Smartphone use while driving a simulator study

Published Project Report PPR592 Smartphone use while driving a simulator study D Basacik, N Reed and R Robbins Commissioned by

Transport Research Laboratory Creating the future of transport PUBLISHED PROJECT REPORT PPR592 Smartphone use while driving A simulator study D. Basacik, N. Reed and R. Robbins Prepared for: IAM (the Institute of Advanced Motorists) Project Ref: 11111660 Quality approved: Dan Basacik (Project Manager) Nick Reed (Technical Referee) Transport Research Laboratory 2011

Disclaimer This report has been produced by the Transport Research Laboratory under a contract with IAM. Any views expressed in this report are not necessarily those of IAM. The information contained herein is the property of TRL Limited and does not necessarily reflect the views or policies of the customer for whom this report was prepared. Whilst every effort has been made to ensure that the matter presented in this report is relevant, accurate and up-to-date, TRL Limited cannot accept any liability for any error or omission, or reliance on part or all of the content in another context. When purchased in hard copy, this publication is printed on paper that is FSC (Forest Stewardship Council) and TCF (Totally Chlorine Free) registered. Contact details TRL Crowthorne House Nine Mile Ride Wokingham Berkshire RG40 3GA Tel: +44 (0)1344 773131 Fax: +44 (0)1344 770356 Email: enquiries@trl.co.uk The Institute of Advanced Motorists IAM House 510 Chiswick High Road LONDON W4 5RG Tel: +44 (0) 20 8996 9600 Fax: +44 (0) 20 8996 9601 Email: enquiries@iam.org

IAM Chief Executive s Foreword Over the last few years mobile phones have become significantly more developed, allowing people to access social networking sites, browse the internet, use maps and play music. Complex applications can now be used on the move anywhere in the world, and as technology advances further we need to understand how it impacts on driver behaviour. We know that driver errors contribute to the vast majority of road accidents, with in-car distractions such as mobile phones increasing the risks of careless driving. Evidence on in-car distractions needs to stay up to date with advances in technology, and research on the multiple uses of smartphones is now needed. The IAM has commissioned this timely report from TRL (Transport Research Laboratory) to understand the risks of using smartphones to access social networking sites whilst driving. As the UK s largest independent road safety charity, we are committed to original research, and this report provides clear and decisive results which we hope will contribute to the future of road safety. The ban on using handheld mobile phones in 2003 was met with a mixed response, and enforcement continues to be an issue. However, this report clearly shows that using a smartphone to access social networking sites creates risks to drivers as serious as drink driving by repeatedly drawing a driver s vision and attention away from the road. All drivers should be aware of the risks they are taking when using any mobile whilst driving. Even drivers who refrain from making calls may be tempted to use a smartphone to check a message, map, update their status, or look for traffic updates. More needs to be done to educate drivers, and encourage safe behaviour. Government and road safety organisations need to work together to highlight the issue of smartphones and challenge perceptions. Phone manufacturers and social network providers also have a key role to play in spreading the message. Attitudes to seatbelts and drink driving have changed dramatically over the last thirty years, and, with the right information, the use of smartphones could become a similar success story. I would like to thank TRL for their sound and in-depth analysis in this report, and for their hard work in collating the results. I believe that this report is a valuable addition to the body of research on in-car distractions, helping to keep road safety analysis up to date with technological advances. Simon Best Chief Executive IAM (Institute of Advanced Motorists)

Contents 1 Introduction 2 2 Literature review 3 2.1 Why worry about distracted driving? 3 2.2 Does mobile phone use while driving increase accident risk? 3 2.3 How does using a mobile phone affect driving? 3 2.4 What is known about smartphone use or social networking while driving? 4 2.5 Research hypotheses 5 3 Method 6 3.1 Participants 6 3.2 Equipment 6 3.3 Familiarisation 7 3.4 Study design 7 3.5 Participant instructions 7 3.6 Route design 8 3.7 Smartphone task 10 3.8 Overall design 14 3.9 Trial procedure 15 3.10 Recorded simulator data 15 3.11 Visual behaviour 16 3.12 Questionnaire 16 3.13 Calculation 16 4 Results 17 4.1 Participants 17 4.2 Reaction time (RT) tasks 17 4.3 Analyses of driving performance while using a smartphone 18 4.4 Visual behaviour 23 4.5 Effects of driving on smartphone use 26 4.6 Subjective effects of smartphone use on driving performance 27 4.7 Patterns of mobile phone use 29 4.8 Perceptions of legality 33 4.9 Perceptions of relative risks of driving behaviours 35 5 Discussion 36 5.1 Comparison with previous studies 37

