To study the speed choice and mental workload of elderly cyclists on electrical assisted bicycles (e-bikes) in simple and complex traffic situations compared to these on conventional bicycles, a field experiment was conducted using two instrumented bicycles. These bicycles were identical except for the electric pedal support system. Two groups were compared: elderly cyclists (65 years of age and older) and a reference group of cyclists in middle adulthood (between 30 and 45 years of age). Participants rode a fixed route with a length of approximately 3.5 km on both bicycles in counterbalanced order. The route consisted of secluded bicycle paths and roads in a residential area where cyclist have to share the road with motorized traffic. The straight sections on secluded bicycle paths were classified as simple traffic situations and the intersections in the residential area where participants had to turn left, as complex traffic situations. Speed and mental workload were measured. For the assessment of mental workload the peripheral detection task (PDT) was applied. In simple traffic situations the elderly cyclists rode an average 3.6 km/h faster on the e-bike than on the conventional bicycle. However, in complex traffic situations they rode an average only 1.7 km/h faster on the e-bike than on the conventional bicycle. Except for the fact that the cyclists in middle adulthood rode an average approximately 2.6 km/h faster on both bicycle types and in both traffic conditions, their speed patterns were very similar. The speed of the elderly cyclists on an e-bike was approximately the speed of the cyclists in middle adulthood on a conventional bicycle. For the elderly cyclist and the cyclists in middle adulthood, mental workload did not differ between bicycle type. For both groups, the mental workload was higher in complex traffic situations than in simple traffic situations. Mental workload of the elderly cyclists was somewhat higher than the mental workload of the cyclists in middle adulthood. The relatively high speed of the elderly cyclists on e-bikes in complex traffic situations and their relatively high mental workload in these situations may increase the accident risk of elderly cyclist when they ride on an e-bike.

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.

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This paper discusses a naturalistic driving study on the use of mobile phones and navigation systems while driving. Manual interactions with these devices while driving can cause distraction from the driving task and reduce traffic safety. In this study 21 subjects were observed for 5 weeks. Their behavior was logged by four cameras, a Global Positioning System sensor, and a number of additional sensors. Results are presented on the durations and frequencies of manual interactions with the mobile phone and navigation system. Different manual subtasks with different levels of visual interaction are discussed, as well as how these interactions relate to driving speed. Results show that participants spent on average 1% of their driving time interacting with the navigation system and 4% of their driving time interacting with the mobile phone, excluding mobile phone conversations. For the mobile phone, 48% of the interactions took longer than 15 s; for the navigation system it was 40%. The average duration of a visual–manual subtask for the navigation system and the mobile phone was not significantly different when the task was performed while driving or while the vehicle was standing still. Exploratory analysis of vehicle speed suggests that drivers do not seem to adjust their speed while performing a visual–manual task. The results are discussed in relation to traffic safety and recommendations for future research.