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Purpose – The purpose of this paper is to investigate the influence of driver demographic characteristics on the driving safety involving cell phone usages. Design/methodology/approach – A total of 1,432 crashes and 19,714 baselines were collected for the Strategic Highway Research Program 2 naturalistic driving research. The authors used a case-control approach to estimate the prevalence and the population attributable risk percentage. The mixed logistic regression model is used to evaluate the correlation between different driver demographic characteristics (age, driving experience or their combination) and the crash risk regarding cell phone engagements, as well as the correlation among the likelihood of the cell phone engagement during the driving, multiple driver demographic characteristics (gender, age and driving experience) and environment conditions. Findings – Senior drivers face an extremely high crash risk when distracted by cell phone during driving, but they are not involved in crashes at a large scale. On the contrary, cell phone usages account for a far larger percentage of total crashes for young drivers. Similarly, experienced drivers and experienced-middle-aged drivers seem less likely to be impacted by the cell phone while driving, and cell phone engagements are attributed to a lower percentage of total crashes for them. Furthermore, experienced, senior or male drivers are less likely to engage in cell phone-related secondary tasks while driving. Originality/value – The results provide support to guide countermeasures and vehicle design. Keywords Prevalence, Distracted driving related to cell phones, Driver demographic characteristics, Naturalistic driving study, Population attributable risk percentage Paper type Research paper © Haotian Cao, Zhenghao Zhang, Xiaolin Song, Hong Wang, Mingjun Li, Song Zhao and Jianqiang Wang. Published in Journal of Intelligent and 1. Introduction Connected Vehicles. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) National Highway Traffic Safety Administration (NHTSA) licence. Anyone may reproduce, distribute, translate and create derivative reported that 3,450 people in the USA were killed in works of this article (for both commercial and non-commercial purposes), distraction-related crashes in 2016, and 444 (14 per cent) subject to full attribution to the original publication and authors. The full fatalities occurred in crashes with at least one of the drivers terms of this licence may be seen at http://creativecommons.org/licences/ involved in cell phone usages at the time of the crash. by/4.0/legalcode Consequently, using cell phones while driving increases the risk This work is supported in part by the Joint Laboratory for Internet of Vehicles, Ministry of Education-China Mobile Communications Corporation under Grant ICV-KF2018-01 and in part by the National Natural Science Foundation of China under Grant 51975194 and 51905161. Thecurrent issueand full text archiveofthisjournal is availableon We would like to thank the Transportation Research Board of the National Emerald Insight at: https://www.emerald.com/insight/2399-9802.htm Academies of Science (USA) for providing the SHRP2 Insight dataset. Findings and conclusions of this paper are those of authors and do not necessarily represent views of the SHRP 2, the Transportation Research Board, or the National Academies (USA). Journal of Intelligent and Connected Vehicles 3/1 (2020) 1–16 Received 28 October 2019 Emerald Publishing Limited [ISSN 2399-9802] [DOI 10.1108/JICV-10-2019-0012] Accepted 23 November 2019 1 SHRP 2 naturalistic driving data Journal of Intelligent and Connected Vehicles Haotian Cao et al. Volume 3 · Number 1 · 2020 · 1–16 of crash fatalities and injuries and continues to be a widely Nevertheless, we noticed that the most previous research was concerned issue related to public safety. Moreover, NHTSA mainly focusing on the individual level (e.g. using odds ratio reported that 13 per cent of drivers distracted by cell phones in [OR]) (Dingus et al., 2016; Klauer et al.,2014). Considering fatal crashes were aged 15-19 years, while drivers aged 20-29 OR can only describe the odds that a crash will occur given a represented 35 per cent of fatalities (National Highway Traffic particular exposure and compared to the odds of the crash Safety Administration, 2018). Thus, from the point of occurring in the absence of that exposure, while the crash risk improving driving safety, it is worthwhile to investigate the difference between different groups cannot be directly relationship between the risk posed by using cell phones while presented. What is more, the risk level of one secondary task driving and driver demographic characteristics. engagement among the whole population is unclear while only According to a report in the USA, 542,000 drivers of cars have ORs reported. Other limitations of previous studies might were observed talking on cell phones during one day, equating include small sample size (Chaparro et al.,2005; Merat et al., to 3.8 per cent of American drivers (Pickrell et al.,2016), using 2005; Tractinsky et al.,2013), which the occurrence rate on a cell phones while driving is rather common. The Harris population basis was not considered, thereby missing the Group’spoll(Harris Interactive, 2011) of representative opportunity for a quantitative description of the driving risk for samples of the adult American population showed that despite a particular population. While some of the studies only 47 per cent of respondents believing that using cell phone while investigated in a single factor effect (e.g. age