Executive Summary A substantial body of research has shown that use of a mobile phone while driving leads to poorer driving performance. Previous research has covered different elements of telephone use, including hand-held and hands-free use while making a call and the reading and writing of text messages. Mobile phones have recently undergone a substantial change and smartphones have gained popularity. These devices allow users to take part in a much broader range of activities than traditional mobile phones, including browsing the web, emailing, social networking, and browsing and playing personal media. With smartphones now becoming common in the commercial marketplace, more and more drivers are able to engage in a broad range of possible activities on their phone. There is a clear potential for motorists to be tempted to conduct these activities whilst driving a vehicle. This study set out to investigate whether there was an effect of social networking using a smartphone on driving performance. Twenty-eight young male and female participants took part in the study and drove a driving simulator through the same test scenario twice: once while using a smartphone to interact with a social networking site, and once without this distraction. This experimental approach allowed potentially hazardous road situations to be designed and experienced twice, in complete safety. The results of the experiment clearly show that participants driving performance was impaired by the concurrent smartphone task, and the smartphone task was also affected by driving. When compared with their driving performance without a smartphone: Participants were more likely to miss the reaction time stimuli while using their phone. When they did respond, reaction times to visual and auditory stimuli were found to increase by approximately 30% when using a smartphone to send and receive messages on a social networking site. They were unable to maintain a central lane position and this resulted in an increased number of unintentional lane departures. They were unable to respond as quickly to a lead vehicle gradually changing speed, thus driving at a more variable time headway. They spent between 40% and 60% of the time looking down while using a smartphone to write or read messages, compared with about 10% of the time looking down in the same sections of the control drive. These results suggest that participants driving was impaired when they were using a smartphone to send and receive messages on a social networking site. The results of this study indicate that this reduction in driving performance is likely to have been a result of three different types of distraction: having to concentrate on the smartphone task (cognitive), holding the phone (physical), and the significant increase in time spent looking at the phone (visual) in order to interact with it. Although participants did reduce their speed, this was not enough to compensate for the poorer driving performance; even though they were driving more slowly, they were still unable to control the vehicle as well as they did when they were not using their smartphone. Version 2.0 1 PPR592

1 Introduction There is a substantial body of research that has shown the potential distraction posed to a driver by the use of a mobile phone. This research has covered different elements of telephone use, including hand-held and hands-free use while making a call (Burns et al., 2002, Strayer et al., 2006, Parkes et al., 2007) and the reading and writing of text messages (Reed and Robbins, 2008). In recent years, however, mobile phones have undergone a substantial change and socalled smartphones have gained popularity. Aside from making phone calls and sending text messages, these devices enable users to send and receive emails, connect to the internet to browse web pages, browse and play music and videos, social network and carry out a broad range of other activities. According to Ofcom, smartphone sales accounted for 48% of all mobile phone sales in the UK in the first quarter of 2011 (Ofcom, 2011). With smartphones now becoming common in the commercial marketplace, more and more drivers are now able to engage in a broad range of possible activities on their phone. There is a clear potential for motorists to be tempted to conduct these activities whilst driving a vehicle. Furthermore, even for the more traditional calling and texting functions of a phone, smartphone interfaces are significantly different to mobile phone interfaces from five to ten years ago. For example, many smartphones have touchscreens rather than keypads (Canalys, 2010), and with increasing functionality come the issues of device and menu complexity. Such interface issues are likely to change how people use the device and therefore how distracting it is to use the device while driving. From the findings of research looking at more traditional aspects of mobile phone use in a vehicle, it is likely that smartphone use in a vehicle would pose a distraction and lead to a detriment in driving performance. What is not clear is the extent to which performance would be expected to decrease. This report describes a study that has investigated the effects of using a smartphone for social networking while driving using a high-fidelity driving simulator. Chapter 2 presents a review of the literature on the use of mobile phones while driving and gives some background to the study, whilst Chapters 3 through 5 present the method, results and findings of the driving simulator study. Version 2.0 2 PPR592

2 Literature review 2.1 Why worry about distracted driving? In recent decades driver distraction has been a popular research topic and continues to receive significant attention. Two main factors are likely to be driving ongoing research. Firstly on-road studies are showing just how salient a factor distraction is in crashes. In NHTSA s 100-Car Naturalistic Driving Study, participants were given an instrumented vehicle to drive instead of their own vehicle. The resulting dataset included 43,000 hours of driving (approximately 2 million vehicle miles) and showed that engagement in a secondary task contributed to more than 22% of all crashes and near-crashes that were recorded in the study period (Klauer et al., 2006). The second reason is that the potential for drivers to be distracted is increasing as technology develops. More recent sources of distraction outside of the vehicle include motion picture advertising billboards (Chattington et al., 2010), while smart phones and systems built into the vehicle give the driver the opportunity to engage with email, social networking sites, music collections and to conduct a range of other activities. Thus, an improved understanding of distraction as a contributory factor to crashes, and an awareness of the increasing potential for drivers to be distracted by technology are driving research efforts in this area. 2.2 Does mobile phone use while driving increase accident risk? Data from the 100-car study (Klauer et al., 2006) shows that distraction is associated with accidents but does not distinguish between different distractors. An earlier study examining the mobile telephone records belonging to 699 drivers involved in car accidents (with property damage only) showed that the risk of collision increased fourfold with mobile phone use (Redelmeier and Tibshirani, 1997). Relative risk of collision was 1.3 times higher if calls were made up to 15 minutes before the collision and this figure rose to 4.8 times higher if a call was placed within 5 minutes of the collision (Redelmeier and Tibshirani, 1997). Interestingly this study found no difference in accident risk between those having hand-held and hands-free mobile phone conversations, suggesting that the risk of collision isn t related to holding the phone. The results of this study indicate that having a mobile phone conversation while driving increases crash risk. 2.3 How does using a mobile phone affect driving? Studies conducted on-road or in laboratory settings have looked closely at the particular effects that mobile phone use has on the performance of activities required for driving. They have concluded that mobile phone use is associated with: Increased ratings of mental workload (Parkes et al., 2007; Lesch & Hancock, 2004; Harbluk et al., 2002; Burns et al., 2002; Parkes et al., 1993) A reduction in people s perceptual visual fields by up to 10% (Maples et al., 2008) Longer glances ahead, at the expense of other monitoring behaviours such as checking the mirrors and vehicle instruments (Parkes et al., 2007; Harbluk et al., 2002) Version 2.0 3 PPR592

