Comparing the cycling behaviours of Australian, Chinese and Colombian cyclists using a behavioural questionnaire paradigm

Prediction of rear-seat belt use: Application of extended theory of planned behavior

OBJECTIVES

Rear-seat belts have been shown to significantly reduce the severity of road vehicle collisions and fatalities. However, their use by rear-seat passengers is significantly less than that by front-seat passengers. Thus, the psychological factors underlying individuals' decision to wear a seat belt in the rear seat require further investigation.

METHODS

An extended theory of planned behavior (eTPB) was used to examine individuals' behavior of wearing a rear-seat belt. An online survey was conducted and a total of 515 valid questionnaires were collected in China.

RESULTS

While attitude, descriptive norms, and law enforcement all have a significant effect on individuals' intention to wear a seat belt in the rear, subjective norms and perceived behavioral control do not. Individuals' attitudes toward wearing a seat belt in the rear seat are significantly influenced by law enforcement and behavioral intention, but not by perceived behavioral control. The mediation effect analysis reveals that law enforcement has the greatest overall effect on behavior, followed by attitude and descriptive norms.

CONCLUSIONS

The results of this paper contribute to more effective recommendations to improve the use of rear seat belts and to safeguard rear seat passengers.

Predictors of older people's intention to engage in cycling violation behaviour with an integrative model

In China, bicycles are a popular mode of transportation for senior citizens. A disproportionate number of traffic-related fatalities and injuries involve cyclists. The violation of cycling laws is a significant cause of cyclist crashes. Few studies have analyzed the cycling violation behaviour of seniors. Therefore, it is essential to examine the factors that influence older individuals' intention to engage in cycling violation behaviours. In this study, the effects of social-demographic characteristics, the exogenous constructs in the health belief model (HBM), and the theory of planned behaviour (TPB) on senior cyclists' violation intention were investigated using hierarchical regression analysis. Interviews were conducted with older cyclists in urban areas of Wuhan City, all above 60 years of age. The results showed that very little variance in behavioural intention could be explained by social-demographic factors. The TPB has a significantly greater capacity than the HBM to explain variance in behavioural intention. Perceived susceptibility, perceived benefit, cues to action, subjective norm and attitude significantly impacted behavioural intention, whereas perceived severity, perceived barrier and self-efficacy did not.

How do driving behavior and attitudes toward road safety vary between developed and developing countries? Evidence from Iran and the Netherlands

INTRODUCTION

The rates of road traffic injuries and fatalities in developing countries are significantly higher than in developed countries. This study examines the differences in driving behavior, road safety attitudes, and driving habits between a developed country (the Netherlands) and a developing country (Iran), which bear major differences in terms of crash involvement per population.

METHOD

In this context, this study assesses the statistical association of crash involvement with errors, lapses, aggressive driving incidents, and non-compliance with traffic rules, attitudes, and habits. Structural equation modeling was used to evaluate data obtained from 1,440 questionnaires (720 samples for each group).

RESULTS

The results revealed that more insecure attitudes toward traffic-regulation observance, negative driving habits, and risky behaviors, such as traffic rule violations act as influential factors of crash involvement. Iranian participants showed a greater likelihood to get involved in violations and driving habits with a higher level of risk. In addition, lower levels of safety attitudes toward traffic-regulation observance were observed. On the other hand, Dutch drivers were more likely to report lapses and errors. Dutch drivers also reported safer behavior in terms of unwillingness to engage in risky behaviors such as violations (speeding and no-overtaking). The structural equation models for crash involvement based on behaviors, attitudes, and driving habits were also evaluated for their accuracy and statistical fit using relevant indicators.

PRACTICAL APPLICATIONS

Finally, the findings of the present study point out the need for extensive research in some areas to foster policies that can effectively enhance safer driving.

Sharing roads with automated vehicles: A questionnaire investigation from drivers', cyclists' and pedestrians' perspectives

Despite the promised benefits, the introduction of Automated Vehicles (AVs) on roads will be confronted by many challenges, including public readiness to use those vehicles and share the roads with them. The risk profile of road users is a key determinant of their safety on roads. However, the relation of such risk profiles to road users' perception of AVs is less known. This study aims to address the above research gap by conducting a cross-sectional survey to investigate the acceptance of Fully Automated Vehicles (FAVs) among different non-AV-user groups (i.e., pedestrians, cyclists, and conventional vehicle drivers). A total of 1205 road users in Queensland (Australia) took part in the study, comprising 456 pedestrians, 339 cyclists, and 410 drivers. The Theory of Planned Behaviour (TPB) is used as the theoretical model to examine road users' intention towards sharing roads with FAVs. The risk profile of the participants derives from established behavioural scales and individual characteristics are also included in the acceptance model. The study results show that pedestrians reported lowest intention in terms of sharing roads with FAVs among the three groups. Drivers and cyclists in a lower risk profile group were more likely to report higher intention to share roads with FAVs than those in a higher risk profile group. As age increased, pedestrians were less likely to accept sharing roads with FAVs. Drivers who had more exposure time on roads were more likely to accept sharing roads with FAVs. Male drivers reported higher intention towards sharing roads than female drivers. Overall, the study provides new insights into public perceptions of FAVs, specifically from the non-AV-user perspective. It sheds light on the obstacles that future AVs may encounter and the types of road users that AV manufacturers and policymakers should consider closely. Specifically, groups such as older pedestrians and road users who engage in more risky behaviours might resist or delay the integration of AVs.

