicle Drivers Association in Nan’an District, Chongqing, China. The 40 drivers participating in the intervention were divided equally into the experimental group and the control group, and the demographic variables of the two groups were similar. First, before the implementation of the education intervention course, both groups of drivers completed the first questionnaire. Second, according to the intervention course’s characteristics of psychological diagnosis stage, comprehension stage, working through stage, and reeducation stage, the drivers in the experimental group received four educational interventions within a week (they were set on Monday, Wednesday, Friday, and Sunday), with each course of intervention time lasting an hour (10 : 00 AM-11 : 00 AM). Subsequently, the experimental group completed the second questionnaire, while the control group filled the second questionnaire without going through the courses. Finally, in order to verify the sustainability of the intervention effect, all participants were contacted again half a month after the educational intervention and filled in the questionnaire for the third time. The intervention experimental process is shown in Figure 2.

For the content of intervention experiment, according to the preset requirements of REBT experience, the intervention process was divided into four courses. In the first 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 process, the impact of distractions on normal driving, and the accidents caused by distractions so that the drivers could realize the high incidence and harmfulness of distractions. In the second lesson, we would analyze the risk perception and psychological motivation of drivers when they were distracted so that drivers could understand the mechanism of distraction. In the third lesson, gradually teaching correct driving knowledge to the participants would enable them to recognize and challenge their incorrect cognition, changed their interpretation of distracted driving behaviours, and transformed the bad behaviours into proper ones. In the fourth lesson, the proportion of distracted driving behaviours and their corresponding risk levels were listed so that drivers could further understand the dangers of distracted driving and generate self-correction ability (see Table 2 for details).

2.3. Participants

To ensure the sufficiently representative samples, 361 questionnaires were selected in the first study stage. As a megacity in China, Chongqing contains diverse traffic scenes and drivers. We conducted our study in Nan’an District of Chongqing and randomly selected drivers to complete the questionnaire survey. After excluding invalid questionnaires such as those with missing answers, a total of 321 valid questionnaires were collected, a response rate of 88.9%. The samples included 208 men and 113 women. 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 ages were evenly distributed, ranging from 18 to 57 years old, with an average age of 34.8 years and a standard deviation of 10.1 years. The number of driving experience ranged from 1 year to 31 years, with an average of 6.84 years and a standard deviation of 6.5 years.

In the second stage, with the assistance of the traffic police department, 40 drivers were selected from the first stage of 321 ordinary drivers, as intervention experiment participants. Compared with the ordinary drivers, the 40 drivers with a total of 12 points were deducted from their driver’s license. Such drivers are usually assumed to have a higher tendency to be distracted and need more traffic safety education.

3. Results

3.1. Demographics and Descriptive Variables

A total of 321 valid questionnaires were collected in this study. Please refer to Table 3 for more information about the participants. Regarding driving time per day, 82% of drivers drive for less than two hours a day. With the increase of age, the frequency of drivers driving every week has increased from sometimes driving to frequent driving. According to the statistics of driver’s license point deduction, 89% of drivers were deducted less than 6 points, and 76% less than 3 points. In addition, driving experience increase with age, with an average of 6.84 years for all drivers.

3.2. Tendency of Distracted Driving Behaviours

As seen in Table 4, the average value represents the tendency of each distraction behaviour, from 1 (never occur) to 5 (always occur). Cognitive distractions have the highest frequency (2.79, SD = 1.274) of the three types of distractions during driving. In this distraction type, “chatting with other people in the car” has the highest frequency (3.43, SD = 1.301). The highest frequency may be because drivers generally believe that this behaviour has little influence on driving in their daily driving scenarios. As a common type of distraction, physical distractions are ranked second in frequency, with an average of 2.21 (SD = 1.131). At the same time, “adjusting the air conditioner temperature and window” is the second most frequent distracted behaviour (3.31, SD = 1.206). The least frequent distraction type is visual distraction (2.11, SD = 1.131); “answering an acquaintance’s phone on the highway (hand-hold)” is the least frequent of all kinds of distracted behaviours, with a score of only 1.51 (SD = 0.979).