Poorer awareness of and response to the traffic situation (Parkes et al., 2007, Parkes and Hooijmeijer, 2000) despite longer glances ahead Slower detection of and responses to hazards (Caird et al., 2008) A reduction in speed in an attempt to compensate for the perceived impairment caused by using a phone (Reed and Robbins, 2008; Burns et al., 2002) This list shows how driving performance can suffer when people are given the additional task of using a mobile phone. Interestingly, driving simulator studies have also shown that for some performance measures, hands-free mobile phone conversations can reduce driving performance as much as hand-held mobile phone conversations. For example, Parkes et al. (2007) concluded that mobile phone use delayed reaction times when compared with a control condition, and also when compared with a condition in which participants were at the UK legal limit for drink-driving, yet there was no statistically significant difference between the hand-held and hands-free mobile phone conditions. When taken together with the studies described in Section 2.2, a clear conclusion is that having a mobile phone conversation while driving impairs driving performance. 2.4 What is known about smartphone use or social networking while driving? This review has not found research looking at the effects of smartphone use for social networking on driving. However, it is possible to make high-level predictions based on studies which have looked at other devices or tasks that are in some ways similar. Reading a message received through a social networking application on a smartphone may be similar to reading a text message received on an ordinary mobile phone. Reed and Robbins (2008) investigated the effects of reading and writing text messages on driving performance. When reading a text message, participants took longer to respond to stimuli, and were less able to keep to the centre of their lane or at a fixed distance behind a lead vehicle. Similar findings are also reported by Cooper et al. (2011) who studied driving performance while texting. Their experiment was carried out on a test track and the authors report significantly slower responses, more missed response events, reductions in speed, poor lane keeping and fewer glances ahead while texting. Writing a message on a social networking site could be comparable with writing a text message. The studies carried out by Reed and Robbins (2008) and Cooper et al. (2011) found that writing a text message on a mobile phone had an even greater effect on driving performance than reading a message. All of the participants used mobile phones with ordinary push-button keypads in the former study, and although a wider variety of phone interfaces were used in the latter, no analysis of these differences is presented. Owens et al. (2010) examined driving performance while text messaging using handheld and in-vehicle systems. Participants used either a phone with a standard keypad or a touch screen but differences between groups were not reported. Johnson (2011) highlights perceptual issues which affect how efficiently people can use touchscreens for data entry. He argues that while it is possible for people to learn to touch type without looking at a keyboard, the smooth surfaces of touch screens prevent people from being able to feel the location of keys, and therefore interactions require visual input. Sears (1991) found that participants in his study were able to type on Version 2.0 4 PPR592

average 58 words per minute on a standard keyboard, but only 25 words per minute on a touchscreen keyboard with the same layout. Keyboard and key size have a significant effect on typing speed. An experiment by Sears et al. (1993) has shown that both novice and experienced participants were able to type more quickly on a larger touchscreen keyboard with larger keys, than a smaller keyboard with smaller keys. The keyboards used in this study ranged from 6.8 to 24.6cm wide. Tsimhoni et al. (2002) gave participants of their study the task of entering an address into a satellite navigation system while driving. Participants used three methods to enter the address; two of these used speech recognition and one used a touch screen keyboard on a 7 inch screen (this, compared with the 3.5 inch screen on the iphone 4, not all of which is taken up by a keyboard). Keys were 12.7mm high by 12mm wide, and spaced 0.7mm apart. The study showed that the speech recognition systems were clearly favourable; people were able to complete address entry much more quickly and were able to keep closer to the centre of their lane, suggesting that manual text entry on the touch screen was a distracting task. In summary, the literature seems to suggest that reading text on a mobile phone leads to poorer driving performance. One would expect this to hold true when using a smartphone for social networking. There is also evidence to suggest that text entry on a mobile phone distracts from the driving task. Furthermore, there are suggestions that use of touch screens for text entry brings with it issues that could exacerbate the problem. 2.5 Research hypotheses Based on this literature it is possible to come up with some predictions about driving performance while using a smartphone for social networking. These predictions form the research hypotheses to be investigated during the driving simulator study: Participants will drive more slowly when they are using a smartphone Participants reaction times will be slower when using a smartphone Participants lane position will vary more while using a smartphone Participants will spend more time looking inside the vehicle while using a smartphone Participants will report that their driving was worse while using a smartphone The method used during this study is described in Chapter 3. It is based heavily on that used by Reed and Robbins (2008), which itself is based Burns et al. (2002), enabling comparison of the extent of driver impairment with previous studies. Version 2.0 5 PPR592

