Perceived enjoyment, concentration, intention, and speed violation behavior: Using flow theory and theory of planned behavior

Atombo, Charles; Wu, Chaozhong; Zhang, Hui; Wemegah, Tina Dzigbordi

doi:10.1080/15389588.2017.1307969

Cited by 29 publications

(21 citation statements)

References 43 publications

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“…[ 14 ] There were other investigations which also confirmed the positive effect of attitude on safe-driving behaviors. [ 17 21 29 ] Some other research in Queensland, Australia, showed that drivers' attitude, as a key factor in safe driving, can be altered through educating driving techniques and skills as well as the adoption of TPB. [ 30 ] Therefore, education is to be taken seriously in changing attitude to driving behaviors.…”

Section: Discussionmentioning

confidence: 99%

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Prediction of taxi drivers' safe-driving behaviors based on the theory of planned behavior: The role of habit

Razmara

Aghamolaei

Madani

et al. 2018

J Edu Health Promot

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Section: Discussionmentioning

confidence: 99%

“…observed that intention was a predictor of speed rule violation. [ 29 ] A research conducted by Ashoogh et al . showed that the stronger the correlation between intention and behavior, the significantly higher the occurrence rate of health-related behaviors.…”

Section: Discussionmentioning

confidence: 99%

Prediction of taxi drivers' safe-driving behaviors based on the theory of planned behavior: The role of habit

Razmara

Aghamolaei

Madani

et al. 2018

J Edu Health Promot

View full text Add to dashboard Cite

“…Before testing of the structural model, the indicators were subjected to Confirmatory Factor Analysis (CFA) to evaluate how each item represents their corresponding latent variables and also determine the validity and reliability of the scale. Because the construct of weekday and weekend were According to previous studies, goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), comparative fit index (CFI) and normed fit index (NFI) values of 0.9 or above and root mean square error of approximation (RMSEA) of 0.08 or less indicate a good fit between the model and the data [45,46]. As displayed in Fig.…”

Section: Measurement Modelmentioning

confidence: 99%

“…Based on previous study recommendations, a two-step approach was used for the analyses [44,45]. Before testing of the structural model, the indicators were subjected to Confirmatory Factor Analysis (CFA) to evaluate how each item represents their corresponding latent variables and also determine the validity and reliability of the scale.…”

Section: Measurement Modelmentioning

confidence: 99%

Modeling the effect of days and road type on peak period travels using structural equation modeling and big data from radio frequency identification for private cars and taxis

Wemegah

Zhu

Atombo

2018

Eur. Transp. Res. Rev.

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Purpose: The main congestion on roads occur during peak hours, apart from incidents such as road accidents and construction works. Although there have been studies on peak period travels, these studies have only implicitly considered weekday, weekend and road type in their investigations. In this paper, it is proposed to investigate explicitly, the effect of weekday and weekend travel variability and road type on peak hour vehicular movement which leads to congestion. A study of vehicular movement patterns during these times can influence and impact on planning decisions for transportation engineers. Methods: This study utilizes structural equation model (SEM) to investigate the vehicular movements influence of weekdays, weekends, road type choice and car type on two peak hour periods 6 am to 9 am and 4 pm to 7 pm and one off-peak hour 9 am to 12 noon. Results: Using vehicular movement data from Radio Frequency Identification for Nanjing, China, for the month of May 2014, it was revealed that in most of the cases, weekday travels influence peak hour travels more than weekends and that off-peak hour travels for both weekdays and weekends show little variations. The study also discovered that choice of road type and car type, have varying influence on peak hour travels. Conclusions: The high significance ratios of results prove that these chosen variables are suitable for investigations into peak hour travel pattern studies. The study has also proved the viability of this modeling method to investigate policy measures to reduce peak period congestion.

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“…Wang et al [20] defined the impacts of his/her physical and mental states on traffic safety as the driver's propensity, which covers the mentality towards the traffic condition and decision/behavior under various dynamic factors. Under the theory of planned behavior (TPB), Atombo et al [21] identified the determinants of the driver's intention to speed and speed violation behavior.…”

Section: Introductionmentioning

confidence: 99%

Influencing Factors for Illegal Driving Behaviors of Rural Bus Drivers

Xiao¹,

Liang²

2020

IJSSE

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To improve the safety management of rural buses, this paper attempts to identify the factors affecting the drivers' behavioral features and their illegal driving behaviors. From the perspective of driver psychology, the safety incentive (SI) index was introduced to improve the theory of planned behavior (TPB) model. Then, the authors verified the improved TPB model for the illegal driving behaviors of rural bus drivers. In addition, the influence mechanisms of several factors on the illegal driving behaviors were fully analyzed, namely, attitude toward the behavior (AB), subjective norm (SN), perceived behavioral control (PBC), and the SI. The results show that the rural bus drivers have not developed a correct understanding of illegal driving behaviors; the attitudes of families, friends and passengers can indirectly affect the driving behaviors; the PBC of the driver has a direct or an indirect influence on the illegal driving behaviors; the SI, as the additional index, has a significant inhibitory effect on illegal driving behaviors. Finally, it is advised to improve the safety management of rural buses by improving education and publicity, supervising the whole driving process, and perfecting the SI system.

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Perceived enjoyment, concentration, intention, and speed violation behavior: Using flow theory and theory of planned behavior

Cited by 29 publications

References 43 publications

Prediction of taxi drivers' safe-driving behaviors based on the theory of planned behavior: The role of habit

Prediction of taxi drivers' safe-driving behaviors based on the theory of planned behavior: The role of habit

Modeling the effect of days and road type on peak period travels using structural equation modeling and big data from radio frequency identification for private cars and taxis

Influencing Factors for Illegal Driving Behaviors of Rural Bus Drivers

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