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 affected by a single factor has significant difference [35]. Therefore, the ANOVA method is used to analyze the relationship between drivers’ age and drivers’ distracted behaviours. Similarly, the average value represents the tendency of each distraction behaviour. That is, whether there are significant differences in the tendency of drivers of different age groups to engage in various distractions. The results are presented in Table 5. With an age interval of seven years, the drivers are divided into four groups, and the differences in cognitive distractions, visual distractions, physical distractions, and total distracted behaviours of drivers in different age groups were investigated. Cognitive distractions, visual distractions, and total distractions gradually increased from the 20–27 to 28–35 age group. However, for physical distractions, the largest mean value appears in the youngest age group and then decreases in the other age groups.

3.3. Path Coefficient from Latent Variables to Distractions

To further quantitatively demonstrate the influence of the relationships among the psychological factors in the questionnaire, a structural equation model was introduced to quantify the various latent variables. In addition, personal details were added to jointly explain the distracted behaviour and the assumptions H1–H4 were made in Figure 3. The structural equation model of data included the five latent variables of the driver’s personal details, distractions attitude, subjective norms, perceived behavioural control, and distracted behaviours. Each latent variable corresponded to several items as observed variables [25]. To facilitate the corresponding analysis in Amos 26.0 (structural equation modeling software), it was necessary to encode each latent variable and observation variable with symbols [36]. The coded item data were entered into the structural equation model (Figure 4) for path fitting, and the path was modified twice according to the fit index. It was added a path from A12 to P12 and set the paths from P to P7 and P to P8 as the same value. The fit index after the two modifications is shown in Table 6. Finally, a path graph that meets the requirements of the fit index was formed.

After the structure equation model and path modifications were determined, model path parameter estimation was conducted and the results with standardized estimation are shown in Table 7.

3.4. Intervention Effect

3.4.1. The Effectiveness of Intervention Training

The second questionnaire survey was conducted immediately after the course training. The score distribution of distracted behaviours of the drivers in the experimental group and the control group is plotted in Figure 5. It can be seen that the average score of distracted behaviours was lower in the experimental group, and the difference values after training were −0.50 (cognitive distraction), −0.45 (visual distraction), and −0.51 (physical distraction). In addition, the maximum and minimum scores of each behaviour also decreased in varying degrees, and the distribution range of the experimental group was significantly smaller than that of the control group, indicating that the score distribution of the experimental group was more concentrated.

3.4.2. The Persistence of Intervention Training

Half a month after the end of intervention courses, we contacted the drivers who had participated in the educational intervention and distributed 40 questionnaires as the third questionnaire survey.

First, we statistically contrasted the scores of the three psychological factors in the second questionnaire survey and the scores in the third survey. The results of the paired sample t-test are shown in Table 8. As can be seen from the table, the differences were not significant when values were all greater than 0.05, which indicated that the belief cognition of the drivers regarding distracted driving behaviour had not changed during half a month.

Second, using the method of repeated measures ANOVA analysis, the scores of the three types of distraction behaviours in the experimental group and the control group were compared in the three-time questionnaire survey. Mauchly’s test of sphericity was applied, as shown in Table 9.

Since the values of the spherical test for the three types of distracted behaviours are all less than 0.05, multivariate analysis of variance is required. The analysis results are shown in Tables 10–12 and in Figure 6. In figure, time point 1 is the first survey, time point 2 is the second survey, and time point 3 is the third survey. The results of the multivariate analysis of variance showed that the scores of the three types of distractions in the intervention group were significantly different across the mentioned time points. The results of interaction effect analysis between the time point and the intervention mode showed that the effects of intervention tests at different time points were maintained.

(a)

(b)

(c)

4. Discussion

4.1. Questionnaire Validity and Reliability

To further explore the psychological factors, it is necessary to carry out a targeted questionnaire survey on distracted driving behaviour [21, 24, 37, 38]. As the research basis of the questionnaire survey, this study constructed a driving distraction scale with good reliability and validity in line with a Chinese cultural background, traffic scenes, and traffic regulations. Compared with the questionnaire by Prat et al. [23], the driving distraction questionnaire established in this study incorporates more scene information to cover all three types of distracted behaviours. Drivers could understand the questionnaire contents more conveniently through using plentiful scene descriptions, which could further improve the reliability and validity of the distracted driving questionnaire. Additionally, through a small-scale preliminary survey, some vague items were deleted, which made the semantic expression of the driving distraction scale clearer and laid a good foundation for the later statistical analysis.

