Secondary task engagement in German cyclists – An observational study

Safety Science 120 (2019) 290–298

Contents lists available at ScienceDirect

Safety Science journal homepage: www.elsevier.com/locate/safety

Secondary task engagement in German cyclists – An observational study ⁎

T

Anja Katharina Huemer , Selvi Gercek, Mark Vollrath Technische Universität Braunschweig, Institut für Psychologie, Ingenieur- und Verkehrspsychologie, Gaußstraße 23, 38106 Braunschweig, Germany

A R T I C LE I N FO

A B S T R A C T

Keywords: Cycling Secondary task Headphones/earphones Mobile phone use Observational study Situational influences Cyclists’ characteristics

Bicycling in traffic requires continuous attention to be paid to one’s environment. In addition, motor coordination is needed to safely handle a bicycle. Accordingly, distracted cycling has been demonstrated to impair cycling performance (De Waard et al., 2015). We conducted an observational study of cycling behavior in Braunschweig, Germany, in which we observed 2187 cyclists. Overall, 22.7% (95% CI: 20.9–24.4%) of all cyclists were engaged in any secondary task, with wearing headphones or earphones being the most frequent behavior (13.1%, 95% CI: 11.7–14.5%), followed by interactions with other cyclists (7.0%, 95% CI: 5.9–8.0%). Mobile phones were used by 2.0% of all cyclists (95% CI: 1.4–2.6%), with most of them (1.5%, 95% CI: 1.0–2.0%) conversing on their phones. Secondary tasks were more frequent in the morning, and mobile phone use was less frequent in bad weather. Females and young cyclists were more frequently engaged in a secondary task than males and older cyclists. Being engaged in a secondary task was also shown to correlate with less frequent helmet use. Engagement in secondary tasks, especially using the smartphone and wearing headphones or earphones was more often found in cyclists riding hands-free. Overall, these frequencies are in accordance with the findings of the limited number of studies that have been conducted. When riding a bike, acoustic impairment from wearing headphones or earphones seems to be the major problem. The correlation with other safety precautions like not wearing a helmet indicates that this might be due to a lack of awareness concerning the possible dangers of these behaviors.

1. Introduction Distraction seems to become one of the major causes of traffic accidents. Singh (2015) reported for the US that recognition error, which included driver’s inattention, internal and external distractions, and inadequate surveillance, was the most frequently assigned critical reason, linked to 41% of crashes. In Austria and Switzerland, “driver distraction or inattention” is recorded as a causing factor in traffic crashes by the police. In Austria distraction was the most common cause for traffic crashes, assigned to 38% of crashes with injured or killed people in 2016 (Statistik Austria, 2017). However, statistics here do not allow for vehicle-specific analyses. In Switzerland this is possible. While here 21.7% of all parties involved in crashes with injured or killed people were found to be inattentive or distracted, in cyclists’ crashes, distraction or inattention was found in 19.1% (Bundesamt für Statistik, 2019). There is still a discussion about which kinds of distraction increase the risk of a crash. While the first case-control studies of cars seemed to indicate that talking on the phone increases accident risk (Redelmeier and Tibshirani, 1997), more recent studies even indicate a reduction of crash risk when driving and phoning (see Victor et al., 2014; Dingus ⁎

et al., 2016, and Young, 2017; for differing views on the SHRP2 naturalistic driving data; https://insight.shrp2nds.us/). However, it is pretty clear that texting on the phone increases crash risk in car drivers (Dingus et al., 2016; Young, 2017). Meta-analyses on experimental studies of secondary tasks while car driving generally find negative effects on driving performance (e.g. Caird et al., 2014; Ferdinand and Menachemi, 2014; OviedoTrespalacios et al., 2016; Papantoniou et al., 2017; Vollrath et al., 2014). Compensatory behavior of drivers is discussed to be mediating the detrimental effects of secondary tasks on crash risk. This can be done by (a) only engaging in secondary tasks in relatively low risk driving situations (e.g. Huth et al., 2015; Metz et al., 2015) and (b) by driving within larger safety margins (e.g. slower and with a longer headway; e.g., Fitch et al., 2014; Oviedo-Trespalacios et al., 2017) while engaging in secondary tasks. However, in order to estimate crash risk due to distraction and to discuss the scope of the problem, information about the prevalence of different kinds of distraction while driving without a crash is required. Only if this information is collected and matched to the prevalence of distraction in crashes, a crash risk can be calculated. Additionally, analyzing the frequency and circumstances (e.g. only when stopping at

Corresponding author. E-mail addresses: [email protected] (A.K. Huemer), [email protected] (S. Gercek), [email protected] (M. Vollrath).

https://doi.org/10.1016/j.ssci.2019.07.016 Received 28 August 2018; Received in revised form 24 May 2019; Accepted 11 July 2019 0925-7535/ © 2019 Elsevier Ltd. All rights reserved.

Safety Science 120 (2019) 290–298

A.K. Huemer, et al.

occupation was higher than in the German or Italian sample: in teens and young adults, up to 77% of participants reported using devices for phoning, texting and listening to music, but only 20% of middle-aged and older participants did. In demanding situations, the older the participant, the more likely that he or she was to refrain from listening to music (Stelling-Kończak et al., 2017). Chataway et al. (2014) compared perceptions and behaviors of cyclists in Copenhagen, Denmark and Brisbane, Australia. They found reported distracted cycling to be more common in male and more experienced cyclists and less common in older cyclists and parents. Cyclists in Copenhagen reported a higher rate of distracted cycling. The use of safety gear (wearing a helmet, using reflectors and bicycle lights) was negatively correlated with distracted cycling. To the best of our knowledge, the prevalence of secondary tasks while cycling has been examined by observational studies only five times: three times in the Netherlands (De Waard et al., 2010; De Waard et al., 2015; Terzano, 2013) and twice in the United States (Ethan et al., 2016; Wolfe et al., 2016). In the Netherlands, where using your phone when cycling is not forbidden, 2.8% (De Waard et al., 2010) to 3.5% (Terzano, 2013) of cyclists were found to use their phone while cycling, with increasing rates of texting over time (0.6% in 2008 to 2.3% in 2013; De Waard et al., 2015). Wearing headphones was found between 7.7% (De Waard et al., 2010) and 9.1% (Terzano, 2013), talking to other cyclists and pedestrians between 2.3% (De Waard et al., 2010) and 14.0% (Terzano, 2013). The authors also found that cyclists wearing earphones ran red lights more frequently (De Waard et al., 2010) and distracted cyclists to engage in more unsafe behaviors in general (Terzano, 2013). Even though the Groningen observations have been conducted at different locations (De Waard et al., 2010; De Waard et al., 2015), no analyses were conducted on location-specific secondary task performance rates. Ethan et al. (2016) observed cyclists on bike lanes at five different locations in popular cycling areas of Manhattan, New York City. Males and users of the Citi Bike bicycle-sharing program were found to more often wear headphones as well as to engage in more unsafe behavior. The authors also found wearing headphones and exhibiting more than one unsafe cycling behavior to be more prevalent during the recreational hours of weekdays and the weekend when compared to the morning commute. Locations differed significantly in rates of wearing headphones or earphones and talking on phones. Wolfe et al. (2016) found 31.2% of cyclists crossing four intersections with high bicycle traffic and collisions in Boston, United States, to be engaged in secondary tasks: 17.7% were found to be visibly wearing headphones or earphones, and 13.5% were visually engaged in secondary tasks by using mobile phones in their hands or mounted on handlebars. Here, cyclists were more often engaged in secondary tasks during midmorning and midday times than during rush hour, as had been expected.

