Mutual Interferences of Driving and Texting ...

Proceedings of the Human Factors and Ergonomics Society 58th Annual Meeting - 2014

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Mutual Interferences of Driving and Texting Performance Jibo He, Alex Chaparro, Joseph Crandall, Colton Turner, Kirsten Turner, and Jake Ellis Department of Psychology, Wichita State University, Wichita, KS

Not subject to U.S. copyright restrictions. DOI 10.1177/1541931214581452

Despite legislative and social campaigns to reduce texting while driving, drivers continue to text behind the wheel. There is abundant evidence demonstrating that texting while driving impairs driving performance. While past driver distraction research has focused on how texting influences driving, the influence of driving on texting behaviors is often ignored. This study used a classic Lane Change Task and a smartphone texting application to study the mutual influences of driving and texting. Results showed that concurrent texting impaired driving by increasing variability in lane position. Meanwhile, driving impaired texting by increasing the texting task completion time and texting errors, and reduced texting key entry speed. The finding of mutual interferences of driving and texting provides new knowledge for social campaigns, which intend to persuade drivers not to drive while texting, and importantly provides a scientific basis for the development of smartphone-based technology to reduce the risky behavior of driving while texting.

INTRODUCTION Texting while driving is a disturbingly common and dangerous behavior. The National Safety Council (2012) estimated that 281,000 to 786,000 crashes in 2012 involved text messaging. A survey by the American Automobile Association (2008) reported that 14.1% of all drivers and 48.5% of young drivers age 18 to 24 admitted that they text while driving. Concurrent texting impairs driving in various ways. For example, texting while driving increases hazard response time (Burge & Chaparro, 2012; Drews, Yazdani, Godfrey, Cooper, & Strayer, 2009; Leung, Croft, Jackson, Howard, & Mckenzie, 2012), increases lane deviations and lane excursions (Alosco et al., 2012; Crandall & Chaparro, 2012; Hosking, Young, & Regan, 2009; McKeever, Schultheis, Padmanaban, & Blasco, 2013; Rudin-Brown, Young, Patten, Lenné, & Ceci, 2013), increases mental demand (Owens, McLaughlin, & Sudweeks, 2011; Rudin-Brown et al., 2013), increases gaze-off-road durations (Hosking et al., 2009; Libby, Chaparro, & He, 2013; Owens et al., 2011), causes more collisions (Alosco et al., 2012; Drews et al., 2009), and raises the risk of an accident by 8 to 23 times (National Safety Council, 2012; Olson et al., 2009). However, how driving influences texting performance is less well understood. Despite the associated risk , drivers continue to text while behind the wheel. Drivers may read or send texts out of habit, regardless their subjective perception of the risks of such behaviors (Bayer & Campbell, 2012) and are more likely to take a phone call if they believe the call is important (Nelson, Atchley, & Little, 2009). The public safety concerns related to texting while driving has attracted the attention of legislators, automakers, and safety researchers. Researchers and practitioners have explored many approaches to reduce the incidence of texting while driving, including illegalization, social campaigns, and technological solutions. Legislation efforts have sought to discourage the behavior by making texting while driving