4.2. Influence of Demographic Variables

After the statistical analysis of the scale data, the analysis results of drivers’ demographic information and driving information show that the scores of male drivers in the same age group are significantly higher than those of female drivers in terms of driving age, weekly driving frequency, and everyday driving time, which is consistent with other relevant studies [4, 24]. In terms of driver’s license point deduction, male drivers in the same age group are deducted more points than female drivers. The reason for this result may be related to the fact that male drivers are more aggressive in driving style and more prone to speeding, illegal lane changing, and other violations [39]. Furthermore, it is worth noting that female drivers in the same age group have a larger ratio of variance in mean driving experience than male drivers, indicating that the polarization of driving experience among female drivers is more obvious than the male ones.

Among all types of distracted driving behaviours, the frequency of cognitive distraction (2.79) is the highest, comparing with physical distraction (2.21) and visual distraction (2.11). Cognitive distraction can be experienced when a driver is keeping sight on the road ahead, and often there is no action with one’s hands or feet. Therefore, from the perspective of risk perception, the harmfulness of cognitive distraction is generally considered to be the lowest. In particular, the frequency of “talking to other people in the car” is the highest, so its potential risk cannot be ignored. Therefore, relevant traffic regulations have been established in many regions of China to prohibit public transport drivers from talking to passengers.

By analyzing the influence of various demographic information pieces on driving distractions, it is found that the driver’s age has a significant negative correlation with the distraction frequency. A possible reason for this result is that younger drivers tend to operate multimedia devices during driving compared with older groups. Additionally, weekly driving frequency and time have a significant positive correlation with the frequency of physical distraction. This indicates that drivers with higher driving proficiency are more likely to participate in physical distraction, and such drivers usually think that the physical distraction has little adverse impact on themselves.

Relevant studies in social psychology have shown that human behaviours are influenced by internal beliefs. Whether an individual will carry out a certain behaviour is ultimately determined by the mediating variable of behavioural intention [40, 41]. A similar mechanism exists for driver distraction. In recent years, with the in-depth research and practice of relevant theories, an increasing number of scholars believe that the TPB is an effective means to analyze the mechanisms of various behaviours [42, 43]. However, only the three core elements of TPB (attitude, subjective norms, and perceived behavioural control) were involved in the path fitting in the preliminary test. Many fitting resists were not fully explained by these paths. To further improve the overall explanatory of the model, this study explores multiple types of potential influencing factors of the driving distraction scale. Relevant literature studies on attention characteristic factors show that drivers with low attention levels in daily life tend to be more distracted while driving. Therefore, this study adds the variables of drivers’ attention characteristics to increase the explanatory of the model. However, according to the fitting results of the structural equation model, the path coefficient is −0.09, a very weak negative correlation which is not statistically significant. However, Tosi et al. [44] preliminarily confirmed that attention characteristics have a certain potential influence on driving behaviour.

4.3. Improving of the Intervention Effect

Driving distraction behaviour is affected by different psychological characteristics. Different driving styles, value orientations, and drivers’ own attributes will affect their distraction intention to varying degrees. At present, research on driver distraction behaviour mostly remains in the field of explicit visual distraction detection rather than exploring the internal psychological essence of the distraction. This study innovatively explores the internal mechanism of Chinese driver distraction behaviour from a psychological perspective by exploring the psychological motivation behind the distraction phenomenon. Furthermore, in order to make the intervention experiment more effective, educational intervention based on REBT was carried out for relevant psychological factors with a high path coefficient [33, 45, 46]. This method has a better operability and allows us to conduct a centralized training intervention for 20 drivers in the form of courses, avoiding the psychological resistance of drivers involved in biomedical therapy.