red traffic lights) of distracted mobility can also be used to examine if and to what extent the compensatory behavior of people in traffic exists and to find out if certain sub-groups of them are especially prone to be distracted. 1.1. Secondary task engagement while and cycling Other than for car driving (e.g.; Caird et al., 2014; Ferdinand and Menachemi, 2014; Lipovac et al., 2017, Oviedo-Trespalacios et al., 2016; Papantoniou et al., 2017; Vollrath et al., 2014), international data on secondary task engagement and influencing factors (e.g. Huemer et al., 2018) while cycling are relatively scarce (Mwakalonge et al., 2014; Stelling-Kończak et al., 2015; Stavrinos et al., 2018). In the three crash analyses that have investigated secondary tasks as contributing factors in cyclist crashes, it was found that engagement in secondary tasks contributed to crashes in 11% of minor crashes involving commuter cyclists (de Geus et al., 2012) and in 29% of those involving commuting adolescents (Vanparijs et al., 2016) in Belgium. For cyclists older than 50 years of age in the Netherlands, Boele-Vos et al. (2017) found distraction to be a contributing factor in at least 12% of crashes. In this study, secondary task related crashes were most common among males between 50 and 74 years of age who “veer off course and then collide with oncoming traffic or run off the road” (p. 9). In the aforementioned Swiss traffic crash statistics for 2017 (Bundesamt für Statistik, 2019), 518 of 705 (73.5%) cyclists’ crashes that were found to be influenced by inattention or distraction of the cyclists were skid related single crashes. It has been explicitly demonstrated that interactions with electronic devices such as mobile phones and portable music players impair cycling performance (Ahlstrom et al., 2016; De Waard et al., 2010; De Waard et al., 2011; De Waard et al., 2014; Goldenbeld et al., 2012; Stavrinos et al., 2018; Terzano, 2013). Cyclists engaged in secondary tasks are found to exhibit delayed response times and less head movement than cyclists not engaged in secondary tasks (De Waard et al., 2015). With regard to auditory secondary tasks, especially listening to music with headphones or earphones, De Waard et al. (2011) found that cyclists missed many relevant acoustic signals. Specifically, with in-earphones, more than two-thirds of the participants in the authors’ experiment did not hear the sound of a bicycle bell or a honking horn. However, when De Waard et al. (2015) examined the lane positions of cyclists who were engaged in secondary tasks on a variety of infrastructures the authors did not find these cyclists to demonstrate differing behaviors on assorted types of cycling paths. In surveys, researchers found that using a mobile phone or wearing headphones or earphones while cycling enhances the likelihood of being involved in self-reported crashes and near crashes (Goldenbeld et al., 2012; Ichikawa and Nakahara, 2008; Puchades et al., 2018; von Below, 2016), while others did not (Hollingworth et al., 2015; StellingKończak et al., 2017). Useche et al. (2018a) and Useche et al. (2018b) found distractions to significantly predict self-reported crashes through the mediation of risky behaviors. Self-reported prevalence of secondary tasks while cycling seems to differ between countries. In Japan, Ichikawa and Nakahara (2008) found up to 71% of male and 60% of female Japanese high school commuters reported using their phone while cycling. In Europe, the U.K. survey of injured cyclists (Hollingworth et al., 2015) found that only 0.6% of riders reported wearing headphones during a crash. Puchades et al. (2018) found self-reported smartphones use was found to be low among Italian cyclists. Here, usage was negatively correlated with age. In Germany, a representative sample of cyclists found that 11.5% reported making phone calls while cycling and 17.6% reported listening to music. Again, secondary task occupation was negatively correlated with age (Von Below, 2016). In the Netherlands, another two studies (Goldenbeld et al., 2012; Stelling-Kończak et al., 2017) found no gender, but age differences in cyclists’ self-reported use of electronic devices while cycling. In both Dutch Studies, reported secondary task

1.2. Influencing factors for secondary-task prevalence In a recent overview of observational studies, Huemer et al. (2018) reported that, for car drivers, the prevalence of engaging in secondary tasks while driving has been found to differ by gender, age, and ethnic groups in many studies, along with the presence of passengers in the vehicle, day of the week and time of day, and road and vehicle type. Younger (and, in recent years, middle-aged) drivers are generally more often found to engage in secondary tasks while driving. Other than the time-of-day results of Ethan et al. (2016) and Wolfe et al. (2016) and the location dependence of unsafe behaviors and phone use found by Ethan et al. (2016), to our knowledge influencing factors on the prevalence of cycling while engaged in secondary tasks have not yet been examined in observational studies, but found in self-reports (Chataway et al., 2014; Goldenbeld et al., 2012; Puchades et al., 2018; StellingKończak et al., 2017; Von Below, 2016). For car drivers, it has also been found that engagement in engaged 291

Safety Science 120 (2019) 290–298

A.K. Huemer, et al.

1.4. Aims of the present study

in secondary tasks depends on the environmental situation (Metz et al., 2014). Adaptation to environmental influences such as weather conditions and road surfaces may be more important for cyclists than for car drivers, as these changes are perceived more directly on a bicycle than in a vehicle; as such, failure to adapt has a stronger impact on cyclists’ well-being in the case of a crash or a fall. Behavioral adaptation to secondary tasks in cycling has been found by Kircher et al. (2015), who demonstrated that cyclists both slow down before starting selfpaced secondary tasks and adapt to technology-paced tasks while doing them.

For Germany, observational data on the prevalence of cyclists’ secondary-task behavior do not yet exist. Therefore, the first aim of the present study was estimate the observable frequency of different secondary tasks while cycling in Germany. The second aim was to test whether the differences in secondary task engagement that have been found in survey data can also be found in observational data. Therefore, we examined (a) whether the prevalence of different activities depends on cyclists’ characteristics (for example, age and gender); (b) whether cyclists adapt their behavior to the traffic situation (such as cycling path design and traffic density) and environmental conditions (such as weather and lighting conditions); and (c) whether cyclists’ secondary task engagement is related to safety precautions (e.g. wearing a helmet). As the study is explorative, no hypotheses were formulated.