or driving driving was “very risky”, nearly 60 per cent of the group still experience) on driving performance involved with distractions engaged in cell phone usage. Meanwhile, it is important to (Guo et al.,2017; Kass et al., 2007; Klauer et al.,2014). know how often drivers are likely to engage in cell phone- Though there were some studies investigating the prevalence of related tasks while driving and which kind of group of drivers distracting related behaviors by roadside observation. was more likely to be affected by cell phone activities, such that However, considering the roadside observation, a stationary policymakers might get benefits from the results and more observer simply records the activities and demographic effective countermeasures could be developed. What is more, characteristics (e.g. gender, age) of drivers as they pass a many investigations have indicated that using cell phones while selected location (Ranney, 2008). Roadside observational driving negatively affects various aspects of driving studies did not include a range of traffic and roadway performances, such as lane-keeping (McKeever et al., 2013; conditions that occur during the driving. As a result, observing Rudin-Brown et al., 2013), speed selection (Metz et al.,2015; sites were often limited, leading to an inaccurate assessment of Reimer et al.,2014; Tractinsky et al.,2013), car-following the prevalence of distractions (Johnson et al.,2004; Sullman, distance (Caird et al., 2014; He et al.,2014; Oviedo- 2012). On the contrary, naturalistic observation assesses the Trespalacios et al., 2017), visual scanning (Caird et al., 2014; driving behavior of drivers over a period of time using Collet et al.,2010; Oviedo-Trespalacios et al., 2017), response instrumented vehicles. Thus, naturalistic data were collected time in avoiding road hazards (Caird et al.,2018; Lipovac et al., over an extended period and represent normal, daily driving 2017;Oviedo-Trespalacios et al., 2017), and even the risk of that occurs in a metropolitan environment, and the sample crashing (Dingus et al.,2016; Simmons et al., 2016; Simons- sizes were large enough. Consequently, the prevalence Morton et al.,2014). It was also found that using a cell phone estimates would be more reasonable. Therefore, this study is while driving was likely to be influenced by driver demographic based on a massive naturalistic driving database – the Strategic characteristics such as drivers’ age and driving experience, but Highway Research Program 2 (SHRP 2) – which aims to not by gender. For instance, Zhao et al. (2013) demonstrated evaluate the correlation between different driver demographic that gender was not a significant factor in mobile phone characteristics (age, driving experience or their combinations) distracted driving, based on a quasi-naturalistic study of 108 and the crash risk regarding cell phone engagements, as well as participants. Guo et al. (2017) also found that driver’s gender the correlation among the likelihood of the cell phone did not significantly affect the safety of driving. Additionally, it engagement, multiple driver demographic characteristics was found that older drivers had worse driving performance (gender, age and driving experience) and environment compared to young drivers when engaged in dual-tasking conditions by using the mixed logistic regression model. We (Asbridge et al., 2013; Chaparro et al.,2005). Guo et al. (2017) also use the population attributable risk (PAR) percentage and are based on a massive naturalistic driving data set to point out the prevalence (P ) to quantitatively investigate the crash risk that the influence of secondary tasks on driving safety varied a because of cell phone engagements during the driving. lot because of the driver’s age and the nature of tasks. The study concluded that teenagers and young adults bare a higher risk 2. Materials and methods than medium-aged drivers when undertaking one secondary The SHRP 2 Naturalistic Driving Study, which is the largest task while driving. As for the driving experience, Tractinsky et al. (2013) found that younger drivers were more influenced naturalistic driving-behavior study to date, monitored over by phone conversations than middle-aged or experienced 3,500 participants and produced 2PB of their day-to-day drivers. Moreover, Klauer et al. (2014) also identified that the naturalistic driving data from 2010 to 2013. The data were risk of crashing because of secondary-task engagement for collected from six sites in the USA: Seattle, WA; Tampa, FL; Buffalo, NY; Durham, NC; State College, PA; and novice drivers was higher than for experienced drivers. Bloomington, IN. The naturalistic driving data were obtained Choudhary and Velaga (2019) showed that young drivers manage to compensate for less steering reversals and show using the data acquisition system (DAS) from key-on to key-off higher variations in lane positioning compared to professional for every trip. The onboard DAS collected four video messages drivers. (i.e. driver’s face, driver’s hand, front road and rear road), 2 SHRP 2 naturalistic driving data Journal of Intelligent and Connected Vehicles Haotian Cao et al. Volume 3 · Number 1 · 2020 · 1–16 vehicle network information (i.e. speed, brake and accelerator as shown in Table II, are analyzed. Finally, a data set position) and additional sensors from the DAS (including comprising 21,146 epochs was collected from 3,302 vehicles global positioning system, forward radar and accelerometer). with 3,481 drivers in 23,697 trips, which were recorded in the The Technical Coordination and Quality Control were SHRP 2 database. Table III shows