According to the evaluation of the intervention course’s effects, distraction attitude, subjective norms, and perceived behavioural control are significantly decreased, indicating that the educational intervention achieves a good validity. However, the decline in the three factors is not consistent. Only the decline in attitudes exceeds 20%, reaching 21.1%, while the decline in the other two factors was less than 20%. This result may be related to the shorter duration of the intervention course. Therefore, in the future research, it is necessary to explore the balance point of intervention design to ensure that the course validity and drivers’ acceptance of intervention are of a high level. Additionally, from the perspective of the sustainability of the intervention effect, the three types of factors decrease by no more than 20%, but they all exceed 15%, indicating that the persistence effect of the intervention is slightly weaker than the effectiveness. Because the evaluation of the persistence effect is only carried out at two weeks after the end of the intervention, there is a lack of continuous tracking and comparison during longer periods.

Improving the driver’s cognitive level of distraction harm fundamentally reverses the driver’s distraction intention. The extraction of typical influencing factors is helpful for the distraction detection system to consider psychological factors such as driver personality attributes and distraction traits as early warning correction parameters. Matching the differences of individual attention traits to the personalized early warning model can correct the early warning threshold, improve the accuracy of distraction detection, and further expand the detection system from the explicit visual distraction level to the more implicit cognitive distraction field. However, only three types of significant psychological influencing factors were identified in this study. Relevant studies have added factors such as legal norms and risk perception [17, 23], which constitute an extended TPB to explain driver behaviour. Future research will need to examine different psychological factors to identify their impact on driver distraction.

In the past, driver training was mostly guided by improvements in driving skills. Driver cognition teaching mainly focused on familiarity with regulations and road signs and did not target the correction of wrong driving habits [47]. In this study, the driver distraction behaviour frequency data obtained through the distraction scale show that drivers participated in more distraction activities than expected, and among all psychological factors, subjective norm has the most significant impact on distracted behaviour. However,Chen et al. [24] regarded attitudes as the most significant impact and descriptive norm as the least important factor. In any case, in the driving training process, not only general education links such as driving skills but also the test links of relevant psychological scales need to be added, to comprehensively evaluating whether drivers have a potentially high risk of distraction. As the most important influencing factor, driver’s subjective norm needs to be paid close attention to. Since subjective norm represents the psychological pressure outlined by the surrounding people or society when drivers participate in distraction, the intervention course for subjective norm needs to start from the context level in the future to ensure the further improvement of the intervention effect.

Additionally, with the promotion of relevant national legislation, the majority of drivers have a certain understanding of the harm caused by distraction. However, many focus on the use of hand-held phone, ignoring that the distraction behaviours found in this study are diverse. Underestimating the harm caused by these behaviours will cause cognitive bias. Therefore, relevant cognitive intervention courses must be included in driver training.

4.4. Strengths and Limitations of the Research

The use of a questionnaire base on the TPB further improved our understanding of the psychological mechanism of driving distraction. The educational intervention method adopted can well reduce the tendency of distracted driving behaviour. However, there are still some limitations in the current research. For example, in the analysis of the influencing factors of driving distraction, the existing latent variables do not fully explain driving distraction. In addition, in the existing evaluation of the intervention effect, the sample size of divers is relatively small, the follow-up time is relatively short, and the long-term impact of educational intervention should be investigated in future research.

5. Conclusion

Overall, this study innovatively explored the internal mechanism of Chinese drivers’ distracted driving behaviour and found that subjective norms are the most important latent variables that affect the distraction. It showed the important influence of social orientation on driving distractions. Other factors, such as attitude and perceived behavioural control, also significantly affect distraction behaviour. There are significant differences in the tendency of distracted behaviour among different age groups. The effectiveness and persistence of intervention training are in good performance, suggesting that educational intervention has promotional value. It effectively increased the drivers’ awareness of the risks of distracted driving. The research on the influencing factors and intervention effects of distracted driving behaviour can be applied to select drivers with a potential distraction risk and conduct educational intervention for such groups to improve traffic safety.