3 Method 3.1 Participants Twenty-eight participants were recruited from the TRL participant database to take part in the study, with an approximately even split between males and females. They met the following criteria in order to be included in the study: Participants described themselves as regular users of Facebook on smartphones Participants had the Facebook app on their smartphone Participants were current owners of a touchscreen smartphone (iphone or Android) Participants were aged between 18 and 25 Participants drove more than 5000 miles per year Participants had experience of driving on a motorway Participants had driven the simulator before Participants successfully completed a familiarisation drive in the simulator Participants were required to use their own phones for the study. 3.2 Equipment The TRL Driving Simulator (DigiCar) consists of a medium sized family hatchback (Honda Civic) surrounded by four 3 4 metre projection screens giving 210º front vision and 60º rear vision, enabling the normal use of the vehicle s driving and wing mirrors. The road images are generated by four PCs running SCANeR II software (manufactured by Oktal) and are projected onto the screens by five Digital Light Processing (DLP) projectors. Images are refreshed at a rate of 60Hz (every 16.7msec) whilst data is sampled at a rate of 20Hz (every 50msec). Figure 1: TRL driving simulator, DigiCar Version 2.0 6 PPR592

Electric motors supply motion with 3 degrees of freedom (heave, pitch and roll) whilst engine noise, external road noise, and the sounds of passing traffic are provided by a stereo sound system. Two studies have demonstrated the validity of the TRL simulator (Duncan, 1995; Sexton, 1997) and Diels et al. (in press) confirm that the current simulator system is at least as accurate as that used in the Duncan and Sexton studies. 3.3 Familiarisation Participants were required to complete a ten minute familiarisation drive on a benign motorway environment prior to completing any of the test drives. This was to help participants to become comfortable with controlling the simulator vehicle and driving in the virtual environment. The drive included a car following task in which participants were required to drive at a safe and constant distance behind a lead vehicle. During this task, white chevrons were included on the motorway (as used on some sections of UK motorways) helping the participant to judge a safe distance to the lead vehicle. 3.4 Study design After familiarisation, participants carried out two drives: A smartphone drive in which they had to read and write messages and update their status using a social networking application A control drive along the same route, but without having to use a smartphone To counterbalance any learning effects caused by driving the same route twice, the order of these two drives was alternated between participants. 3.5 Participant instructions Participants were given the following instructions: Version 2.0 7 PPR592

Please adjust the seat position and secure the safety belt. The car controls work in the same manner as any normal car and it operates with a manual gearbox. You need to make sure the car is in neutral when you start it and it needs plenty of revs, otherwise it has a tendency to stall. It is important that you drive as you would normally. We don t want you to drive as if you are on a driving test nor as if the simulation is a computer game. We are not here to judge your driving, so please do not feel anxious. A red bar like the one you can see on the screen now will appear during your drive. There is also a buzzing noise that will sound during the drive. When you hear the buzzing noise, or see the red bar please press the clutch pedal as quickly as you possibly can. You will hear the buzzing sound about 20 seconds into this drive as a practice to help you recognise it. The drive will start on the motorway with normal traffic. After a while, the motorway will end and you will reach a series of bends. You should try to keep to 40 mph through this section and the simulator will assess your ability to keep to the centre of your lane through the bends. After the series of bends, you will drive on the motorway again. After a period of time you will see a vehicle in front of you. Please pull up behind this vehicle and follow it, doing your best to keep at a safe and constant distance behind it. A voice instruction will let you know when the car following task has finished. There will be voice instructions to remind you about these tasks. Before the smartphone drive, participants were also instructed: During this drive you will be asked to send and receive private messages on Facebook and update your Facebook status. Your contact for all of the private messages is Daniel Boyd, who we added as your Facebook friend earlier. Please only send and read messages when you are asked to. Please write messages as you normally would, ie using short words and predictive text. If participants asked whether they should put their phone in a specific place in the vehicle, they were advised that they should keep it wherever they normally would while they were driving. 3.6 Route design The simulator route driven by participants consisted of four sections with smooth naturalistic transitions between each section. These are shown in Table 1. Version 2.0 8 PPR592

Table 1: The road sections used for the simulator trial Section Description Length Configuration 1 Motorway 1 17.5 miles 2 Two loops 4.6miles 3 Car following 8.1 miles 4 Motorway 2 7.3 miles 3 lane motorway plus hard shoulder in each direction. Light traffic present Each loop is a two-lane figure 8 with a long left turn and long right turn separated by a short straight 3 lane motorway plus hard shoulder in each direction. One vehicle present that the participant is required to follow at a steady distance 3 lane motorway plus hard shoulder in each direction. Light traffic present Total 37.5 miles In the loops section, participants were instructed to try to stay in the centre of their lane and to drive at 40mph. In the car following section, participants were instructed to follow the lead vehicle at safe and constant distance (as they would have experienced in the familiarisation drive). The lead vehicle smoothly and repetitively increased and decreased its speed between 43.8mph (70kph) and 68.8mph (110kph) over a period of 20 seconds. 3.6.1 Reaction time events During both the smartphone and control drives, participants were required to respond to trigger stimuli on four occasions in order to test their reaction times. In three of the reaction events, the trigger stimulus was a short auditory tone (60dB; 0.45 seconds duration; 333Hz). The fourth reaction time trigger event was the presentation of a red bar stimulus above the carriageway and ahead of the driven vehicle across all motorway lanes. This is shown in Figure 2. Version 2.0 9 PPR592