1.3. Cycling in Germany About 78% of all German households possess at least one bicycle. 35% of Germans report cycling at least once a week. The modal split of cycling is about 11%, the typical trip has a distance of 4 km (Mobilität in Deutschland, 2018). The federal government aims to significantly boost cycling modal share (Nationaler Radverkehrsplan, 2020) and reduce road fatalities by 40% in 2020 compared to 2011 (Verkehrssicherheitsprogramm 2011; 2011). Unfortunately, cyclists have a rather high risk of being injured in crashes compared to vehicle drivers (for an international overview, see Chaurand and Delhomme, 2013; for German data, see Schreck, 2016). In 2017, 80,013 severe cycling crashes (those involving injuries or criminal offenses) occurred on German roads, accounting for 0.3% of all police-recorded traffic crashes in Germany. Three hundred and eightytwo cyclists (0.05% of those cyclists who were in a crash) were killed, accounting for 12% of all persons killed in traffic crashes in Germany in 2017 (calculations based on Statistisches Bundesamt, 2018). Ninety-one percent of police-reported severe cycling crashes occurred in urban areas (Statistisches Bundesamt, 2018). With regard to the causes of these accidents, in Germany up to now distractions are not coded as errors in traffic. Urban cycling in Germany for the most part takes place on special infrastructure, such as cycling paths. Such infrastructure is generally found on the sides of streets and is separated from driving lanes by parking lots and/or secondary green areas. Cyclists younger than 8 years old must use pedestrian paths and those older than 10 must stay on the designated cycling infrastructure. Children aged 8 and 9 are allowed to use both. If no such infrastructure is present, cyclists have to use the road. In mainly two instances, use of cycling infrastructure is not mandatory: (a) Some older cycling paths have been mandatory in the past but legal changes made them non-mandatory anymore with the paths still being there. Here, cyclists can choose where to ride. (b) If a cycling path is in bad condition (ice, rugged surface etc.), cyclists do not have to use it and are allowed to use the street. Cycling paths may be directly adjacent to pedestrian paths, separated only by surface colors, or they may be shared paths for both pedestrians and cyclists. Here, cyclists are required to be considerate of pedestrians (§2(4); §2(5) StVO [Straßenverkehrs-Ordnung, 2013, German Traffic Law]; (§2(8.1) VwVStVO [Administrative Regulations for the German Traffic Law]). According to §23(1) StVO, vehicle drivers and riders (including cyclists) must not obstruct their sight or hearing. It is forbidden to have any electronic device used for entertainment, information, or communication in one’s hand(s) or to operate it in any way. The use of speech control is mandatory when using such devices, unless the operation is carried out away from traffic with a few short glances that are adapted to the environmental and traffic conditions. However, knowledge of these rules among cyclists in Germany is generally low. In a survey by Huemer and Eckhardt-Lieberam (2016), only 33% of cyclists knew that they are not allowed to hold their phone while cycling and only 10% knew that they must not block hearing. Therefore, many cyclists may not be aware that device use is forbidden, or believe that the law only applies to car drivers.

2. Material and methods Using a cross-sectional design, observations were made between October 17, 2017, and November 17, 2017, at eight locations on public urban roads (speed limit for motorized traffic: 50 km per hour) within the city of Braunschweig, Germany. The study was conducted in accordance with the German privacy policy and the requirements of the ethics board of the Faculty of Life Sciences of the Technische Universiät Braunschweig. According to the German privacy policy, video recordings of people are not allowed at a resolution high enough that the characteristics we were interested in for this study could be identified. Therefore, direct observation and data entry were used. To ensure that the observation process was as unobtrusive as possible, we used only one observer who positioned herself in a way that she was able to observe the cyclists’ behavior before they pass and thus before they see her observing them. A trained observer used a tablet PC and the configurable software Observation 2.1 (2017) to record the characteristics and behavior of oncoming cyclists. The use of the Observation 2.1 software and one observer has been validated when investigating car driver engagement in secondary tasks (Kathmann et al., 2017; Vollrath et al., 2016). Additionally, these direct observations are widely used in observational research on road users’ secondary-task engagement (see Huemer et al., 2018, for an overview on car drivers’ secondary-task observations). Additionally, the interrater reliability for this observation of engagement in secondary tasks while cycling had been tested in a small pilot study (conducted right before the main observation in similar conditions) that found correlations above 0.9 for all observed variables. 2.1. Dependent variables 2.1.1. Secondary tasks The following secondary tasks could be observed and were recorded (multiple selections were possible):

• Handheld phoning: Cyclists were holding their phones while having conversations on them; • Hands-free phoning: Cyclists were talking on their phones while not • • • • • 292

holding them; hands-free phoning was also assumed when cyclists were talking (not singing) and wearing headphones. Using the smartphone: Cyclists were operating (typing on) their mobile phones; Headphones/earphones: Cyclists were wearing headphones or earphones; Interaction with others: Cyclists were talking to someone else with whom they were cycling together; Eating/drinking/smoking: These activities were recorded separately; Other: Non-cycling activities that do not fall into any of the above

Safety Science 120 (2019) 290–298

A.K. Huemer, et al.

Braunschweig, especially not for on-road cycling facilities or the city center, it is so for commuter cycling. At all locations, despite different path designs, mandatory cycling infrastructure separated from vehicular traffic by a secondary green (a strip of trees and grass alongside the road) is present. Observation locations were situated at a distance of least 50 m from any junctions. The locations differed in the presence of pedestrians on the path that must be used by cyclists (see Fig. 2). At locations 1 to 6, pedestrians and cyclists’ mandatory paths were separated by another strip of secondary green. At location 6, however, there was a bus stop near the cycling path that pedestrians might cross. At location 7, cyclists and pedestrians were required to share a common path, and cyclists have to be particularly careful here by law. At location 8, the mandatory separated paths of pedestrians and cyclists were immediately adjacent to each other, separated only by differing surfaces.

categories. 2.1.2. Safety precautions Safety precautions were recorded in order to examine its relation to secondary-task engagement (multiple selections were possible). These behaviors were as follows:

• Wearing a helmet; • Wearing light-colored or reflective clothing; • Having the bike properly lit; • Keeping at least one hand on the handlebars. 2.2. Predictors In the field study, two types of predictors were used. The first set of predictors (location and cycle path design, as well as time of day and day of the week) was quasi-experimental, as their instances had been actively selected. These are described in Sections 2.2.1 and 2.2.2. The second set of predictors (cyclist gender and age; cycling alone, in a group, or with children; actual path used as well as secondary task engagements and safety precautions) could not be actively selected and was therefore recorded as found (these predictors are described in Section 2.3).

2.2.2. Time of day and day of the week Rush hours were chosen for the observations: in the morning (07:15–11:00), the flow of traffic into the city was observed, and in the afternoon/evening (15:00–18:45), the traffic going to the outskirts was observed. Each location was observed once on a weekday between Tuesday and Thursday and once on a Friday to control for differing traffic situations. 2.3. Observation method and recorded predictors

2.2.1. Location and cycle path design All locations were along a prominent cycling route adjacent to an arterial urban road in the city of Braunschweig in order to ensure sufficient bicycle traffic for an efficient observation. This route is commonly used by commuters (both motor vehicles and cyclists) going in and out of the city center in the morning and afternoon and is used by numerous public bus lines throughout the day. The route is used by many students of the Technische Universität Braunschweig to travel from a dormitory located in the northern outskirts of Braunschweig to the university’s campus in the city center and back (see Fig. 1). While the route may not be representative for all cycling traffic in the city of

In the Observation 2.1 software, there are two screens for data entry. The first one (session screen) was filled in at the beginning of each observation period and contains information on the location (here, only four options are given, as the time of day codes for the direction of travel), cycle path design (three options), traffic flow at the beginning of the observational period (in cyclists per minute; cyclists were counted in the first two minutes of an observational period), observation time (eight options), and day of the week. Weather (four options: sunny, clear, cloudy/foggy, rain) and lighting conditions (three options:

Fig. 1. Locations of observation. Map and picture © Google, 2018. 293

Safety Science 120 (2019) 290–298

A.K. Huemer, et al.