Adolescent Aggressive Riding Behavior: An Application of the Theory of Planned Behavior and the Prototype Willingness Model

Cycling has always been popular in China, especially during the years when the government encouraged green travel. Many people participate in rides to ease traffic congestion and increase transfer convenience. Due to the disorganized and tidal nature of cycling, cyclists create many conflicts with other groups. Adolescents are vulnerable road users with a strong curiosity and risk-taking mindset. Identifying the factors influencing adolescents’ aggressive riding behavior can assist in developing strategies to prevent this behavior. An online questionnaire was used to collect data on bicycling among students in a middle school in Guangzhou, China. The theory of planned behavior (TPB) and prototype willingness model (PWM) have been applied to study travel behavior and adolescent risk behavior. To investigate the impact of psychological variables on adolescent aggressive behavior, we used TPB, PWM, TPB + PWM, and an integrated model. Behavioral intentions are greatly influenced by attitudes, subjective norms, and perceived behavioral control. Both descriptive and moral norms played a role in behavioral willingness. The integrated model explained 18.3% more behavioral variance than the TPB model. The social reactive pathway explained more variance in behavior than the rational path.

A quantitative traffic performance comparison study of bicycles and E-bikes at the non-signalized intersections: Evidence from survey data

As common transportation modes at non-signalized intersections, bicycles and e-bikes have been involved in most traffic crashes. Although a large number of studies have been dedicated to studying the safety problems caused by bicycles and e-bikes, there is still limited attention paid to the differences between them, especially at non-signalized intersections. This paper compares the differences between bicycles and e-bikes based on a self-administered questionnaire. This questionnaire was distributed to bicycle users (N = 453) and e-bike users (N = 439). The personal characteristics, decision-making capacity, the feeling of infrastructure, perceived level of service, and perceived level of risk were adopted as the performance indicators to depict the difference in the study area. Using statistical methodologies and the Structural Equation Model (SEM), key findings indicate that perceived level of service was found to be significantly different between bicycles and e-bikes at most non-signalized intersections. 43.4 % of e-bike riders often or always choose to avoid riding under extreme weather, while 58.7 % of bicycle riders avoid riding under extreme weather. Moreover, compared with bicycles, e-bikes' decision-making capacity is affected more by infrastructure quality. The difference between bicycles and e-bikes highlights the need for differentiated development of cycling safety education and law enforcement.

Risky business: Comparing the riding behaviours of food delivery and private bicycle riders

The growth in the gig economy and a preference for home delivery of meals due to COVID-19 have led to huge growth in the food delivery business internationally and consequent road safety concerns. There is increasing evidence that delivery riding is an occupation with significant road safety risks because work pressures encourage risky behaviours. However, there is little or no research that directly compares delivery and private riders. Thus, the aim of this study was to examine the impact of riding for work by comparing the observable riding behaviours of food delivery and private bicycle riders. Specifically, this investigation used decision trees to analyse the prevalence and patterns of risky riding behaviours of 2274 bicycle food delivery riders (BFDRs) and 1127 private bicycle riders observed in the inner suburbs of Brisbane, Australia. The results showed that helmet use was higher for BFDRs than private riders (99.8% versus 93.4%) but varied by company and for some companies, female BFDRs had lower wearing rates. Male BFDRs on electric bikes were more likely to wear helmets than those on standard bikes (99.7% versus 94.9%). Using a handheld mobile phone or having a mobile phone in a cradle was less common for one company (0.6%) than for the others (3.0%) or among private riders (1.8%). Among riders from the Other Companies, using a handheld mobile phone was more common on standard bikes and differed by time of day. Female BFDRs were more likely to be observed using handheld mobile phones. Overall, 24.0% of riders facing a red traffic or pedestrian signal ("red light") did not stop. This was more common among riders who rode on the footpath (Australian term for sidewalk), and particularly those who moved between the footpath and the road on electric bikes (49.5%) and among those who rode in the wrong direction in the traffic lane (55.0%). Whether the rider was a BFDR or private rider had little influence on red light running. The results suggest that BFDRs are not more likely to perform the risky behaviours examined, but that other factors such as bicycle type, gender, time of day and infrastructure appear to be more important determinants. However, the differences among companies suggest that organisational factors deserve further investigation.

Short-Term Prediction of Bike-Sharing Demand Using Multi-Source Data: A Spatial-Temporal Graph Attentional LSTM Approach

As a convenient, economical, and eco-friendly travel mode, bike-sharing greatly improved urban mobility. However, it is often very difficult to achieve a balanced utilization of shared bikes due to the asymmetric spatio-temporal user demand distribution and the insufficient numbers of shared bikes, docks, or parking areas. If we can predict the short-run bike-sharing demand, it will help operating agencies rebalance bike-sharing systems in a timely and efficient way. Compared to the statistical methods, deep learning methods can automatically learn the relationship between the inputs and outputs, requiring less assumptions and achieving higher accuracy. This study proposes a Spatial-Temporal Graph Attentional Long Short-Term Memory (STGA-LSTM) neural network framework to predict short-run bike-sharing demand at a station level using multi-source data sets. These data sets include historical bike-sharing trip data, historical weather data, users’ personal information, and land-use data. The proposed model can extract spatio-temporal information of bike-sharing systems and predict the short-term bike-sharing rental and return demand. We use a Graph Convolutional Network (GCN) to mine spatial information and adopt a Long Short-Term Memory (LSTM) network to mine temporal information. The attention mechanism is focused on both temporal and spatial dimensions to enhance the ability of learning temporal information in LSTM and spatial information in GCN. Results indicate that the proposed model is the most accurate compared with several baseline models, the attention mechanism can help improve the model performance, and models that include exogenous variables perform better than the models that only consider historical trip data. The proposed short-term prediction model can be used to help bike-sharing users better choose routes and to help operators implement dynamic redistribution strategies.