Figure 2: Red bar stimulus Participants were instructed to respond by depressing the clutch pedal as quickly as possible. Clutch depression is measured from 0 (foot off clutch) to 1 (clutch fully depressed). The threshold for clutch activation was 0.1 (10% clutch depression). If clutch depression was greater than 10% at the time of the reaction time trigger, the event would have been ignored but this did not occur in any of the trials. If participants failed to respond within 10 seconds, this was treated as a missed event. 3.7 Smartphone task The research brief was to use a social networking task. This would serve the purpose of straying beyond the functionality of a standard mobile phone whilst still requiring participants to take part in written communication, as one would when sending and receiving text messages. 3.7.1 Choice of social networking application A plethora of applications are available which allow users to access many websites via their smartphones. For the current study, we conducted market research to identify a widely used social networking application. This was to ensure the secondary task used in the experiment was representative of real-world usage. Table 2 shows which mobile applications were most widely visited by UK mobile consumers in April 2011. The data show that nearly 8.8 million UK mobile owners used Version 2.0 10 PPR592

an app that connected to the internet during April 2011, with Google Maps ranking as the most accessed app with 6.4 million unique users. It can be seen that the social networking application Facebook was ranked as the third most often used application. Other popular social networking applications such as LinkedIn or Twitter did not feature in the top 10. Table 2: Connected Mobile Applications Ranked by Unique Visitors (from Comscore, 2011) Rank Mobile app Total unique visitors 1 Google Maps 6,419,503 2 Yahoo! Weather 3,567,047 3 Facebook 3,456,442 4 Google Mobile 2,554,329 5 YouTube 2,438,348 6 ebay 1,195,496 7 Sky Sports Live Football Score Centre 1,004,085 8 Yahoo! Stocks 959,289 9 WhatsApp Messenger 798,656 10 Sky News 732,374 Total connected app users: 8,735,197 Figure 3 shows the demographics of Facebook users. It can be seen that for all age categories, there tends to be approximately an equal split between male and female users with slightly more female users. Furthermore, the figure shows that Facebook use is biased towards the younger age categories. In the context of the current study, it is of relevance to point out that motorists under 24 years of age are overrepresented in the accident statistics (DfT, 2011). Hence, the issue of smartphone use by younger drivers is of particular relevance. On the basis of the above information, Facebook was chosen as an appropriate application to be used in the experimental study. Age (years) Number of users Figure 3: UK Facebook users split by age and gender (Sources: Facebook, Alexa, June 2010) Version 2.0 11 PPR592

3.7.2 Choice of smartphone operating system Table 3 shows the percentage of users for the different smartphone operating systems. Apple and Google Android account for 96% of the market share. Because of the similarity in both functionality and user interface of the Facebook application on both Apple and Android, the experiment allowed participants to partake with either of the operating systems (OS). Table 3: Share (%) of Connected Application Users by Smartphone Operating System (from Comscore, 2011) Smartphone Operating System Apple ios Google Android OS Symbian OS Other OS Total Unique Visitors 5,702,166 2,699,982 118,957 356,871 %Share 65% 31% 1% 3% Figure 4 shows the Facebook home page on the Apple (left) and Google Android (right) operating systems. It can be seen that the application is similar on both OS whereby the Apple OS provides slightly more functions ( Chats Groups Places ) on the Facebook homepage. In selecting the tasks to be completed by participants during the study, those related to these three functionalities were excluded. Figure 4: Sample screenshot of the Facebook home page on Apple (left) and Android (right) operating systems Version 2.0 12 PPR592

3.7.3 Facebook tasks whilst driving Through the course of the drive participants were required to perform a succession of different tasks using the Facebook application. Participants were asked to become Facebook friends with a Daniel Boyd, whose account was used by experimenters to send messages to participants and receive messages from them during the smartphone drive. 3.7.3.1 Writing messages Participants had to write five messages in the drive. All comprised an approximately similar number of characters. The instructions as to what to write and the message recipient were delivered as automated verbal messages in the simulation. Participants were instructed to compose the text in their own usual style. This included using predictive text, autocorrect and applying SMS language. The first message was included as practice to ensure participants were comfortable what was required of them. Table 4 shows the messages that participants were required to compose. Table 4: Messages composed by participants whilst driving Message Section Message Characters Practice 1 I am driving a great car simulator 34 1 1 Happy birthday Have fun at the party 36 2 2 Nice to see you at the cafe yesterday 37 3 3 Dont worry Have a nice time in Paris 36 4 4 Sorry about your ankle Get well soon 36 3.7.3.2 Reading messages Participants were sent two messages over the course of their drive. Participants were informed by an automated voice instruction that they were about to receive a message and that they would need to read the message in order to be able to answer the questionnaire at the end of the drive. Table 5 shows the messages that participants received. Table 5: Messages received by participants whilst driving Message Section Message 1 1 Edward has forgotten his BOWTIE for the wedding 2 2 Fiona won the SILVER medal in the 100m sprint 3.7.3.3 Updating Facebook status In addition to writing and reading messages, participants were also asked to update their Facebook status. Table 6 shows the section and text for the status update. Version 2.0 13 PPR592