illegal. Social campaigns have sought to educate drivers about the risks of texting while driving. Information technology companies, cell phone manufacturers and carriers have developed technologies to mitigate the risks of texting while driving. AT&T and T-Mobile have developed smartphone applications to discourage texting while driving, such as Drive Mode and DriveSmart. Smartphone users can enable the ‘Drive Mode’ to reduce or eliminate incoming calls and messages and thereby reduce their exposures to texting while driving. A downside of these technologies is they require drivers to activate and deactivate the ‘Drive Mode’, which may reduce drivers’ willingness to use such applications. Despite these legislative, social and technological efforts and the associated risks, drivers continue to text while driving. As many as 91% of college students reported having sent text messages while driving, even though they agreed that texting while driving was dangerous and should be illegal (Atchley, Atwood, & Boulton, 2011; Harrison, 2011). Evidence for the effectiveness of legal prohibitions of texting while driving is mixed. McCartt et al. (2010) found that bans in the District of Columbia (D.C.), New York, and Connecticut reduced handheld phone use over time, however this could be partially due to an increase in the use of handsfree devices(Mccartt, Hellinga, Strouse, & Farmer, 2010). Evidence indicates that state anti-texting laws have little effect on the likelihood of a teenager to text while driving, with 39 percent of teens reporting texting in states where it is illegal compared to 44 percent of teens in states that have no restrictions (American Academy of Pediatrics, 2013). Despite legislative efforts to curb texting while driving, many drivers will continue to do so. The difficulty of legislation, social campaigns and technology innovations to reduce driver distraction highlights the continuing need for research and a well-rounded understanding of how texting influences driving and vice versa. According to the theories of dual-task performance (Levy, Pashler, & Boer, 2006; Pashler, 1994), when two tasks

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are carried out concurrently, the performances of one or both tasks may be impaired, causing a dual-task performance decrement. Although both tasks may be impaired, most driving studies focus on driving performance, while texting performance is mostly ignored or less emphasized. Smartphones allow researchers to collect detailed data on secondary task performance. In this study, we utilize the capability of smartphones to capture texting performance and describe the mutual interferences of texting and driving.. METHODS Participants Twenty college-age participants (10 men, 10 women, M = 22.50 years, SD = 4.99 years) from the community of Wichita State University volunteered to participate in the driving experiment. All participants were screened prior to participation to ensure normal or corrected-to-normal vision. All participants were right handed, active drivers, with at least 2 years of driving experience (M = 6.80 years; SD = 4.79 years). All owned a touch screen smartphone to ensure familiarity with smartphone operations. Participants reported sending 96 text messages per day on average (SD = 95). Apparatus and Stimuli Driving simulator. Driving performance was assessed using a driving simulator consisting of a car seat, a Logitech Driving Force GT steering wheel and pedals. The Lane Change Task version 1.2 simulated the driving task using a 60"-Sharp AQUOS 3D HD LCD display. Cell phone. A 4.3" HTC ThunderBolt touch-screen smartphone running the Android 2.3.4 operating system was used for the texting task. The buttons on the keyboard of the smartphone were arranged in a QWERTY layout. Experiment Tasks Lane change task (LCT). In the lane change task, participants traveled down a straight section of road with three lanes and were prompted to change lanes according to directions on signs, which appeared on both sides of the roadway. Participants maintained a constant speed of 60 km/h and were instructed to make lane changes as quickly and accurately as possible when they saw the lane change sign. The LCT was developed by Daimler Chrysler AG Research and Technology to test driver distraction (Hofmann, Rinkenauer, & Gude, 2008; Huemer & Vollrath, 2010). Texting task. A texting task was used to assess texting performance with two holding postures (texting with one hand vs. texting with two hands). An incoming text message sound was played at a random time interval, which followed a uniform distribution in the range of 40 to 60 seconds. Once the incoming sound was played, participants would open the texting application to send text messages. Participants were presented with a text message to repeat and instructed to enter the text message into the phone manually. The text message consisted of a 10-digit telephone number. The application saved time stamps, keyboard entries, and motions of the smartphone in log files for later analysis.