Fig. 2. Photos of the observer’s perspective at the eight locations.

cycling in a group, with a child, or alone as predictors and (1) the presence of any secondary task activity, (2) cycling with headphones or earphones, and (3) cycling while using a mobile phone as criteria. For criterion (4) interaction with others, cycling in a group, with a child, or alone was omitted as a predictor as it is, of course, confounded with the criterion. The relationship between secondary task engagement and safety precautions was examined by chi-squared tests. All of the following analyses are conducted at a significance level of α = 0.05.

daylight, twilight, dark) were also recorded here to analyze their potential influences on secondary-task engagement. If weather and lighting conditions changed during an observation period, a new data recording session was started and the new conditions were entered. On the second screen (case screen), which was filled out for every cyclist observed, his or her characteristics and behaviors are entered by tapping on the corresponding button. Cyclists’ estimated ages were coded in three categories (young, about 18–24 years old; middle aged, about 25–64; and older drivers, from 65 years onwards). Gender was coded as a binary value. It was also observed if cyclists were traveling with a child, in a group, or alone. Cycling with someone was assumed when more than one cyclist was oncoming and cyclists’ speeds were adapted to each other, rather than one overtaking the other. Observed secondary tasks were then coded, followed by safety precautions behavior. For legal reasons, we did not observe cyclists younger than 18 years of age. Therefore, every seemingly adult oncoming cyclist was observed and coded when cycling alone. When cyclists were cycling in a group, as many adults as possible in this groups were observed, with those being observed being selected randomly. Therefore, cyclists in groups are underrepresented in the data.

3. Results Within 42 h of observations, 2,178 adult cyclists were observed. Descriptive statistics of the sample are given in Table 1. 3.1. Prevalence of secondary tasks One thousand six hundred and eighty-four cyclists (77.3%, with 95% confidence interval [CI] of 75.6–70.1%) were found to not be engaged in any secondary task. 464 (21.3%, with 95% CI: 19.6–23.0%) were observed to participate in one of the recorded activities. 28 (1.3%; with 95% CI: 0.8–1.8%) were observed partaking of two secondary tasks (of these, 19 were holding a phone in their hand and engaging in another activity), and two cyclists were observed to both have their phone in one hand while typing on it and to wear headphones or earphones, resulting in an overall 22.7% (95% CI: 20.9–24.4%) of cyclists engaging in one or more secondary activity. The most common secondary task found was “wearing headphones or earphones,” observed in 285 (13.1%, with 95% CI: 11.7–14.5%) cyclists. Interactions with others were found among 152 cyclists, which corresponds to 82.6% (95% CI: 81.7–83.5%) of those 184 cyclists traveling in groups and 7.0% of all cyclists (95% CI: 5.9–8.0%). Mobile-phone-related activities

2.4. Statistical analyses The observations were automatically stored on the device's hard drive as text files and then imported into SPSS 24 using a routine included with the Observation 2.1 program. To examine influencing factors on secondary task engagement, we computed four logistic regression models (method: enter, Wald criterion) using location and cycle path design, day of the week and time of observation, cyclist traffic volume, weather and lighting conditions, sex, age group and 294

Safety Science 120 (2019) 290–298

A.K. Huemer, et al.

Table 1 Sample characteristic. Descriptive statistics

N

%

Location 1 2 3 4 5 6 7 8

225 363 299 299 265 249 270 208

0.10 0.17 0.14 0.14 0.12 0.11 0.12 0.10

Cycling path design Shared path Separated path Separated path plus green

535 208 1435

24.56 9.55 65.89

Time of day 7:15–8:00 8:15–9:00 9:15–10:00 10:15–11:00 15:00–15:45 16:00–16:45 17:00–17:45 18.00–18:45

465 254 318 149 251 281 239 221

21.35 11.66 14.60 6.84 11.52 12.90 10.97 10.15

Day of the week Tuesday Wednesday Thursday Friday

732 369 407 670

33.61 16.94 18.69 30.76

Weather Sunny Clear Cloudy/foggy Rain

413 420 1120 225

18.96 19.28 51.42 10.33

Lighting conditions Daylight Twilight Dark

1439 375 364

66.07 17.22 16.71

Gender Female Male

1209 969

55.51 44.49

Age group Young (18–24) Middle-aged (25–64) Older (65+)

1208 879 91

55.46 40.36 4.18

Group Alone In group With child

1994 171 13

91.55 7.85 0.60

Traffic flow (bicycles/minute) 0.5 1.0 1.5 2.0 2.5 3.0

362 933 332 332 111 108

16.62 42.84 15.24 15.24 5.10 4.96

Fig. 3. Overall observed percentage of the different types of secondary tasks while cycling. Error bars show 95% CI. Table 2 Significant predictors of the logistic regression for any secondary task activity. Any secondary task

Confidence interval

Predictor

Wald

p

OR

Lower

Upper

Time of day 18:00–18:45 vs. 07:15–8:00 Male vs. female Age Medium vs. young Older vs. young Group vs. alone With child vs. no child

12.144 5.883

0.096 0.015

0.39

0.177

0.825

8.248 127.625 112.517 19.002 178.538 4.954

0.004 < 0.001 < 0.001 < 0.001 < 0.001 0.026

0.69

0.548

0.893

0.20 0.03 32.28 4.58

0.150 0.007 19.400 1.203

0.272 0.155 53.751 17.541

The OR of males compared to females was 0.69, indicating more engagement in secondary task for females. For age, middle-aged cyclists had an OR of 0.20 as compared to younger cyclists, and older cyclists had an OR of 0.03 when compared to the youngest age group. Thus, engagement in secondary tasks was clearly most frequent on the part of the young cyclists. The OR of cycling in a group as compared to cycling alone was 32.28, indicating more engagement in secondary tasks in groups. The OR for cycling with a child versus no child was 4.58. No significant effects were found for location, cycling path design, day of the week, weather, or lighting conditions. The regression model used to predict wearing headphones or earphones explained 19.6% of variance (Nagelkerke R2), χ2(25) = 243.922, p < .001; for details, see Table 3). Cycling with headphones or earphones was less prevalent in the evening (OR = 0.22), for males (OR = 0.69), and for the middle-aged group as compared to the young cyclists (OR = 0.15). It was more frequent for those cycling in a group (OR = 2.04) compared to those cycling alone. Overall, these effects were nearly identical to those of all distracting activities. As the frequencies of the different mobile phone categories were

were found infrequently, in only 44 cyclists (2.0%; with 95% CI: 1.4–2.6%). An overview of the activities is given in Fig. 3.