Smartphone use while driving Table 6: Status update text Section Text Ch haracters 3 I am nearr the end of the virtual world 38 8 3.7.3.4 Control message es To compare how quickly participants could write short messages on their smartphone, participants were timed com mposing some comparable text on their smartphone when they were not driving. These e were as follows: Table 7: Timed message es composed by participants without distraction d (i.e. driving) Message Characters Best of luck for your driving test today 40 Well done Looking forward to o the wedding 40 Please can you bring red win ne tonight 37 Where did you get those new w trousers 36 3.8 Overall design Figure 5 shows the incidence e of social networking tasks and reaction tiime (RT) events in the smartphone drive. Note that the control drive is exactly the same as a the smartphone drive with the exception thatt participants were not required to interact with their phone. Figure 5: Schematic timeline of events in the drive Version 2.0 14 PPR592

3.9 Trial procedure The trial proceeded as shown in Table 8. Table 8: Trial procedure and schedule Time from start Activity Duration 0 Welcome and introduction 5 5 Control messages (either before or after drives) 5 10 Simulator: Familiarisation drive 10 20 Background questionnaire 10 30 Drive 1 (Smartphone or Control) 40 70 Break 10 80 Drive 2 (Control or Smartphone) 40 120 Phone usage questionnaire 20 140 Depart 3.10 Recorded simulator data Table 9 shows the data recorded by the simulator. All data was recorded at 20Hz. Table 9: Data recorded by the simulator Data Time X position of interactive vehicle Y position of interactive vehicle Z position of interactive vehicle Speed Distance through trial Lateral distance from centre of road Headway Time Headway Accelerator pedal Brake pedal Clutch pedal Steering wheel Notes Time elapsed since the start of the trial The X position of the interactive vehicle within the map of the simulated environment. The Y position of the interactive vehicle within the map of the simulated environment. The Z position of the interactive vehicle within the map of the simulated environment. Current speed of the interactive vehicle Distance travelled by participant relative to the start of the virtual road The distance of the centre of the interactive vehicle from the centre of the road The distance headway between the interactive vehicle and the back of any vehicle ahead. The time headway between the interactive vehicle and the back of any vehicle ahead. Current proportion of accelerator pedal depression. Current proportion of brake pedal depression. Current proportion of clutch pedal depression. Current angle of steering wheel rotation Version 2.0 15 PPR592

3.11 Visual behaviour In addition to the simulator data, a video of participants faces was recorded during the drive in order to analyse visual behaviour. Videos coding took place as follows. The coders skipped to the points in each smartphone drive where the automated voice instruction ended. From there, they watched the video at (at least) half speed, using a stopwatch to record: Total time spent looking down (this does not distinguish between looking at the phone or other parts of the in-vehicle environment) Total time spent looking up (ie at the road, mirrors, etc) Duration of interaction with phone (sum of the above) Smartphone interaction was assumed to finish if the participant looked up for at least five consecutive seconds. The same locations of each participant s control drive were also coded. 3.12 Questionnaire A post-trial questionnaire was also administered in order to record participants perceptions of their riving performance and opinions about mobile phone use while driving. 3.13 Calculation Data processing was conducted using Microsoft Excel 2007 and statistical analysis took place using SPSS 19.0. In all statistical tests p values of less than 0.05 were taken to be significant. Where shown, error bars indicate the 95% confidence interval of the mean. Version 2.0 16 PPR592

4 Results 4.1 Participants Twenty-eight participants took part in the study. Twelve were female and sixteen were male. Participants were aged between 18 and 26 years (M=22.4, SD = 2.32). Only participants who used Apple or Android phones with touchscreen keypads and who described themselves as being regular users of the Facebook application on their smartphone were selected. 4.2 Reaction time (RT) tasks During each drive in the simulator, participants were asked to respond to three auditory tones and a red bar visual stimulus by pressing the clutch pedal as quickly as possible. In the smartphone drive these stimuli coincided with social networking tasks: Auditory reaction time task 1 coincided with Read message 1 in section 1 Auditory reaction time task 2 coincided with Write message 2 in section 2 Auditory reaction time task 3 coincided with Update status in section 3 Visual reaction time task coincided with Write message 4 in section 4 In the control drive, participants were given no social networking tasks to coincide with the reaction time tasks. Thus, a comparison between the two drives was possible. 4.2.1 Response rate Data for 220 reaction time tests were collected; 112 from participants control drives and 108 from their smartphone drives. Participants failed to respond to three of these tests during the control drive, and 10 of the tests during the smartphone drive. These differences are statistically significant (Fisher s exact; p=.047) suggesting that participants were more likely to fail to respond to reaction time stimuli while using their smartphone. In real driving scenarios this has implications for drivers responses to hazardous situations, and suggests that drivers may miss warnings or hazards. 4.2.2 Reaction times On occasions where participants did respond to the auditory or visual stimuli, their reaction times tended to be slower during the smartphone drives. Auditory 1 was triggered during a read message task, Auditory 2 during a write message task, Auditory 3 during a status update task and the Visual stimulus during a write message task. Version 2.0 17 PPR592