Data Availability

The questionnaire survey data used to support the findings of this study were supplied by J.S. Peng under license and so cannot be made freely available. The data can be obtained from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

This study was supported by the University Creative Research Group Project of Chongqing (CXQT21022), Chongqing Graduate Research Innovation Foundation (CYB21218), and Natural Science Foundation Project of Chongqing (CSTB2022NSCQ-MSX0549 and CSTB2022NSCQ-MSX1516). The authors would like to thank Editage for English language editing. They are also grateful to Nan’an District Branch of Chongqing Traffic and Patrol Police Headquarters for the support of questionnaire.

References

S. Soheil, K. Ali, S. Maryam Mousavi, B. Dadashova, and D. Lord, “Quantifying the automated vehicle safety performance: a scoping review of the literature, evaluation of methods, and directions for future research,” Accident Analysis & Prevention, vol. 152, Article ID 106003, 2021.
View at: Google Scholar
E. Hauer, “On the relationship between road safety research and the practice of road design and operation,” Accident Analysis & Prevention, vol. 128, pp. 114–131, 2019.
View at: Google Scholar
P. Sârbescu and L. Maricuţoiu, “Are you a “bad driver” all the time? Insights from a weekly diary study on personality and dangerous driving behavior,” Journal of Research in Personality, vol. 80, pp. 30–37, 2019.
View at: Google Scholar
Z. Feng, Y. Lei, H. Liu et al., “Driving anger in China: a case study on professional drivers,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 42, pp. 255–266, 2016.
View at: Google Scholar
J. Tim, D. Shannon, M. Völler, F. Murphy, and M. Mullins, “Smartphone use while driving: an investigation of Young Novice Driver (YND) behaviour,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 77, pp. 209–220, 2021.
View at: Google Scholar
S. L. Kim and J. H. Yang, “Evaluation of the effects of driver distraction part 1: based on simulator experiments,” in Proceedings of the 2018 IEEE International Conference On Systems, Man, and Cybernetics (SMC), Miyazaki, Japan, October 2018.
View at: Publisher Site | Google Scholar
P. Papantoniou, “Structural equation model analysis for the evaluation of overall driving performance: a driving simulator study focusing on driver distraction,” Traffic Injury Prevention, vol. 19, no. 3, pp. 317–325, 2018.
View at: Google Scholar
W. Li, J. Huang, G. Xie, F. Karray, and R. Li, “A survey on vision-based driver distraction analysis,” Journal of Systems Architecture, vol. 121, Article ID 102319, 2021.
View at: Google Scholar
L. Qin, Z. R. Li, Z. Chen, M. S. Andi Bill, and D. A. Noyce, “Understanding driver distractions in fatal crashes: an exploratory empirical analysis,” Journal of Safety Research, vol. 69, pp. 23–31, 2019.
View at: Google Scholar
O. Oviedo-Trespalacios, M. M. Haque, M. King, and S. Washington, “Effects of road infrastructure and traffic complexity in speed adaptation behaviour of distracted drivers,” Accident Analysis & Prevention, vol. 101, pp. 67–77, 2017.
View at: Google Scholar
V. F. Sartori and A. P. C. Larocca, “Drivers’ speed profile at curves under distraction task,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 44, pp. 12–19, 2017.
View at: Google Scholar
A. Kashevnik, R. Shchedrin, C. Kaiser, and A. Stocker, “Driver distraction detection methods: a literature review and framework,” IEEE Access, vol. 9, Article ID 60063, 2021.
View at: Google Scholar
X. Wang, R. Xu, S. Zhang, Y. Zhuang, and Y. Wang, “Driver distraction detection based on vehicle dynamics using naturalistic driving data,” Transportation research part C: emerging technologies, vol. 136, Article ID 103561, 2022.
View at: Google Scholar
Y. Zhang, Y. Chen, and C. Gao, “Deep unsupervised multi-modal fusion network for detecting driver distraction,” Neurocomputing, vol. 421, pp. 26–38, 2021.
View at: Google Scholar
P. Gershon, K. R. Sita, C. Zhu et al., “Distracted driving, visual inattention, and crash risk among teenage drivers,” American Journal of Preventive Medicine, vol. 56, no. 4, pp. 494–500, 2019.