Table 3 Significant predictors of the logistic regression for wearing headphones or earphones.

3.2. Predictors for secondary task engagement

Secondary task: headphones/earphones

Table 2 presents the results for the significant predictors in the regression model for the presence of any secondary task activity as the criterion (χ2(24) = 559.851, p < .001, Nagelkerke R2 = 0.345, for details see Table 2). Time of day, age, gender, and cycling in a group versus alone, and cycling with a child versus no child were found to be significant predictors. In the evening cyclists were less likely to be engaged in secondary tasks than in the morning (odds ratio [OR] = 0.39). 295

Confidence interval

Predictor

Wald

p

Time of day 18:00–18:45 vs. 07:15–8:00 Male vs. female Age Medium vs. young Group vs. alone

184.460 10.809 7.033 100.880 100.880 17.795

0.010 0.001 0.008 < 0.001 < 0.001 < 0.001

OR

Lower

Upper

0.22 0.69

0.093 0.526

0.548 0.908

0.15 2.04

0.111 0.012

0.228 0.198

Safety Science 120 (2019) 290–298

A.K. Huemer, et al.

4. Discussion

Table 4 Significant predictors of the logistic regression for mobile phone use. Secondary task: mobile phone use

In this observational study on mostly young and middle-aged cyclists on a connecting cycle route, about one-fifth of cyclists (22.7%) were found to be engaged in secondary tasks. This figure is comparable with the results of Terzano (2013), who found 27.6% of cyclists to be engaged in secondary tasks, and less than the findings of Wolfe et al. (2016; 31.2.%), who included objects hanging from the handlebars as distractions which we did not. The most prominent secondary task was wearing headphones or earphones (13.1%), which has been shown to be able to severely disrupt audio perception (De Waard et al., 2011). This is also in line with Terzano (2013) and Wolfe et al. (2016), who found 9.1 and 17.7%, respectively, of cyclists to wear headphones or earphones. The second most commonly found secondary activity in our study was interaction with others (7.0%), which was found less often than by Terzano (2013; 14%), but is often also found in car drivers (11−14% in recent observations, Huemer et al., 2018) when recorded. Mobile phone use was low (2%), comparable to the findings for cyclists of Terzano (2013; 3.5%), De Waard et al. (2010; 2.8%), and De Waard et al. (2015; 3.0%). In car drivers, mobile phone use is found in about 6% to 10% in recent observational studies, with prevalence within overall phone use changing from mostly hand-held phone use to mostly texting over the last ten years (Huemer et al., 2018). It is quite interesting, that in German cyclists, phone use is much lower than in German car drivers (about 6.6% in cities in 2017; Kathmann et al., 2017), but only slightly lower than in the Dutch cycling data, despite handheld phone use for cyclists being forbidden in Germany since as long as for car drivers but just recently having been forbidden in the Netherlands (since 1 July 2019). In contrast to the most recent Dutch cycling data (De Waard et al., 2015) as well as the trend in car driving data (Huemer et al., 2018), in our sample, talking on the phone played a larger role in secondary task engagement than did texting. Also in line with Terzano’s findings (Terzano, 2013), cyclists who were engaged in secondary tasks were also found to be less safe in other regards, especially when it comes to wearing a helmet or light-colored clothing or having at least one hand on the handlebars of their bicycles. In summary, cyclists’ engagement in secondary tasks differs from those of car drivers, with auditory secondary tasks being more prevalent. The demographics of those found to be most engaged in secondary tasks do not differ from the results found in other cycling studies (e.g. Goldenbeld et al., 2012; Stelling-Kończak et al., 2017) or car driving (e.g., Vollrath et al., 2016; Huemer et al., 2018). In our sample, the overrepresented younger age group was also found to be engaged in secondary tasks more often. In contrast to Ethan et al. (2016), we did not find engagement in secondary tasks to be more prevalent in recreational hours of the day or on weekends. We instead found quite the opposite: Engagement in secondary tasks was more often found in the early morning. Surprisingly, no influences of environmental conditions other than weather conditions on mobile phone use were found. Although it is not surprising that phones are used more often in good weather than in rain, as the possibility of damage to a phone is quite high in poor conditions, it may also be that bicyclists refrain from using the phone in bad weather because it seems too dangerous to them. However, it is indeed surprising that we did not find that location influenced secondary task prevalence. With regard to phone related behavior, a reason may by that bicyclists are contacted via phone and thus do not fully control their behavior. Although cyclists clearly demonstrated that they are able to adapt their behavior to protect themselves and their possessions from damage by bad weather, they also showed that they do not seem to be so considerate about others, for example, pedestrians who might be endangered by cyclists engaged in secondary tasks. As in their New York study, Ethan et al. (2016) found location of observation to have a great influence, future researchers are strongly encouraged to explore the range of times and locations in which cyclists adapt their behaviors to environmental conditions. Some limitations are inherent to the method of observation and

Confidence interval

Predictor

Wald

p

OR

Lower

Upper

Age Medium vs. young Sunny/clear vs. rainy/foggy/ cloudy

6.253 6.253 5.102

0.044 0.012 0.024

0.31 7.66

0.125 1.308

0.777 44.563

relatively low, conversing and typing on the phone (hands-free and handheld) were combined as mobile phone use. Fourteen percent of the variance in the use of mobile phones while cycling could be explained by the regression model (Nagelkerke R2; χ2(25) = 43.719, p = .012; see Table 4). Here, weather conditions had the most explanatory power, with the use of a mobile phone being 7.66 times more frequent in sunny and clear weather compared to rainy or cloudy/foggy weather. With regard to the other factors, middle-aged cyclists were less likely to be distracted by their phones than the young age group (OR = 0.31). For interaction with other cyclists, the regression model for accounted for 11.6% of variance (Nagelkerke R2; χ2(5) = 102.370, p < .001; see Table 5). Interactions with other cyclists had a higher prevalence at separated paths compared to separated paths with green (OR = 1.88). Males were more often found to interact with other cyclists (OR = 1.99) than females. For age, middle-aged as well as older cyclists had a lower prevalence of interacting with other cyclists when compared to the youngest age group (OR = 0.15). Given the low frequencies, no regressions were computed for eating, drinking, or smoking. As “other” included very different activities, no regression was computed for that category either.

3.3. Relationship between secondary task engagement and safety precautions To examine the relationship between secondary task engagement and safety precautions, we conducted chi-squared tests for the four major types of secondary tasks (overall, combined phone use, wearing headphones or earphones, and interactions with others) and the recorded safety precautions. Significant effects were found only for overall distraction and wearing headphones or earphones (all other p > .05). Those who engaged in any type of distraction were less likely to wear light-colored clothing (χ2(1) = 7.895, p < .005) or a helmet (χ2(1) = 68.581, p < .001). They were also less often found to have at least one hand on the handlebars (χ2(1) = 76.540, p < .001). Cyclists using their smartphone were less often found to have at least one hand on the handlebar (χ2(1) = 6.689, p = .010). Perhaps most relevant to our findings, cyclists who were wearing headphones or earphones were also less likely to wear a helmet (χ2(1) = 43.863, p < .001) or to have both of their hands on the handlebars (χ2(1) = 9.199, p = .002; see Fig. 4).