Reaction time (sec) 2.6 2.4 2.2 2 1.8 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0 Auditory 1 Visual Control Smartphone Figure 6: Mean reaction times Paired samples t-test comparisons show that differences were statistically significant for two pairs of the reaction time tests: Auditory 1 (t(24) = -2.242, p=.035) and Visual (t(22) = -3.478, p-.002). Figure 6 illustrates these differences. These results suggest that using a smartphone for social networking while driving can slow drivers responses to events taking place on the road. The differences in reaction times between the two drives were not statistically significant for Auditory 2 or 3. 4.3 Analyses of driving performance while using a smartphone Participants were asked to interact with their phone on seven separate occasions during their social networking drive. On four occasions they were asked to write a pre-defined message, they were instructed to read messages on two occasions and they were also asked to update their Facebook status. As participants drove the same route in their control drives, it was possible to compare driving performance in the sections where participants were interacting with their smartphone, with driving performance at the exact same locations in the control drive. Four key measures of driving performance were taken: Mean speed Standard deviation of speed (an indication of how variable speed was) Standard deviation of lane position (an indication of variability of lateral position) Maximum speed For one section of the route, in which participants wrote their third message while following a lead vehicle, the time headway between the driven vehicle and the lead vehicle was also calculated. The instructions to interact with their smartphone were pre-recorded and played at exactly the same point along the route for each participant. As it was difficult to ascertain the precise point at which participants started and finished using their phone, the following assumptions were made to define the sections for analysis: Version 2.0 18 PPR592

Smartphone interaction was assumed to start at the moment the pre-recorded instruction ended Smartphone interaction was assumed to end if participants looked up at the road for an uninterrupted duration of at least 5 seconds Statistically significant differences in driving performance are described below. 4.3.1 Mean speeds Participants were instructed to write their first and fourth messages while driving in light traffic along a motorway, their second message while following a series of curves at 40mph and their third message while following a lead vehicle at a constant distance. Figure 7 shows participants mean speeds during these smartphone episodes. 90 80 Mean speed (mph) 70 60 50 40 30 20 10 Control Smartphone 1st written 2nd written 3rd written 4th written Figure 7: Mean speeds during smartphone episodes On average participants drove more slowly when writing messages on their smartphone while driving (Write 1: t(23)=2.238, p=.035; Write 2: t(22)=2.626, p=.015; Write 3: t(26)=2.134, p=.042; Write 4: t(25)=2.057, p=.05). This is consistent with previous research which suggests that participants slow down in order to compensate for the perceived impairment of using a phone while driving (see Chapter 2). The difference in mean speeds for the two drives were not statistically significant for the message reading or status update tasks. 4.3.2 Standard deviation of speed Participants speed varied more in two sections of the smartphone drive when compared with the control drive. These were while writing their fourth message (on a motorway in light traffic) and updating their Facebook status (while following a lead vehicle whose speed varied). Figure 8 shows these differences. Version 2.0 19 PPR592

5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0.5.0 4th written Status update Control Smartphone Figure 8: Standard deviation of speed during smartphone episodes Paired samples t-tests confirmed that these results were statistically significant (Write 4: t(25)=-2.305, p=.03; Update status: t(26)=-2.736, p=.011), suggesting that participants were slowing down and speeding up more while using a smartphone. 4.3.3 Standard deviation of lane position Participants were asked to write their second message and read the second message sent to them while following a curved section of road. They were asked to drive at 40mph and in the centre of their lane. While carrying out these smartphone tasks their lane position varied more than in the same section of the control drive. The same happened when they were writing their third message while following a lead vehicle on a motorway. These differences are shown in Figure 9. Standard deviation of lane position.90.80.70.60.50.40.30.20.10.00 2nd written 3rd written 2nd read Control Smartphone Figure 9: Standard deviation of lane position during smartphone episodes Paired samples t-tests were conducted and the results showed that these differences were statistically significant (Write 2: t(22)=-3.598, p=.002; Write 3: t(26)=-4.539, Version 2.0 20 PPR592

p=.000; and read 2: t(26)=-5.114, p=.000). No significant differences were found for other smartphone tasks. Further calculations were carried out to investigate the consequences of participants poor lane-keeping. It was found that participants crossed over the lane markings 26 times while using their smartphone to write the second message, but kept within their lane at all times during their control drive. This is shown in Figure 10. Number of lane excursions 30 25 20 15 10 5 0 0 Control 26 excursions, 5 participants Smartphone Figure 10: Number of lane excursions during 2nd written message While writing their third message, participants crossed the lane markings four times, but did not cross the lane markings in the same section of the road during their control drive. They did not cross the lane markings at all while reading a message sent to them. The deterioration in lateral control is consistent with results from other studies (see Chapter 2) which have looked at the effects of distracted driving. This suggests that driving while using a smartphone to write messages could pose a significant threat to the safety of vehicles in adjacent lanes. 4.3.4 Maximum speed The maximum speed that participants reached during each smartphone episode was calculated but no significant differences emerged between these speeds and the highest speeds reached in the same sections of the control drive. 4.3.5 Time headway Calculations were also made to understand how well participants were able to follow the lead vehicle while writing their third message. The results showed no difference in mean time headway or the shortest time headway observed, but did show a difference in how variable participants time headway was (t(26)=-3.291, p=.003). Version 2.0 21 PPR592