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Procedure Participants were informed of the purpose of the experiment and then signed a consent form to participate. They then completed a questionnaire regarding driving experience and cellular phone use, followed by a visual screening using the Snellen visual acuity chart to ensure they had normal or corrected-to-normal visual acuity. There were five counterbalanced experimental conditions, including a driving-only condition, two dual task conditions in which participants texting while driving with one hand (drive + text one hand) or two hands (drive + text two hands), and two texting-only conditions in which participants completed the texting task with one or two hands. The session began with instructions on how to complete the tasks and a practice session in order for participants to get accustomed to the simulator and the cell phone. Prior to beginning the lane change task, correct lane change action was demonstrated to participants by the researcher. The driver performed the lane change task to ensure correct execution of the task. Participants were instructed to prioritize the driving task during duel-task conditions and text when they felt comfortable or believed it was safe. The phone was used in portrait orientation for all texting conditions. In the two hand texting conditions, participants were instructed to keep both hands on the phone while texting. Participants practiced each of the text-only conditions for 2 minutes and each of the driving conditions for 6 minutes, for a total of 22-minutes of practice. After the practice, participants began the experimental trials. Each trial lasted about 12 minutes, with a total of 105 minutes for the whole experiment. Participants could quit the study at any time. After the study, participants were given course credits for their participation. Data Analysis Measures of driving performance included the mean and standard deviation of lane deviation. Lane deviation was the difference between the driver’s actual lane position (red line) and the “ideal model” of lane position (green line) for the lane change task, seen in Figure 1. A large value of the standard deviation of lane deviation indicates poorer lanekeeping performance and a greater risk of lane departure and collision with vehicles in the adjacent lanes. Driving performance variables were submitted to one-way analyses of variances (ANOVA) with driving conditions (drive-only, drive-one-hand, and drive-two-hands) as the only withinsubject factor. Texting performance was measured using the texting task completion time, key entry per second (KES), and texting errors. Texting errors were measured using the Levenshtein distance of two strings (simply termed as edit distance)(Levenshtein, 1966). The edit distance is the number of discrete steps required to make the strings identical, including insertions, deletions, switching and substitutions. Larger edit distances indicate larger differences between the two strings, or more texting errors. For example, the numbers 5200251314 and 200251319 have a Levenshtein distance of two. The pair of numbers can be made identical by inserting a



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5 at the beginning (insertion) and substituting the last 9 into 4 (substitution) for the later number. The messages participants entered were compared to the messages shown to them using the edit distance algorithm to measure texting errors. Texting performance variables were submitted to two-way analyses of variance (ANOVA) with task load (text-only vs. drive + texting) and texting methods (one hand vs. two hands) as within-subject factors.

Figure 3. Mean lane deviation across task conditions.

Figure 1. Simulator Output. The green line represents the ideal path defined by the computer and the red line represents the path taken by the driver. RESULTS Driving Performance The standard deviation of lane deviation (See Figure 2) was significantly different across task conditions, F (2, 38) = 19.43, p < .001, η2p = .51. The standard deviation of lane deviation in the drive-only condition (M = 1.04 m, SD = .19 m) was significantly smaller than that in the drive + texting with one hand (M = 1.14 m, SD = .25 m) and drive + texting with two hands conditions (M = 1.18 m, SD = .25 m), t (19) = -3.83, p = .001 and t (19) = -6.45, p < .001 respectively. The standard deviation of lane deviation in the drive + texting one hand condition was also moderately smaller than that in the drive + texting two hands condition, t (19) = -1.96, p =.06. The mean lane deviation (Figure 3) also differed significantly across task conditions, F (2, 38) = 24.18, p < .001, η2p = .56. The mean lane deviation in the drive-only condition (M= .77 m, SD= .15 m) was significantly smaller than that in the drive + texting with one hand (M= .89 m, SD= .21 m), and drive + texting with two hands conditions (M= .92 m, SD= .22 m), t (19) = -5.52, p < .001 and t (19) = -5.76, p < .001 respectively. The mean lane deviation in the drive + texting with one hand did not differ from the drive + texting with two hands condition, t (19) = -1.42, p = .17.