View at: Google Scholar
H. Grahn and T. Kujala, “Impacts of touch screen size, user interface design, and subtask boundaries on in-car task's visual demand and driver distraction,” International Journal of Human-Computer Studies, vol. 142, Article ID 102467, 2020.
View at: Google Scholar
G. M. Fitch, K. Grove, R. J. Hanowski, and M. A. Perez, “Compensatory behavior of drivers when conversing on a cell phone: investigation with naturalistic driving data,” Transportation Research Record, vol. 2434, no. 1, pp. 1–8, 2014.
View at: Google Scholar
D. L. Strayer, J. Turrill, M. Joel, Cooper, N. Medeiros-Ward, and F. Biondi, “Assessing cognitive distraction in the automobile,” Human Factors, vol. 57, no. 8, pp. 1300–1324, 2015.
View at: Google Scholar
P. Choudhary and N. R. Velaga, “Driver behaviour at the onset of yellow signal: a comparative study of distraction caused by use of a phone and a music player,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 62, pp. 135–148, 2019.
View at: Google Scholar
A. Sheykhfard and F. Haghighi, “Driver distraction by digital billboards? Structural equation modeling based on naturalistic driving study data: a case study of Iran,” Journal of Safety Research, vol. 72, pp. 1–8, 2020.
View at: Google Scholar
K. Shaaban, S. Gaweesh, and M. M. Ahmed, “Investigating in-vehicle distracting activities and crash risks for young drivers using structural equation modeling,” PLoS One, vol. 15, no. 7, Article ID e0235325, 2020.
View at: Google Scholar
P. Schroeder, M. Wilbur, and R. Peña, “National Survey on Distracted Driving Attitudes and Behaviors-2015,” United States, National Highway Traffic Safety Administration, Washington, DC, USA, 2018, No DOT HS 812 461.
View at: Google Scholar
F. Prat, M. E. Gras, M. Planes, S. Font-Mayolas, and M. J. M. Sullman, “Driving distractions: an insight gained from roadside interviews on their prevalence and factors associated with driver distraction,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 45, pp. 194–207, 2017.
View at: Google Scholar
H.-Y. W. Chen, B. Donmez, L. Hoekstra-Atwood, and S. Marulanda, “Self-reported engagement in driver distraction: an application of the Theory of Planned Behaviour,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 38, pp. 151–163, 2016.
View at: Google Scholar
I. Ajzen, “The theory of planned behavior: frequently asked questions,” Human Behavior and Emerging Technologies, vol. 2, no. 4, pp. 314–324, 2020.
View at: Google Scholar
A. Bagheri, N. Emami, and A. D. Christos, “Farmers' behavior towards safe pesticide handling: an analysis with the theory of planned behavior,” Science of the Total Environment, vol. 751, Article ID 141709, 2021.
View at: Google Scholar
X. Fu and Z. Juan, “Understanding public transit use behavior: integration of the theory of planned behavior and the customer satisfaction theory,” Transportation, vol. 44, no. 5, pp. 1021–1042, 2017.
View at: Google Scholar
I. Sen, F. Rad, and G. S. Agilkaya, “Analyzing fish consumption behavior of Turkish consumers with theory of planned behavior,” Thalassas: International Journal of Marine Science, vol. 38, pp. 929–937, 2022.
View at: Google Scholar
C. L. Palmer, L. burwitz, N. C. Smith, and A. borrie, “Enhancing fitness training adherence of elite netball players: an evaluation of maddux's revised theory of planned behaviour,” Journal of Sports Sciences, vol. 18, no. 8, pp. 627–641, 2000.
View at: Google Scholar
M. Karthaus, E. Wascher, M. Falkenstein, and S. Getzmann, “The ability of young, middle-aged and older drivers to inhibit visual and auditory distraction in a driving simulator task,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 68, pp. 272–284, 2020.
View at: Google Scholar
S. E. Brewster, M. A. Elliott, and S. W. Kelly, “Evidence that implementation intentions reduce drivers’ speeding behavior: testing a new intervention to change driver behavior,” Accident Analysis & Prevention, vol. 74, pp. 229–242, 2015.
View at: Google Scholar
