Research shows that the ability to anticipate safety-critical situations is predictive of safe performance in traffic. Thus far, hazard anticipation training has been developed mainly for car drivers. These training programs may not be appropriate for cyclists who are exposed to different types of hazards. This study aimed to develop a PC-based hazard anticipation training for experienced cyclists, and evaluate its short-term effectiveness using hazard anticipation tests. Sixty-six electric bicycle users completed either a hazard anticipation training or a control intervention. The hazard anticipation training consisted of videos divided into two modules (instructions and practice) and was designed using various evidence-based hazard anticipation educational methods such as a ‘What happens next?’ task, expert commentary, performance feedback, and analogical transfer between hazardous traffic situations. The evaluation of the training showed that cyclists from the training group identified hazards faster compared to the control group cyclists, but no significant difference was found in the number of detected hazards between the two groups. The training had a small positive effect on cyclists’ prediction accuracy at safety-critical intersection situations. No effect was found on perceived danger and risk in hazardous traffic situations. Our results suggest that experienced cyclists’ hazard anticipation skills can be improved with the developed PC-based training. Future research should evaluate the retention and transfer of learned skills.

Objective: It is well established within the traffic psychology literature that a distinction can be made between driving skill and driving style. The majority of self-report questionnaires have been developed for car drivers, whereas only limited knowledge exists on the riding skill and style of cyclists. Individual differences in cycling skills need to be understood in order to apply targeted interventions. Methods: This study reports on a psychometric analysis of the Cycling Skill Inventory (CSI), a self-report questionnaire that asks cyclists to rate themselves from definitely weak to definitely strong on 17 items. Herein, we administered the CSI using an online crowdsourcing method, complemented with respondents who answered the questionnaire using paper and pencil (n = 1,138 in total). Our analysis focuses on understanding the major sources of variance of the CSI and its correlates with gender, age, exposure, and self-reported accident involvement as a cyclist. Results: The results showed that 2 components underlie the item data: Motor–tactical skills and safety motives. Correlational analyses indicated that participants with a higher safety motives score were involved in fewer self-reported cycling accidents in the past 3 years. The analysis also confirmed well-established gender differences, with male cyclists having lower safety motives but higher motor–tactical skills than female cyclists. Conclusions: The nomological network of the CSI for cyclists is similar to that of the Driving Skill Inventory for car drivers. Safety motives are a predictor of self-reported accident involvement among cyclists.

Introduction: Many bicycle–car crashes are caused by the fact that the driver fails to give right of way to the cyclist. Although the car driver is to blame, the cyclist may have been able to prevent the crash by anticipating the safety-critical event and slowing-down. This study aimed to understand how accurate cyclists are in predicting a driver's right-of-way violation, which cues contribute to cyclists' predictions, and which factors contribute to their self-reported slowing-down behavior as a function of the temporal proximity to the conflict. Method: 1030 participants were presented with video clips of nine safety-critical intersection situations, with five different video freezing moments in a between-subjects design. After each video clip, participants completed a questionnaire to indicate what the car driver will do next, which bottom-up and top-down cues they think they used, as well as their intended slowing-down behavior and perceived risk. Results and conclusions: The results showed that participants' predictions of the driver's behavior develop over time, with more accurate predictions (i.e., reporting that the driver will not let the cyclist cross first) at later freezing moments. A regression analysis showed that perceived high speed and acceleration of the car were associated with correctly predicting that the driver will not let the cyclist cross first. Incorrect predictions were associated with believing that the car has a low speed or is decelerating, and with reporting that the cyclist has right of way. Correctly predicting that the driver will not let the cyclist cross first and perceived risk were significant predictors of intending to slow down in safety-critical intersection situations. Practical applications: Our findings add to the existing knowledge on cyclists' hazard anticipation and could be used for the development of training programs as well as for cycling support systems.

This study investigated cycling performance of middle-aged (30–45 years old; n = 30) versus older (65+ years; n = 31) participants during low-speed tasks for which stabilization skills are known to be important. Additionally, participants’ self-ratings of their cycling skills and performance were assessed. Participants rode once on a conventional bicycle and once on a pedelec, in counterbalanced order. Three standardized tasks were performed: (1) low-speed cycling, (2) acceleration from a standstill, and (3) shoulder check. During Tasks 1 and 3, the mean absolute steering angle (a measure of the cyclist's steering activity) and the mean absolute roll rate (a measure of the amount of angular movement of the frame) were significantly greater for older participants than for middle-aged participants. These large lateral motions among older cyclists may indicate a difficulty to control the inherently unstable system. Comparing the conventional bicycle and the pedelec, participants reached a 16 km/h threshold speed in Task 2 sooner on the pedelec, an effect that was most pronounced among the older participants. Correlations between skills assessed with the Cycling Skill Inventory and actual measures of cycling performance were mostly not statistically significant. This indicates that self-reported motor-tactical and safety skills are not strongly predictive of measures of actual cycling performance. Our findings add to the existing knowledge on self-assessment of cycling skills, and suggest that age-related changes in psychomotor and sensory functions pose hazards for cycling safety.