Table 5 Significant predictors of the logistic regression for interactions with other cyclists. Secondary task: interaction with other cyclists

Confidence interval

Predictor

Wald

p

OR

Lower

Upper

Cycling path design separated path vs. separated path plus green Male vs. female Age Middle vs. young Older vs. young

6.062 5.986

0.015

1.880

1.133

3.120

15.379 54.491 49.161 6.831

< 0.001 < 0.001 < 0.001 0.009

1.998

1.410

2.804

0.152 0.151

0.089 0.037

0.257 0.623

296

Safety Science 120 (2019) 290–298

A.K. Huemer, et al.

Fig. 4. Percentages of safety precautions in cyclists engaged in secondary tasks and in those who were not.

decreased situational awareness. Both most common secondary tasks, wearing headphones or earphones and interacting with others, correlate with less safe cycling behaviors, i.e. not wearing a helmet or not having at least one hand on the handlebars. This is in line with previous studies and has even been found to increase risky cycling behaviors (Puchades et al., 2018) and self-reported crash risk (Ichikawa and Nakahara, 2008; Puchades et al., 2018, Useche et al., 2018a, Useche et al., 2018b). But, bringing together the low prevalence of visual-manual secondary tasks like phone use found in European observational studies ((2.0−3.5%; this study; Terzano, 2013; De Waard et al., 2010; De Waard et al., 2015), and the high percentage of loss-of-control crashes found to be due to distraction (12% in Boele-Vos et al., 2017) or due to distraction or inattention in Swiss crash data (14.1% of all cyclists’ crashes with injuries, Bundesamt für Statistik, 2019), these visualmanual secondary tasks seem to elevate cyclists’ risk for a fall about 4to 6-fold, as it seems very unlikely, that skid related crashes are caused by auditory impairment due to listening to music. Taken together, it seems that a large number of cyclists are not aware of the effects of unsafe behaviors and secondary task engagement on cycling performance. An educational approach intended to make cyclists aware of the effects of unsafe behaviors and secondary task engagement on cycling performance and thereby teach them how to prevent those negative consequences may be a promising method to promote safer cycling.

need to be mentioned: Some secondary tasks are easier to observe than others. Although eating, drinking, and smoking are quite obvious, wearing headphones or earphones is less so. The wearing of earphones, as the most safety-critical behavior, in particular is quite hard to see if cyclists have longer hair or wear hats or caps or cycling helmets. Here, the actual prevalence might be underestimated in the present study, as it was conducted in the fall, and warmer clothing became more common throughout the weeks. A larger study on a representative sample of cyclists as well as traffic situations and weather conditions over the year is therefore highly recommended. As the study was conducted in autumn, additional observations in summer are recommended. Some biases result from the observation method used in this study: First, all the locations where behavior was observed were along the same main route in order to ensure have sufficient bicycle traffic to observe efficiently. So the effects could be restricted to this type of bicycle path. Second, for legal reasons, we did not observe cyclists younger than 18 years of age, so no information concerning their behavior is available. Third, we observed only some of those cyclists traveling in groups, and as such, cyclists in groups are underrepresented in this study. Additionally, activities of faster cyclists and those cycling in groups are harder to record, as the shorter timeframe in the first case and concealment by others in the second might have hindered observation, which might also lead to underestimation of distractions. Compensatory behaviors like slowing down the bike while talking on the phone (Goldenbeld et al., 2012) also cannot be observed with the present approach, but as Ahlstrom et al. (2016) and Stelling-Kończak et al. (2016) found, these compensatory behaviors are sometimes only reported by cyclists but not shown in real traffic behavior. Observations alone do not reveal anything about cyclists’ motivations for engaging in secondary tasks and unsafe behaviors and therefore should be complemented by, for example, on-site interviews and/ or questionnaires. Such studies may also inform researchers about the role of secondary task engagement in cyclists’ crashes, especially in under-reported single-bike crashes. Overall, these results indicate that the major secondary tasks while cycling are being acoustically impaired by wearing headphones or earphones and interacting with others. As using headphones or earphones impairs cycling performance (Ahlstrom et al., 2016; De Waard et al., 2010; De Waard et al., 2011; De Waard et al., 2014; Goldenbeld et al., 2012; Stavrinos et al., 2018; Terzano, 2013), and as acoustic cues are especially relevant for cyclists, as these may give early warnings concerning the presence of motor vehicles, these tasks may be more safety critical for this population than for car drivers. Riding in groups and interacting with others may be a problem for visual attention and may cause cognitive distraction, leading to a

Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ssci.2019.07.016. References Ahlstrom, C., Kircher, K., Thorslund, B., Adell, E., 2016. Bicyclists’ visual strategies when conducting self-paced vs. system-paced smartphone tasks in traffic. Transport. Res. Part F: Traffic Psychol. Behav. 41, 204–216. https://doi.org/10.1016/j.trf.2015.01. 010. Allgemeine Verwaltungsvorschrift zur Straßenverkehrs-Ordnung (VwV-StVO) [Administrative Regulations to the German Traffic Law] vom 26. Januar 2001* In der Fassung vom 22. Mai 2017 (BAnz AT 29.05.2017 B8) Retrieved August 14, 2018 from http://www.verwaltungsvorschriften-im-internet.de/bsvwvbund_26012001_ S3236420014.htm. Boele-Vos, M.J., Van Duijvenvoorde, K., Doumen, M.J.A., Duivenvoorden, C.W.A.E., Louwerse, W.J.R., Davidse, R.J., 2017. Crashes involving cyclists aged 50 and over in the Netherlands: An in-depth study. Accid. Anal. Prev. 105, 4–10. https://doi.org/10. 1016/j.aap.2016.07.016. Bundesamt für Statistik. (2019). Strassenverkehrsunfälle: Mutmassliche Mängel und Einflüsse nach Mangel oder Einfluss, Objektart, Strassenart, Unfallschwere, Unfalltyp und Jahr. available from: https://www.pxweb.bfs.admin.ch/pxweb/de/px-x1106010100_106/px-x-1106010100_106/px-x-106010100_106.px/table/ tableViewLayout2/?rxid=911e3bd3-70d0-4552-a28d-e8bf128ed49b.

297

Safety Science 120 (2019) 290–298

A.K. Huemer, et al.

