Factors affecting the intention to wear helmets for e-bike riders: the case of Chinese college students.

As the popularity of electric bicycles (e-bikes) continues to surge, the number of accidents involving them has commensurately increased. A significant factor contributing to the high fatality rate in these accidents is the low usage of helmets among e-bike riders. Helmets have been proven to reduce the severity of injuries, yet their usage remains unexpectedly low. This issue is particularly pronounced among college students, the primary buyer group for e-bikes. Regrettably, there is a lack of research exploring their intentions to wear helmets. Understanding determinants of their intentions to wear helmets is crucial in promoting safe e-bike travel. Therefore, the present study aims to develop an integrated theoretical model that combines the Theory of Planned Behavior (TPB) and the Health Belief Model (HBM) to examine the factors influencing e-bike riders' helmet-wearing intentions among college students. Additionally, two variables-descriptive norms and law enforcement-are incorporated. The results indicate that the integrated model accounts for 76% of the variance in helmet-wearing intention, surpassing single-theory models. Specifically, the TPB accounts for 65%, while the HBM explains 53%. Notably, law enforcement emerges as the most influential factor, highlighting the crucial role of enforcing regulations and promoting awareness. Other significant factors include subjective and descriptive norms, attitudes, perceived benefits, perceived susceptibility, perceived barriers, and perceived severity. These findings provide valuable insights for policy development and targeted interventions aimed at improving helmet wear rates among e-bike riders, especially among the college student population.

Assessing the intention to accept inquiry-based teaching pedagogy among Chinese university students: an extension of technology acceptance model.

Introduction: Due to the limitations of traditional didactic teaching, inquiry-based teaching has attracted increasing attention and has become an important content of curriculum teaching reform in college education. Nevertheless, it is vital to investigate students' subjective acceptance of inquiry-based instruction and its influencing factors before inquiry-based teaching methods are widely implemented. Methods: In light of this, taking into account the psychological factors of students, an acceptance model of inquiry-based teaching pedagogy was established based on the extended technology acceptance model (TAM). Three additional variables, namely self-efficacy, implementation quality, and risk perception, were incorporated into the TAM. Firstly, subjective evaluation data of the influencing factors of inquiry teaching acceptance were obtained through a network questionnaire survey from university students in Guangdong, China, using snowball sampling and convenient sampling. A total of 485 valid questionnaires were retrieved, with an effective response rate of 88.2%. Then, internal consistency and reliability, convergent validity and discriminant validity of the model and its hypothesis were tested with reliability and validity tests. Finally, path analysis was used to examine key determinants of students' acceptance of inquiry teaching and moderators. Results: Results indicated that the constructed model can explain the acceptability of inquiry teaching for college students by 88.6%; Attitude has a positive significant impact on behavioral intention; Perceived ease of use indirectly affects behavioral intention through perceived usefulness, while perceived usefulness indirectly affects behavioral intention through attitude; self-efficacy not only directly affects behavioral intention but also indirectly affects behavioral intention through implementation quality; implementation quality indirectly affects behavioral intention through perceived usefulness and attitude; students' risk perception of inquiry-based teaching has no negative impact on behavioral intention. Conclusion: Overall, this study has implications for policymakers, teachers or learners in terms of the implementation and promotion of inquiry-based teaching in college classroom.

Effect of Music Listening on Physiological Condition, Mental Workload, and Driving Performance with Consideration of Driver Temperament.

This paper presents the study on the association between in-vehicle music listening, physiological and psychological response, and driving performance, using the driving simulator approach, with which personality (temperament) was considered. The performance indicators considered were the standard deviation of speed, lane crossing frequency, perceived mental workload, and mean and variability of heart rate. Additionally, effects of the presence of music and music genre (light music versus rock music) were considered. Twenty participants of different personalities (in particular five, four, seven, and four being choleric, sanguine, phlegmatic, and melancholic, respectively) completed a total of 60 driving simulator tests. Results of mixed analysis of variance (M-ANOVA) indicated that the effects of music genre and driver character on driving performance were significant. The arousal level perceived mental workload, standard deviation of speed, and frequency of lane crossing were higher when driving under the influence of rock music than that when driving under the influence of light music or an absence of music. Additionally, phlegmatic drivers generally had lower arousal levels and choleric drivers had a greater mental workload and were more likely distracted by music listening. Such findings should imply the development of cost-effective driver education, training, and management measures that could mitigate driver distraction. Therefore, the safety awareness and safety performance of drivers could be enhanced.

Modeling an enhanced ridesharing system with meet points and time windows.

With the rising of e-hailing services in urban areas, ride sharing is becoming a common mode of transportation. This paper presents a mathematical model to design an enhanced ridesharing system with meet points and users' preferable time windows. The introduction of meet points allows ridesharing operators to trade off the benefits of saving en-route delays and the cost of additional walking for some passengers to be collectively picked up or dropped off. This extension to the traditional door-to-door ridesharing problem brings more operation flexibility in urban areas (where potential requests may be densely distributed in neighborhood), and thus could achieve better system performance in terms of reducing the total travel time and increasing the served passengers. We design and implement a Tabu-based meta-heuristic algorithm to solve the proposed mixed integer linear program (MILP). To evaluate the validation and effectiveness of the proposed model and solution algorithm, several scenarios are designed and also resolved to optimality by CPLEX. Results demonstrate that (i) detailed route plan associated with passenger assignment to meet points can be obtained with en-route delay savings; (ii) as compared to CPLEX, the meta-heuristic algorithm bears the advantage of higher computation efficiency and produces good quality solutions with 8%~15% difference from the global optima; and (iii) introducing meet points to ridesharing system saves the total travel time by 2.7%-3.8% for small-scale ridesharing systems. More benefits are expected for ridesharing systems with large size of fleet. This study provides a new tool to efficiently operate the ridesharing system, particularly when the ride sharing vehicles are in short supply during peak hours. Traffic congestion mitigation will also be expected.