Are Latin American cycling commuters "at risk"? A comparative study on cycling patterns, behaviors, and crashes with non-commuter cyclists

Sensation seeking and crashes among young cyclists

While young cyclists remain overrepresented in cycling crash figures, effective actions to mitigate their risks remain understudied and underapplied, especially in regions with low cycling tradition and weak or fragmented governance, as is the case in most Hispanic countries. One key emerging issue is the potential influence of personality traits such as sensation seeking (SS) on young cyclists' behavior and safety outcomes. This study aimed to assess the relationships among SS, cycling behavior, and safety-related outcomes among a sample of young cyclists. Data were collected from 945 cyclists aged 18-25 from five Hispanic countries, who responded to an electronic survey on personality traits and cycling-related topics. Significant associations were found between sensation seeking and risk-related cycling behaviors, as well as gender differences in SS, risky cycling behavior, and self-reported cycling crash rates, with males exhibiting higher values in all categories. Path analyses suggest that SS predicts self-reported crashes through the full mediation of both deliberate (traffic violations) and unintentional (errors) risky road behaviors, with the former having a greater explanatory effect on young cyclists' self-reported crash figures. The findings of this study highlight the need to address under-researched issues such as sensation seeking (SS) and risk-taking behavior through evidence-based interventions aimed at improving the safety of young cyclists. This is particularly relevant in countries with similar demographic characteristics and further nascent cycling cultures.

The hidden cost of your 'too fast food': stress-related factors and fatigue predict food delivery riders' occupational crashes

Objectives. For several years, the so-called 'gig economy' has kept transforming urban transportation dynamics. However, the literature has often overlooked the demanding, stressful and safety-compromising conditions under which delivery riders carry out their occupational tasks. This research aimed to examine whether fatigue acts as a mediating mechanism in the complex relationships among job settings, stress-related psychosocial factors at work and the occurrence of occupational traffic crashes among two-wheeled food delivery riders. Methods. This cross-sectional study field-surveyed 248 food delivery riders operating across various platforms. Participants responded to a questionnaire on work features, psychosocial factors and occupational safety issues. The data underwent both descriptive analyses and structural equation modeling (SEM). Results. As hypothesized, the occupational (riding) crashes of food delivery riders can be largely explained through work-related fatigue, which exerts a full mediation between job settings, stress-related factors and riding safety outcomes. Conclusions. These results highlight fatigue as a significant yet overlooked threat in this occupation, emphasizing the need to connect stress-related conditions with safety incidents, a relationship not previously explored among delivery riders. Moreover, our findings stress the necessity for policies and interventions targeting stress and fatigue management to improve occupational health and road safety in the gig economy era.

Analyzing Safety Concerns of (e-) Bikes and Cycling Behaviors at Intersections in Urban Area

Extensive effort has been devoted to examining the causal relationship between contributing factors and injury severities. Given the important role of riders’ behaviors in traffic conflicts, this paper aims to analyze the causal effects of traffic conflicts resulting from riders’ behaviors at intersections. The authors collected video data on 152 traffic conflicts caused by riders’ dangerous behaviors in Jiangning District, China. This paper proposes a Bayesian-structural equation modeling (BSEM) approach. Based on the obtained BSEM path coefficient diagram, the factor loadings and path coefficients are analyzed to unveil the potential influence of factors, including personal features, dangerous behavior tendency, temporal and spatial characteristics of dangerous behavior, and the external environment. The results show that compared to human factors, environmental factors have a less direct impact on the severity of traffic conflicts; instead, they have an indirect positive impact on traffic conflicts by affecting behaviors. That is, if riders judge that road conditions are not suitable to conduct dangerous behaviors, they become more cautious in view of current road conditions and time revenue. Furthermore, dangerous cycling behaviors that continue to encroach on the time and space of motorized vehicles are prone to be more dangerous. The dangerous behaviors that continuously encroach on the time and space of motor vehicles (e.g., disobeying traffic signals and riding in a motorway) are significant predictors of serious conflicts. Considering the heterogeneity of riding behavior, these findings could be applied to develop effective education and intervention programs for preventing riders’ high-risk behaviors and improving the traffic environment.