Oviedo-Trespalacios, O., Haque, M.M., King, M., Washington, S., 2016. Understanding the impacts of mobile phone distraction on driving performance: a systematic review. Transport. Res. Part C – Emerg. Technol. 72, 360–380. https://doi.org/10.1016/j.trc. 2016.10.006. Oviedo-Trespalacios, O., Haque, M.M., King, M., Washington, S., 2017. Self-regulation of driving speed among distracted drivers: an application of driver behavioral adaptation theory. Traffic Inj. Prev. 1–7. https://doi.org/10.1080/15389588.2017. 1278628. Papantoniou, P., Papadimitriou, E., Yannis, G., 2017. Review of driving performance parameters critical for distracted driving research. Transp. Res. Procedia 25, 1801–1810. https://doi.org/10.1016/j.trpro.2017.05.148. Puchades, V.M., Pietrantoni, L., Fraboni, F., De Angelis, M., Prati, G., 2018. Unsafe cycling behaviours and near crashes among Italian cyclists. Int. J. Injury Control Saf. Promot. 25 (1), 70–77. https://doi.org/10.1080/17457300.2017.1341931. Redelmeier, D., Tibshirani, R., 1997. Association between cellular-telephone calls and motor vehicle collisions. N. Engl. J. Med. 336 (7), 453–458. https://doi.org/10.1056/ NEJM199702133360701. Schreck, B., 2016. Radverkehr und Verkehrssicherheit? Aktuelle Entwicklungen [Cylcling and traffic saefety? Recent developments]. Presentation at the BASt/UDV Symposium “Mehr Radverkehr – aber sicher!”, Spetember 21st, 2016, Berlin. Retrieved October 11, 2016 from https://radsymposium.de/dokumenten-download/#. Singh, S., 2015. Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey. (Traffic Safety Facts Crash Stats. Report No. DOT HS 812 115). National Highway Traffic Safety Administration, Washington, DC. Statistik Austria, 2017. Straßenverkehrsunfälle: Jahresergebnisse 2016 [Road crashes: yearly results 2016]. Retrieved January 28, 2016 from http://www.statistik-austria. at/wcm/idc/idcplg?IdcService=GET_NATIVE_FILE&RevisionSelectionMethod= LatestReleased&dDocName=117882. Statistisches Bundesamt, 2018. Verkehr- Verkehrsunfälle 2017 [Traffic crashes 2017], Fachserie 8, Reihe 7. Retrieved August 13, 2018 from https://www.destatis.de/DE/ Publikationen/Thematisch/TransportVerkehr/Verkehrsunfaelle/ VerkehrsunfaelleJ2080700177004.pdf. Stavrinos, D., Pope, C.N., Shen, J., Schwebel, D.C., 2018. Distracted walking, bicycling, and driving: systematic review and meta-analysis of mobile technology and youth crash risk. Child Dev. 89 (1), 118–128. https://doi.org/10.1111/cdev.12827. Stelling-Kończak, A., Hagenzieker, M., Wee, B.V., 2015. Traffic sounds and cycling safety: The use of electronic devices by cyclists and the quietness of hybrid and electric cars. Transp. Rev. 35 (4), 422–444. https://doi.org/10.1080/01441647.2015.1017750. Stelling-Kończak, A., Hagenzieker, M., Commandeur, J.J.F., Agterberg, M.J.H., van Wee, B., 2016. Auditory localisation of conventional and electric cars: laboratory results and implications for cycling safety. Transport. Res. Part F: Traffic Psychol. Behav. 41, 227–242. https://doi.org/10.1016/j.trf.2015.09.004. Stelling-Kończak, A., van Wee, G.P., Commandeur, J.J.F., Hagenzieker, M., 2017. Mobile phone conversations, listening to music and quiet (electric) cars: are traffic sounds important for safe cycling? Accid. Anal. Prev. 106, 10–22. https://doi.org/10.1016/j. aap.2017.05.014. Straßenverkehrs-Ordnung (StVO) [German Traffic Law] vom 6. März 2013 (BGBl. I S. 367), die zuletzt durch Artikel 2 der Verordnung vom 17. Juni 2016 (BGBl. I S. 1463) geändert worden ist. Retrieved August 14, 2016 from < https://www.gesetze-iminternet.de/bundesrecht/stvo_2013/gesamt.pdf > . Terzano, K., 2013. Bicycling safety and distracted behavior in The Hague, the Netherlands. Accid. Anal. Prev. 57, 87–90. https://doi.org/10.1016/j.aap.2013.04. 007. Useche, S.A., Alonso, F., Montoro, L., Esteban, C., 2018a. Distraction of cyclists: how does it influence their risky behaviors and traffic crashes? PeerJ 6, e5616. https://doi.org/ 10.7717/peerj.5616. Useche, S., Montoro, L., Alonso, F., Oviedo-Trespalacios, O., 2018b. Infrastructural and human factors affecting safety outcomes of cyclists. Sustainability 10 (2), 299. https://doi.org/10.3390/su10020299. Vanparijs, J., Panis, L.I., Meeusen, R., de Geus, B., 2016. Characteristics of bicycle crashes in an adolescent population in Flanders (Belgium). Accid. Anal. Prev. 97, 103–110. https://doi.org/10.1016/j.aap.2016.08.018. Verkehrssicherheitsprogramm 2011, 2011. Retrieved October 25, 2016 from http:// www.bmvi.de/SharedDocs/DE/Publikationen/LA/verkehrssicherheitsprogramm2011.pdf?__blob=publicationFile. Victor, T., Bärman, J., Boda, C.N., Dozza, M., Engström, J., Flannagan, C., Lee, J.D., Markkula, G., 2014. Analysis of Naturalistic Driving Study Data: Safer Glances, Driver Inattention, and Crash Risk. SHRP 2 Safety Project S08A. Retrieved January 28, 2016 from: http://onlinepubs.trb.org/onlinepubs/shrp2/SHRP2_ prepupS08Areport.pdf. Vollrath, M., Huemer, A.K., Nowak, P., Pion, O., 2014. Ablenkung durch Informationsund Kommunikationssysteme. [Driver distraction by information and communication systems]. Unfallforschung der Versicherer, Forschungsbericht Nr. 26. Berlin. Retrieved February 22, 2017 from udv.de/sites/default/files/tx_udvpublications/fb_ 26_ablenkung_0.pdf. Vollrath, M., Huemer, A.K., Teller, C., Likhacheva, A., Fricke, J., 2016. Do German Drivers use their Smartphones safely? – Not really!. Accid. Anal. Prev. 96, 29–38. https://doi.org/10.1016/j.aap.2016.06.003. Von Below, A., 2016. Verkehrssicherheit von Radfahrern - Analyse sicherheitsrelevanter Motive, Einstellungen und Verhaltensweisen. [Cyclists’ traffic safety – analysis of safety relevant motives, attitudes and behaviors]. Berichte der Bundesanslatlt für Straßenwersen. Mensch und Sicherheit M264. Bremerhaven: Wirtschaftsverlag NW. Wolfe, E.S., Arabian, S.S., Breeze, J.L., Salzler, M.J., 2016. Distracted biking: an observational study. J. Trauma Nursing: Off. J. Soc. Trauma Nurses 23 (2), 65. https:// doi.org/10.1097/JTN.0000000000000188. Young, R., 2017. Removing Biases From Crash Odds Ratio Estimates of Secondary Tasks: a New Analysis of the SHRP 2 Naturalistic Driving Study Data (No. 2017-01-1380). SAE Technical Paper.

