Automated vehicles (AVs) may feature blinded (i.e. blacked-out) windows and external human–machine interfaces (eHMIs), and the driver may be inattentive or absent, but how these features affect cyclists is unknown. In a crowdsourcing study, participants viewed images of approaching vehicles from a cyclist's perspective and decided whether to brake. The images depicted different combinations of traditional vehicles versus AVs, eHMI presence, vehicle approach direction, driver visibility/window-blinding, visual complexity of the surroundings, and distance to the cyclist (urgency). The results showed that the eHMI and urgency level had a strong impact on crossing decisions, whereas visual complexity had no significant influence. Blinded windows caused participants to brake for the traditional vehicle. A second crowdsourcing experiment aimed to clarify the findings of Experiment 1 by also requiring participants to detect the vehicle features. It was found that the eHMI ‘GO’ and blinded windows yielded high detection rates and that driver eye contact caused participants to continue pedalling. To conclude, blinded windows increase the probability that cyclists brake, and driver eye contact stimulates cyclists to continue cycling. Our findings, which were obtained with large international samples, may help elucidate how AVs (in which the driver may not be visible) affect cyclists’ behaviour.

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The authors regret that a mistake was made in Table 3 of the article. This table should read: [Table presented] The figures in bold, italic and red differ from the figures in the published article. As a result of these different values, the sixth sentence of the abstract should read: “S-pedelecs were much faster than conventional bicycles, amounting to a speed difference with conventional bicycles of 9.6 km/h in urban areas (M = 26.9 km/h vs. 17.3 km/h) and of 13.1 km/h in rural areas (M = 31.4 km/h vs. 18.3 km/h).” and the seventh sentence of the abstract should read: “The speed differences between pedelecs and conventional bicycles were much smaller: 2.8 km/h in urban areas (20.1 km/h vs 17.3 km/h) and 3.9 km/h in rural areas (22.2 km/h vs. 18.3 km/h). In the Discussion, Section 4.1. Comparison with previous studies on cycling speed characteristics, the second sentence of the second section should read: “Our study found similar patterns among Dutch riders. S-pedelecs were much faster than conventional bicycles, amounting to a speed difference in mean speeds in urban areas of 9.6 km/h (M = 26.9 km/h vs. 17.3 km/h) and in rural areas of 13.1 km/h (M = 31.4 km/h vs. 18.3 km/h). Values different from those in the published text are in bold, italic, and red. The authors would like to apologise for any inconvenience caused.

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To assess the potential impact of the higher speeds of pedal-assisted bicycles on safety, this study compared conventional bicycles, pedelecs and speed pedelecs (hereafter called s-pedelecs) on mean speeds, speed variability, harsh braking events (decelerations > 2 m/s), and mean speeds above the speed limit (MSAL) in rural and urban areas in the Netherlands Data were collected in daily traffic, while the legal maximum speed for speed-pedelecs was 25 km/h, and pedelecs and s-pedelecs shared the infrastructure with conventional bicycles. Data were collected, using two-wheelers equipped with accelerometers and GPS. Personality factors – sensation seeking and risk taking - were measured with surveys. Regular commuters used one of the three bicycle types for two weeks. Participant bias was intentionally included by allowing participants to select a bicycle type of their preference, resulting in 12 conventional bicycle riders (71 % women), 14 pedelec riders (67 % women) and 20 s-pedelec riders (25 % women). S-pedelecs were much faster than conventional bicycles, amounting to a speed difference with conventional bicycles of 10.4 km/h in urban areas (M =28.2 km/h vs. 17.8 km/h) and of 13.2 km/h in rural areas (M = 31.4 km/h vs. 18.2 km/h). The speed differences between pedelecs and conventional bicycles were much smaller: 2.3 km/h in urban areas (20.1 km/h vs 17.8 km/h) and 4 km/h in rural areas (22.2 km/h vs. 18.2 km/h). Compared to conventional bicycles, s-pedelecs varied their speed to a greater extent and also braked harshly more frequently, showing a greater need for speed adjustment. These adjustments were larger at higher speeds. In contrast, pedelecs did not differ from conventional bicycles on speed variation. MSAL for s-pedelec riders differed by gender. For men the MSAL was 87 % on urban sections and 91 % on rural sections. For women, the MSAL was lower, respectively 23 and 69 %. None of the personality factors were associated with speed variability, harsh braking or MSAL. However, sensation seeking was associated with higher mean speeds on all three bicycle types. To conclude, pedelecs and conventional bicycles are similar in speed patterns, whereas the speed patterns of s-pedelecs differ significantly from the former two. The safety implications are discussed.

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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.