A. Ellis, “Early theories and practices of rational emotive behavior therapy and how they have been augmented and revised during the last three decades,” Advances in REBT: Theory, Practice, Research, Measurement, Prevention and Promotion, Springer, New York, NY, USA, 2019.
View at: Google Scholar
H. Taherdoost, “What is the best response scale for survey and questionnaire design; review of different lengths of rating scale/attitude scale/Likert scale,” International Journal of Academic Research in Management, vol. 8, no. 1, pp. 1–10, 2019.
View at: Google Scholar
P. Mishra, U. Singh, C. M. Pandey, P. Mishra, and G. Pandey, “Application of student's t-test, analysis of variance, and covariance,” Annals of Cardiac Anaesthesia, vol. 22, no. 4, pp. 407–411, 2019.
View at: Google Scholar
T. K. Kim, “Understanding one-way ANOVA using conceptual figures,” Korean journal of anesthesiology, vol. 70, no. 1, pp. 22–26, 2017.
View at: Google Scholar
F. Puteh and M. H. A. Ong, “Quantitative data analysis: choosing between SPSS, PLS, and AMOS in social science research,” International Interdisciplinary Journal of Scientific Research, vol. 3, no. 1, pp. 14–25, 2017.
View at: Google Scholar
H. A. Reesi, J. Freeman, J. Davey, S. Al Adawi, and A. Al Maniri, “Measuring risky driving behaviours among young drivers: development of a scale for the Oman setting,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 55, pp. 78–89, 2018.
View at: Google Scholar
J. Feng, S. Marulanda, and B. Donmez, “Susceptibility to driver distraction questionnaire: development and relation to relevant self-reported measures,” Transportation Research Record, vol. 2434, no. 1, pp. 26–34, 2014.
View at: Google Scholar
L. Sun, L. Cheng, and Q. Zhang, “The differences in hazard response time and driving styles of violation-involved and violation-free taxi drivers,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 82, pp. 178–186, 2021.
View at: Google Scholar
D. Novita and N. Husna, “The influence factors of consumer behavioral intention towards online food delivery services,” TECHNOBIZ: International Journal of Business, vol. 3, no. 2, pp. 40–42, 2020.
View at: Google Scholar
C.-M. Chao, “Factors determining the behavioral intention to use mobile learning: an application and extension of the UTAUT model,” Frontiers in Psychology, vol. 10, 2019.
View at: Google Scholar
M. Aboelmaged, “E-waste recycling behaviour: an integration of recycling habits into the theory of planned behaviour,” Journal of Cleaner Production, vol. 278, Article ID 124182, 2021.
View at: Google Scholar
F. Razali, D. Daud, C. Weng-Wai, and Wilson Rangga Anthony Jiram, “Waste separation at source behaviour among Malaysian households: the Theory of Planned Behaviour with moral norm,” Journal of Cleaner Production, vol. 271, Article ID 122025, 2020.
View at: Google Scholar
J. D. Tosi, R. D. Ledesma, S. A. Useche, G. Dorantes-Argandar, and O. Oviedo-Trespalacios, “Assessing the factor structure of the behaviour of young novice drivers scale (BYNDS),” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 72, pp. 171–183, 2020.
View at: Google Scholar
A. C. James, T. Reardon, A. Soler, G. James, and C. Creswell, “Cognitive behavioural therapy for anxiety disorders in children and adolescents,” Cochrane database of systematic reviews, vol. 11, Article ID CD013162, 2020.
View at: Google Scholar
P. Carlbring, G. Andersson, P. Cuijpers, H. Riper, and E. Hedman-Lagerlöf, “Internet-based vs. face-to-face cognitive behavior therapy for psychiatric and somatic disorders: an updated systematic review and meta-analysis,” Cognitive Behaviour Therapy, vol. 47, no. 1, pp. 1–18, 2018.
View at: Google Scholar
J. He, Z. Wang, M. King, H. Wen, and C. Zhao, “Research on prediction methods for motor vehicle driver training demand based on an S-curve,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 58, pp. 831–842, 2018.
View at: Google Scholar

Copyright

Copyright © 2023 L. Zhang et al. This 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.

PDF Download Citation

Download other formats

Order printed copies