Texting Performance The texting task completion time (Figure 4) produced a significant main effect of task load, F (1, 19) = 130.44, p < .001, η2p = .87, with longer completion time in the drive + texting conditions (texting using either one hand or two hands) (M = 17.11 s, SD = 4.01 s) than the texting-only conditions (M = 9.56 s, SD = 2.27 s). But the main effect of texting methods and interaction were not significant, F (1, 19) = 0.07, p = .80, η2p = .004 and F (1, 19) = 2.39, p = .14, η2p = .11 respectively. The texting errors (Figure 5) also differed significantly across task load conditions, F (1, 19) = 6.59, p < .02, η2p = .26. The texting errors in the texting – only condition (M = 0.12, SD = 0.12) was significantly smaller than that of the drive + texting condition (M = 0.54, SD = 0.77). The main effect of texting methods and interaction effect were not significant, F (1, 19) = .26, p = .28, η2p = .06 and F (1, 19) = 1.19, p =.29, η2p = .06 respectively. Analysis of key entries per second (KES) produced a significant interaction effect of task load and texting methods, F (1, 19) = 9.43, p = .01, η2p = .33. The main effect of task load was significant, F (1, 19) = 150.99, p < .001, η2p = .89. Simple main effect showed that the KES in the texting – only conditions (M =1.18, SD = 0.22) was significantly larger than that of drive + texting conditions (M =0.66, SD = 0.16), t (19) = 12.29, p < .001. The main effect of texting methods was not significant, F (1, 19) = 1.91, p = .18, η2p = .09.

Figure 4. Texting task completion time across conditions.

Figure 2. Standard deviation of lane deviation across task conditions. Downloaded from pro.sagepub.com by guest on October 28, 2015


Figure 5. The texting errors measured using Levenshtein edit distance (Levenshtein, 1966).

Figure 6. Key entry per second (KES) across conditions. Detect driving while texting. To reduce the risks of texting while driving, it is important to detect the occurrence of such behavior. In secondary analysis, we used a logistic regression to investigate whether it is possible to identify changes in texting behavior that would indicate the driver is texting while driving. The variables for the logistic regression analysis included the texting task completion time, texting errors and KES. The dependent variable was the texting conditions, drive + texting or texting - only. A total of 1748 texting instances were analyzed. The best logistic regression model was as follows: Task load = 2.188 -2223.697 ×KES

Drivers may receive many text messages while on the road, but one of the most important messages they are ignoring is that they should not text while driving. Texting can increase their crash risk and endanger their lives. Drivers may continue to text for a variety of reasons. They may send texts to reduce unpleasant emotions (Feldman, Greeson, Renna, & Robbins-Monteith, 2011) or to build and maintain social relationships by replying promptly to a text message. Understanding how driving impacts texting behaviors can provide valuable insights that can be used to develop smartphone-based technology to prevent texting while driving. A preliminary logistic regression analysis showed that texting behavioral data, such as, task completion task, key entry per second, and texting errors could be used to distinguish texting while driving with high accuracy. Automatic detection of texting while driving can improve existing “Drive Mode” applications. If smartphone applications incorporate automatic detection of texting while driving and stop or delay messages from distracting drivers, these applications can be more effective in reducing texting while driving than existing applications, which require drivers to activate it before starting to drive. Future studies shall investigate whether other information including driving dynamics can be used to develop the smartphone-based technology to detect texting while driving. For example, GPS information can be used to calculate travelling velocity, which could then be used as a precondition for identifying whether the user is texting while driving. The smartphone application will detect texting while driving only when users travel at a high speed (such as 20 to 75 mph), which would greatly improve the performance of the application. A better understanding of texting and driving behavior combined with the rapid advancement of smartphone technology, offers new opportunities to develop technological solutions that may make driving safer (Ren, Wang, & He, 2013; He, Fields, Peng, Cielocha, Coltea, & Roberson, 2013). The potential has been previously recognized, just as Dr. Adesman stated, "Technological solutions will likely need to be developed to significantly reduce the frequency of texting while driving." (American Academy of Pediatrics, 2013; Lindqvist & Hong, 2011).

Where for task load, 1 indicates texting - only condition and 2 indicates drive + texting condition. An accuracy of 82.3% was achieved using texting behavior data to classify texting while driving from the texting - only condition. The high accuracy suggests it is possible to develop a smartphone application to detect texting while driving using texting behavioral data, such as KES. DISCUSSION Using the simulated Lane Change Task and smartphone technology, we found evidence of mutual interference between driving and texting. Texting impaired driving by increasing lane deviations; driving impaired texting by slowing text entry and increasing texting errors.

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