3.00 Standard deviation of headway 2.50 2.00 1.50 1.00.50.00 Control Smartphone Figure 11: Standard deviation of time headway Figure 11 shows that time headway varied significantly more during the smartphone drive, suggesting that participants were less able to respond to its changing speed. This finding is consistent with the reaction time data, which also suggests poorer responses to stimuli in the environment. 4.3.6 Gender differences The sample of drivers tested contained an approximately equal number of males and females. Thus, it was possible to investigate the relative effects of the smartphone tasks on the driving performance of male and female participants. Repeated measures ANOVA tests were carried out, with gender as a between-subjects factor. In general, the data showed that there was a statistically significant difference between males and females in the following measurements: Standard deviation of time headway during the 3 rd written message (F(1,25)=4.909, p=.003) Mean speed for write 4 only (F(1,19)=4.762, p=.042) Standard deviation of lane position during write 2 (F(1,21)=4.372, p=.049), write 3 (F(1,21)=4.520, p=.046) and read 2 (F(1,25)=11.876, p=.002) Standard deviation of speed during update message (F(1,25)=10.357, p=.004) Key statistics in relation to these measures are presented in Table 10. Version 2.0 22 PPR592

Table 10: Performance of males and females on key measures of driving performance in each drive Females Males Measure Drive Section N Mean SD N Mean SD SD time headway SD time headway Smartphone Write message 3 12.97.56 16.48.27 Control Write message 3 11 1.92 1.86 16 1.14.76 Mean speed Smartphone Write message 4 11 71.22 3.10 16 74.49 5.05 Mean speed Control Write message 4 11 65.75 8.22 16 73.03 6.18 SD Lane Pos Smartphone Write message 2 10.29.07 14.25.06 SD Lane Pos Control Write message 2 9.63.44 14.40.10 SD Lane Pos Smartphone Write message 3 12.25.08 16.17.04 SD Lane Pos Control Write message 3 11.37.17 16.24.07 SD Lane Pos Smartphone Read message 2 12.33.09 16.26.08 SD Lane Pos Control Read message 2 11.47.14 16.30.07 SD Speed Smartphone Update message 12 2.42 1.09 16 1.69.93 SD Speed Control Update message 11 3.33 1.08 16 2.20.88 The results indicate that for these sections males tended to reduce their speed less, not vary their lateral position, distance from the lead vehicle or speed as much as females did. Thus, on these measures of performance, females did not perform as well as males. Differences between the driving performance of males and females were not statistically significant for any of the other measurements taken. An interaction effect of drive and gender was found for only one of these variables: standard deviation of lane position during the second read message task (F(1,25)=11.582, p=.002). This suggests that the smartphone task affected males differently to females, with females performance deteriorating more than that of males. 4.4 Visual behaviour A video recording of each participant was taken during the drive and this made it possible to observe their visual behaviour. Of particular interest were the sections of the smartphone drive in which participants were interacting with their phone, and the same locations in the control drive. The video was coded to compute the following variables for each smartphone episode: Total time spent looking down (this does not distinguish between looking at the phone or other parts of the in-vehicle environment) Total time spent looking up (ie at the road, mirrors, etc) Duration of interaction with phone (sum of the above) Version 2.0 23 PPR592

On average, participants spent 28.36 seconds per smartphone episode looking down when compared with an average 5.1 seconds looking down in the same sections of the control drive. Table 11 shows how this breaks down across all episodes of smartphone use and also gives the same information about the comparable sections of the control drive. Table 11: Amount of time (in seconds) spent looking down during the messaging section of the smartphone drive and equivalent section of the control drive (note: message completion times not equal) Drive Smartphone episode N Minimum Maximum Mean Std. Deviation Write 1 24 14.14 46.99 31.34 10.37 Smartphone Control Read 1 26 2.42 40.81 16.25 10.08 Write 2 27 5.90 77.91 30.73 14.76 Read 2 25 4.60 36.32 12.55 7.68 Update 27 20.02 92.54 41.55 14.97 Write 3 27 20.64 66.33 34.47 11.52 Write 4 27 15.90 119.52 30.40 20.28 Write 1 20.91 10.23 4.68 2.31 Read 1 19.00 8.74 3.50 2.75 Write 2 25.00 20.00 6.83 4.45 Read 2 27.00 13.65 3.07 3.19 Update 27.00 27.11 6.53 5.62 Write 3 27.00 14.54 4.59 3.17 Write 4 27.00 15.13 4.51 3.58 It can be seen that on average, participants spent longer looking down during sections of the smartphone drive. Figure 12 displays the time participants spent looking down as a percentage of the time they were interacting with the phone during that episode of phone use. This accounts for the fact that each smartphone episode lasted a different length of time. Version 2.0 24 PPR592