To what extent do our walking and cycling behaviours relate to each other, and do we cycle as well as we think we do? Piloting the walking and cycling behaviour questionnaires in the UK

Greater uptake of active transport has been argued as necessary for the transport system to achieve relevant sustainability and public health goals; however, the research tools used to investigate behaviour when using these modes are far less well-developed than those used to investigate driving behaviour. This study takes two self-report behavioural measures, the Walking Behaviour Questionnaire (WBQ) and the Cycling Behaviour Questionnaire (WBQ), and pilots them in the UK. Exploratory and confirmatory factor analyses with data from 428 respondents revealed factor structures different to those described in the limited number of previous studies that used the CBQ and WBQ. Across both questionnaires, scales measuring intentional behaviour differed from original descriptions to a greater extent than did the scale concerning unintentional attention or memory errors. In addition to a validation exercise, this research explored the relationships between variables, finding a correlation between the reported performance of unintentional errors when walking and cycling. Looking in more detail at cycling behaviours, we found that those who rated themselves as more proficient cyclists also reported performing fewer unintentional cycling errors. Results also showed self-reported helmet use to bear little to no relationship with other self-reported cycling behaviours or self-rated cycling proficiency. Finally, using structural equation modelling, we demonstrated that responses to the CBQ add very little (over and above age, gender, and exposure to the road environment) to the explanation of self-reported past collision involvement. In total, only 7% of the variation in past collision involvement was explained by the included variables. We urge caution when using self-report behavioural measures that have not been validated in the context of intended use, and the importance of using such measures in combination with other approaches rather than in isolation when trying to develop an understanding of overall system performance.

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.

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

Cyclists are vulnerable road users and face disproportionately high rates of road trauma, especially in low- and middle-income countries. Behaviour of road users is a system outcome, and thus studying cyclist behaviours can identify problems in the whole road transport system and assist in generating long-lasting, cost-effective solutions to promote cyclist safety. This study aims to investigate the similarities and disparities of cycling behaviour among countries with different income levels and cycling prevalence, and the relationships among cyclist demographic characteristics, behaviours and crash involvement. It applies the Cycling Behaviour Questionnaire (CBQ) to measure the behaviour of cyclists in three countries: Australia (high-income, emerging cycling country), China (mid-income, traditional cycling country) and Colombia (low-income, emerging cycling country). A total of 1094 cyclists (Australia 347, China 368, and Colombia 379) with a mean age of 31.8 years completed the online questionnaire. Confirmatory Factor Analysis was used to examine the factorial structure of the CBQ. The results showed that the three-factor CBQ (i.e., Traffic Violations, Errors and Positive Behaviours) had a stable and uniform factorial structure across all three countries. Cyclists reported more frequent positive behaviours than risky behaviours across all three countries. The Australian cyclists reported more positive behaviours and fewer violations than cyclists from the other countries, which is likely to reflect the road environment, policy and enforcement differences. Male cyclists tended to engage in more risky riding than females and reported more crash involvement. Older cyclists reported less risky riding and less involvement in crashes than younger cyclists. Cyclists who rode more often reported more frequent risky riding. The study helps understand the prevalence of risky/positive cyclist behaviours among countries and provides insights for developing systematic countermeasures to improve cycling environment and cyclist safety.

Bicycle Rider Behavior and Crash Involvement in Australia

This research investigated how behaviours and attitudes of bicycle riders influence crash frequency and severity. The study recruited 1102 Australian bicycle riders for an online survey. The survey comprised questions on demographics, frequency of riding and the number and severity of traffic crashes during the last five years. The survey included the Cycling Behaviour Questionnaire and the Cyclist Risk Perception and Regulation Scale. Overall, there were low levels of errors and violations reported by participants indicating that these behaviours were on average never or rarely exhibited while riding a bicycle. Conversely, participants reported high levels of engagement in positive behaviours and reported high levels of traffic rule knowledge and risk perception. Higher rates of violations and errors were associated with increased crash likelihood, while higher rates of positive behaviours were associated with reduced rates of crash involvement in a period of 5 years. The findings highlight the relationship between errors, total crashes and crash severity Further promotion of positive behaviours amongst riders may also help to reduce the risk of crashes.

“Not as Safe as I Believed”: Differences in Perceived and Self-Reported Cycling Behavior between Riders and Non-Riders

Cycling behavior remains a key issue for explaining several traffic causalities occurring every day. However, recent studies have shown how the assessment of the own safety-related behaviors on the road may substantially differ from how third parties assess them. Thus, the aim of this study was to evaluate the differences between cyclists’ self-reported behavior and the proxy-reported behavior that other (non-cyclist) road users perceive from bike riders. For this purpose, this study used data from two samples: (i) 1064 cyclists (M = 32.83 years) answering the Cycling Behavior Questionnaire—CBQ, and (ii) 1070 non-cyclists (M = 30.83 years) answering an adapted version of the CBQ for external raters—ECBQ. The results show how the self-reported and proxy-reported behaviors of cyclists greatly differ in terms of all behavioral factors composing the CBQ model, i.e., traffic violations, riding errors, and positive behaviors. Also, external raters (non-cyclists) are those targeting significantly riskier behaviors than those self-reported by cyclists. These discrepancies between perceived behaviors may give rise to conflicting viewpoints on the interaction between bicycle riders and other road users. Therefore, this study underscores the importance of behavioral awareness, providing highlights for future studies on the behavioral interaction between cyclists and other road users. Results can be used to improve the road safety of all road users by giving indications on self-and proxy-perceived safety-related behaviors and visibility of protective riding habits.