Understanding electric bike riders’ intention to violate traffic rules and accident proneness in China

Tang, Tianpei; Guo, Yuntao; Zhou, Xizhao; Labi, Samuel; Zhu, Senlai

doi:10.1016/j.tbs.2020.10.010

Cited by 51 publications

(27 citation statements)

References 84 publications

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“…After subjective norm, PBC was important predictor compared to attitude. In particular, the current study found normative component was important predictor of intention for all three violations, supporting previous studies on traffic violations [3,26,27]. The findings of Forward et al [3] showed that norm as important determinant in understanding the difference between traffic violators and non-violators.…”

Section: Predictorssupporting

confidence: 89%

Work-family Conflict, Perceived Stress and Intention to Commit Traffic Violations: A Model Guided by The Theory of Planned Behavior

Shukri¹

2021

Proceedings of the International Conference on Psychological Studies (ICPSYCHE 2020)

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Despite the importance of traffic violations in relation to road safety, little is known, however, about the link between stress particularly work-family conflict and violations behaviors. This study assessed the predictive effect of the theory of planned behavior (TPB) constructs and additional predictors to account for drivers' intentions to commit three specific driving violations: speeding, overtaking in dangerous situations and running red light. This study also extended the theory by adding stress factors (work-family conflict, perceived stress) to assess their influences on the traffic violation intentions. A sample of Malaysian drivers (N = 482; (44.6) males and (55.4%) females), with the average age of 36.7 (SD =10.0) was surveyed with a questionnaire constructed to measure TPB components (attitude, perceived behavioral norm; PBC, subjective norm), stress measures (workfamily conflict, perceived stress), additional predictors (past violation behavior and anticipated regret) and behavioral intentions. Results showed that TPB provided a significant prediction of speeding, running red light and overtaking (43%, 55%, and 47% respectively) with subjective norm construct was the most predictive variable, followed by the PBC. The addition of past behavior and anticipated regret produced small amount of variance explained. No association was found perceived stress and work interference with family (WIF) and violation intentions. Family interference with work (FIW) significantly predicted speeding intention, indicating that as FIW increased, intention to avoid speeding weakened. Mediation analyses confirmed that FIW was related to speeding and overtaking intentions through attitude component. Of demographic variables, gender and age were significant predictors of intention to commit traffic violations. Practical implications of the findings for work-family roles and road safety are discussed.

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Section: Predictorssupporting

confidence: 89%

Work-family Conflict, Perceived Stress and Intention to Commit Traffic Violations: A Model Guided by The Theory of Planned Behavior

Shukri¹

2021

Proceedings of the International Conference on Psychological Studies (ICPSYCHE 2020)

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“…where µ and σ are the mean and the standard deviation of the variables, respectively. Values of R exceeding 0.8 are typical of a satisfactory correlation [73,74]. The second provides an accurate estimation of the model generalization capability by averaging the squares of the differences between backcalculated and predicted moduli: The third one represents in what percentage the input variables can explain a variation of the output variable:…”

Section: Resultsmentioning

confidence: 99%

A Machine Learning Approach to Determine Airport Asphalt Concrete Layer Moduli Using Heavy Weight Deflectometer Data

et al. 2021

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An integrated approach based on machine learning and data augmentation techniques has been developed in order to predict the stiffness modulus of the asphalt concrete layer of an airport runway, from data acquired with a heavy weight deflectometer (HWD). The predictive model relies on a shallow neural network (SNN) trained with the results of a backcalculation, by means of a data augmentation method and can produce estimations of the stiffness modulus even at runway points not yet sampled. The Bayesian regularization algorithm was used for training of the feedforward backpropagation SNN, and a k-fold cross-validation procedure was implemented for a fair performance evaluation. The testing phase result concerning the stiffness modulus prediction was characterized by a coefficient of correlation equal to 0.9864 demonstrating that the proposed neural approach is fully reliable for performance evaluation of airfield pavements or any other paved area. Such a performance prediction model can play a crucial role in airport pavement management systems (APMS), allowing the maintenance budget to be optimized.

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“…The behavior patterns of users in bikesharing systems are also worth exploring. The estimation results of [22] show that descriptive norm, conformity tendency, and past behavior are important factors that affect both e-bike riders' intention to violate traffic rules and accident proneness. The work of [23] found that perceived ease of use positively influences the attitude towards the systems and the use intention.…”

Section: Related Workmentioning

confidence: 99%

“…In all bike-related problems, the most widely studied is bike demand or traffic flow prediction. The studies of [22,36] have identified the importance of natural environmental factors such as temperature, precipitation, and humidity on cycling activities across different cities. At the feature level, studies [5,37] consider a single factor instead of multiple aspects features and thus may neglect representative elements.…”

Section: Related Workmentioning

confidence: 99%

Inferring Long-Term Demand of Newly Established Stations for Expansion Areas in Bike Sharing System

et al. 2021

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Research on flourishing public bike-sharing systems has been widely discussed in recent years. In these studies, many existing works focus on accurately predicting individual stations in a short time. This work, therefore, aims to predict long-term bike rental/drop-off demands at given bike station locations in the expansion areas. The real-world bike stations are mainly built-in batches for expansion areas. To address the problem, we propose LDA (Long-Term Demand Advisor), a framework to estimate the long-term characteristics of newly established stations. In LDA, several engineering strategies are proposed to extract discriminative and representative features for long-term demands. Moreover, for original and newly established stations, we propose several feature extraction methods and an algorithm to model the correlations between urban dynamics and long-term demands. Our work is the first to address the long-term demand of new stations, providing the government with a tool to pre-evaluate the bike flow of new stations before deployment; this can avoid wasting resources such as personnel expense or budget. We evaluate real-world data from New York City’s bike-sharing system, and show that our LDA framework outperforms baseline approaches.

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Understanding electric bike riders’ intention to violate traffic rules and accident proneness in China

Cited by 51 publications

References 84 publications

Work-family Conflict, Perceived Stress and Intention to Commit Traffic Violations: A Model Guided by The Theory of Planned Behavior

Work-family Conflict, Perceived Stress and Intention to Commit Traffic Violations: A Model Guided by The Theory of Planned Behavior

A Machine Learning Approach to Determine Airport Asphalt Concrete Layer Moduli Using Heavy Weight Deflectometer Data

Inferring Long-Term Demand of Newly Established Stations for Expansion Areas in Bike Sharing System

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