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What will cyclists do in future conflict situations with automated cars at intersections when the cyclist has the right of way? In order to explore this, short high-quality animation videos of conflicts between a car and a cyclist at five different intersections were developed. These videos were ‘shot’ from the perspective of the cyclist and ended when a collision was imminent should the car or the bicyclist not slow down. After each video participants indicated whether they would slow down or continue cycling, how confident they were about this decision, what they thought the car would do, and how confident they were about what the car would do. The appearance of the approaching car was varied as within-subjects variable with 3 levels (Car type): automated car, automated car displaying its intentions to the cyclists, and traditional car. In all situations the cyclist had right of way. Of each conflict, three versions were made that differed in the moment that the video ended by cutting off fractions from the longest version, thus creating videos with an early, mid, and late moment for the cyclist to decide to continue cycling or to slow down (Decision moment). Before the video experiment started the participants watched an introductory video about automated vehicles that served as prime. This video was either positive, negative, or neutral about automated vehicles (Prime type). Both Decision moment and Prime type were between subject variables. After the experiment participants completed a short questionnaire about trust in technology and trust in automated vehicles. 1009 participants divided in nine groups (one per Decision moment and Prime) completed the online experiment in which they watched fifteen videos (5 conflicts × 3 car types). The results show that participants more often yielded when the approaching car was an automated car than when it was a traditional car. However, when the approaching car was an automated car that could communicate its intentions, they yielded less often than for a traditional car. The earlier the Decision moment, the more often participants yielded but this increase in yielding did not differ between the three car types. Participants yielded more often for automated cars (both types) after they watched the negative prime video before the experiment than when they watched the positive video. The less participants trusted technology, and the capabilities of automated vehicles in particular, the more they were inclined to slow down in the conflict situations with automated cars. The association between trust and yielding was stronger for trust in the capabilities of automated vehicles than for trust in technology in general.

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Hazard anticipation skills are crucial for safe performance in traffic. Most hazard anticipation training programs have been developed for car drivers [1]. Traffic situations used in these programs may not be appropriate for cyclists because cyclists are subjected to specific types of hazards. In line with research evidence that hazard anticipation skills are suboptimal even in experienced road users, we aimed to develop and evaluate PC-based hazard anticipation training program for experienced adult cyclists. The first evaluation of this training program was conducted among electric bicycle users who seem to be more likely to be involved in a crash that requires treatment at an emergency department than people riding a conventional bicycle [2]. Sixty-six participants, randomly sampled into training and control groups (mean age of 58 and 57 years, respectively), completed either a PC-based training or a placebo intervention. The training intervention consisted of two modules: instructions and practice. In each module, participants saw video-clips of seven hazardous traffic situations. The screen was set to black just before a hazard developed, and participants had to choose one of the four responses to two questions: “What is the location of the hazard” and “What happens next?”. The placebo intervention consisted of the video-clips as well, but participants were asked questions about traffic rules and behaviors of other road users. Immediately after the training and placebo interventions, two video-based hazard anticipation tests were administered to the participants. During the first test participants saw video clips of hazardous intersection scenarios and were asked questions about (1) the prediction of the driver’s behavior, (2) the cyclist’s (own) slowing down behavior, and (3) perceived risk. In the second test, we measured hazard perception response time while participants watched video-clips in which hazards gradually developed. Participants were asked to press the space bar to indicate that the hazard was detected. After each video-clip, participants answered a question about how dangerous the viewed situation was. Results showed that participants assigned to the training group were more accurate in predicting what a car driver will do next in hazardous situations at intersections, and they reported more frequently that they would slow down in these situations compared to the control group. Cyclists who completed the training program outperformed cyclists in the control group regarding hazard perception response time (t (64) = 3.028, d = 0.745, p = 0.004). No significant differences between two groups were observed in perceived riskiness and dangerousness of the hazardous situations. Our preliminary results suggest that accuracy of predicting driver behavior at intersections as well as hazard perception response time among electric bicycle users could be improved with the developed hazard anticipation training program. @en

Listening to music while cycling impairs cyclists’ auditory perception and may decrease their awareness of approaching vehicles. If the impaired auditory perception is not compensated by the cyclist himself or other road users involved, crashes may occur. The first aim of this study was to investigate in real traffic whether teenage cyclists (aged 16–18) compensate for listening to music by increasing their visual performance. Research in real traffic may pose a risk for participants. Although no standard ethical codes exist for road safety research, we took a number of ethical considerations into account to protect participants. Our second aim was to present this study as a case study demonstrating ethical dilemmas related to performing research in real traffic. The third aim was to examine to what extent the applied experimental set-up is suitable to examine bicyclists’ visual behaviour in situations crucial for their safety. Semi-naturalistic data was gathered. Participants’ eye movements were recorded by a head-mounted eye-tracker during two of their regular trips in urban environments. During one of the trips, cyclists were listening to music (music condition); during the other trip they were ‘just’ cycling (the baseline condition). As for cyclists’ visual behaviour, overall results show that it was not affected by listening to music. Descriptive statistics showed that 21–36% of participants increased their visual performance in the music condition, while 43–64% decreased their visual performance while listening to music. Due to ethical considerations, the study was therefore terminated after fourteen cyclists had participated. Potential implications of these results for cycling safety and cycling safety research are discussed. The methodology used in this study did not allow us to investigate cyclists’ behaviour in demanding traffic environment. However, for now, no other research method seems suitable to address this research gap.

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