Caird, J.K., Johnston, K.A., Willness, C.R., Asbridge, M., Steel, P., 2014. A meta-analysis of the effects of texting on driving. Accid. Anal. Prev. 71, 311–318. https://doi.org/ 10.1016/j.aap.2014.06.005. Chaurand, N., Delhomme, P., 2013. Cyclists and drivers in road interactions: A comparison of perceived crash risk. Accid. Anal. Prev. 50, 1176–1184. https://doi.org/10. 1016/j.aap.2012.09.005. Chataway, E.S., Kaplan, S., Nielsen, T.A.S., Prato, C.G., 2014. Safety perceptions and reported behavior related to cycling in mixed traffic: A comparison between Brisbane and Copenhagen. Transport. Res. Part F: Traffic Psychol. Behav. 23, 32–43. https:// doi.org/10.1016/j.trf.2013.12.021. De Geus, B., Vandenbulcke, G., Panis, L.I., Thomas, I., Degraeuwe, B., Cumps, E., Meeusen, R., 2012. A prospective cohort study on minor accidents involving commuter cyclists in Belgium. Accid. Anal. Prev. 45, 683–693. https://doi.org/10.1016/j. aap.2011.09.045. De Waard, D., Schepers, P., Ormel, W., Brookhuis, K., 2010. Mobile phone use while cycling: Incidence and effects on behaviour and safety. Ergonomics 53 (1), 30–42. https://doi.org/10.1080/00140130903381180. De Waard, D., Edlinger, K., Brookhuis, K., 2011. Effects of listening to music, and of using a handheld and handsfree telephone on cycling behaviour. Transport. Res. Part F: Traffic Psychol. Behav. 14 (6), 626–637. https://doi.org/10.1016/j.trf.2011.07.001. De Waard, D., Lewis-Evans, B., Jelijs, B., Tucha, O., Brookhuis, K., 2014. The effects of operating a touch screen smartphone and other common activities performed while bicycling on cycling behaviour. Transport. Res. Part F: Traffic Psychol. Behav. 22, 196–206. https://doi.org/10.1016/j.trf.2013.12.003. De Waard, D., Westerhuis, F., Lewis-Evans, B., 2015. More screen operation than calling: the results of observing cyclists' behaviour while using mobile phones. Accid. Anal. Prev. 76, 42–48. https://doi.org/10.1016/j.aap.2015.01.004. Dingus, T.A., Guo, F., Lee, S., Antin, J.F., Perez, M., Buchanan-King, M., Hankey, J., 2016. Driver crash risk factors and prevalence evaluation using naturalistic driving data. Proc. Nat. Acad. Sci. 113 (10), 2636–2641. https://doi.org/10.1073/pnas. 1513271113. Ethan, D., Basch, C.H., Johnson, G.D., Hammond, R., Chow, C.M., Varsos, V., 2016. An analysis of technology-related distracted biking behaviors and helmet use among cyclists in New York City. J. Community Health 41 (1), 138–145. https://doi.org/10. 1007/s10900-015-0079-0. Ferdinand, A.O., Menachemi, N., 2014. Associations between driving performance and engaging in secondary tasks: a systematic review. Am. J. Public Health 104 (3), e39–e48. https://doi.org/10.2105/AJPH.2013.301750. Fitch, G., Grove, K., Hanowski, R., Perez, M., 2014. Compensatory behavior of drivers when conversing on a cell phone: investigation with naturalistic driving data. Transport. Res. Rec.: J. Transport. Res. Board 2434, 1–8. https://doi.org/10.3141/ 2434-01. Goldenbeld, C., Houtenbos, M., Ehlers, E., de Waard, D., 2012. The use and risk of portable electronic devices while cycling among different age groups. J. Saf. Res. 43 (1), 1–8. https://doi.org/10.1016/j.jsr.2011.08.007. Hollingworth, M.A., Harper, A.J., Hamer, M., 2015. Risk factors for cycling accident related injury: the UK Cycling for Health Survey. J. Transport Health 2 (2), 189–194. https://doi.org/10.1016/j.jth.2015.01.001. Huemer, A.K., Eckhardt-Lieberam, K., 2016. Regelkenntnisse bei deutschen RadfahrerInnen: Online-Befragungen unter Erwachsenen und SchülerInnen. [Rule knowledge among German cyclists: Results of two online-questionnaires for adult and adolescent cyclists]. Zeitschrift für Verkehrssicherheit 62 (5), 250–260. Huemer, A.K., Schumacher, M., Mennecke, M., Vollrath, M., 2018. Systematic review of observational studies on secondary task engagement while driving. Accid. Anal. Prev. 119, 225–236. https://doi.org/10.1016/j.aap.2018.07.017. Huth, V., Sanchez, Y., Brusque, C., 2015. Drivers’ phone use at red traffic lights: a roadside observation study comparing calls and visual-manual interaction. Accid. Anal. Prev. 74, 42–48. https://doi.org/10.1016/j.aap.2014.10.008. Ichikawa, M., Nakahara, S., 2008. Japanese high school students' usage of mobile phones while cycling. Traffic Inj. Prev. 9 (1), 42–47. https://doi.org/10.1080/ 15389580701718389. Kathmann, T., Scotti, C., Huemer, A.K., Mennecke, M., Vollrath, M., 2017. Konzept für eine kontinuierliche Erhebung der Nutzungshäufigkeit von Smartphones beim Fahren – Pilotstudie. [Concept for a continuous observation of smartphone use while driving – Pilot study] Projektendbericht. Bundesanstalt für Straßenwesen. Kircher, K., Ahlstrom, C., Palmqvist, L., Adell, E., 2015. Bicyclists’ speed adaptation strategies when conducting self-paced vs. system-paced smartphone tasks in traffic. Transport. Res. Part F: Traffic Psychol. Behav. 28, 55–64. https://doi.org/10.1016/j. trf.2014.11.006. Lipovac, K., Đerić, M., Tešić, M., Andrić, Z., Marić, B., 2017. Mobile phone use while driving-literary review. Transportation research part F: traffic psychology and behaviour 47, 132–142. Metz, B., Landau, A., Hargutt, V., 2015. Frequency and impact of hands-free telephoning while driving–Results from naturalistic driving data. Transport. Res. Part F: Traffic Psychol. Behav. 29, 1–13. https://doi.org/10.1016/j.trf.2014.12.002. Metz, B., Landau, A., Just, M., 2014. Frequency of secondary tasks in driving – Results from naturalistic driving data. Saf. Sci. 68, 195–203. https://doi.org/10.1016/j.ssci. 2014.04.002. Mobilität in Deutschland – Ergebnisbericht, 2018. Retrieved January 21, 2019 from http://www.mobilitaet-in-deutschland.de/pdf/MiD2017_Ergebnisbericht.pdf. Mwakalonge, J.L., White, J., Siuhi, S., 2014. Distracted biking: A review of the current state-of-knowledge. Int. J. Traffic Transport. Eng. 3 (2), 42–51. https://doi.org/10. 5923/j.ijtte.20140302.02. Nationaler Radverkehrsplan 2020. Retrieved October 11, 2016 from https://nationalerradverkehrsplan.de/. Observation 2.1 - Feldbeobachtung mit dem Tablet [Field observations using a tablet pc], 2017. Available from https://www.tu-braunschweig.de/psychologie/abt/ingenieur/ software.

298