the numbers of crashes and conducted by the Virginia Tech Transportation Institute at baseline epochs in which each behavior was present and in Virginia Polytechnic Institute and State University (Dingus which each was absent. et al., 2015). One way to provide researchers with access to A mixed effect logistic regression model with a driver-specific SHRP 2 data is the SHRP 2 Insight website (https://insight. random effect was used to examine the influence of driver shrp2nds.us/), which allows researchers outside of secure demographic characteristics (age, driving experience or their facilities to browse de-identified driving data and build queries combinations) on the driving safety consequence because of to search what they are interested in. cell phone usages. Moreover, another similar mixed effect Participants comprised approximately half female (1,818) logistic regression model was performed to examine whether and half male (1,662) drivers. Their ages ranged from 16 to 98 driver demographic characteristics (gender, age and driving and were categorized as young drivers (16-24 years old), experience) and environment condition variables (weather, middle-aged drivers (25-59 years old) and senior drivers (>60 light, traffic density and time of the day) were correlated with years old) in this study. Additionally, drivers were divided into the likelihood of the cell phone engagement during the driving. two groups based on their driving experience, namely, less- If the interaction effect is significant at 0.05 level, then the experienced drivers (10 years) and experienced drivers (>10 estimated marginal mean (EMM) of the interaction effect years). Instead of gathering data using a questionnaire after the respecting to the crash risk or the likelihood of the cell phone accident, the SHRP 2 driving study directly records the driver’s engagement would be obtained by using the professional behavior and secondary task engagements before a crash statistical software IBM SPSS Statistics©. We also use the PAR occurred. According to Dingus et al. (2015), more than 1,500 percentage to describe the percentage of one group’s total risk possible crash events were identified in the data set through the which is in excess of the risk among persons not exposed to the automatic crash notification algorithms on the DAS. If the suspect factor (Cole and Macmahon, 1971). In our case, it vehicle status parameter was greater than the threshold value provides an assessment of the percentage of crashes occurring (e.g. breaking at more than 65 gravitational units), it was in the population at-large that are directly attributable to the identified as a crash event. Once the event was identified, it was specific behavior measured. Please follow Appendix to get verified via video reviews. The definition of a crash was any detailed descriptions of statistical methods used in this study, contact between the vehicle and another object. Crash which includes the regression modeling and the calculation of severities were defined as one of the following four levels as the PAR percentage. shown in Table I. A case-cohort approach was used to evaluate the crash risk 3. Result analysis caused by secondary task engagements. The controls were short segments of non-safety critical events and comprised 3.1 Analysis of crash risk normal driving episodes, which were used to represent the According to mixed regression model results, differences of age exposure of risk factors under normal driving conditions. That (P < 0.05), driving experience (P < 0.05), as well as is, random sampling control segments, which are composed of the combination of the age and driving experience (P < 0.05) 6-s periods, a comparable length of time for determining the are statistically significant for the crash risk when regarding the exposure to one secondary-task engagement for crashes, was overall cell phone use in driving, but not for gender. What is used to represent normal driving conditions when the vehicle more, Tables IV and V, as well as depicted in Figures 1, 2 velocity was larger than 5 mph (8.05 km/h). For each driver, the and 3, further show the PAR percentage (with corresponding number of control segments was directly proportionate to the 95 per cent confidence intervals) and prevalence of each cell number of miles or the number of driving hours. A secondary phone-related secondary task by the age group, driving task was coded if it occurred during the control segment. Thus, experience group or their combinations. the random sampling control segments also provided an As depicted in Figure 4(a), EMMs of the crash risk opportunity to calculate the prevalence of one secondary task associated with the overall cell phone use is 12.80 per cent for (Guo and Hankey, 2009). What is more, considering the the young, 6.60 per cent for the middle-aged and 14.30 per research purpose of this study, only secondary tasks associated cent for the senior, as illustrated in Figure 2(a). The results with cell phone usages, which includes seven specific subtasks, indicate that, compared with middle-aged drivers, young drivers would confront higher crash risks when they are Table I The levels of crash severities involved in any cell phone-related engagements while driving, and their difference of the EMM of crash risk is 6.20 per cent Level Severities (P < 0.001). What is more, senior drivers might face higher 1 Airbag/injury/rollover, high delta-V crashes (virtually all crash risks than middle-aged drivers when engaging in cell would be police reported) phones, as their corresponding difference of the EMM of crash 2 Police-reportable crashes (including police-reported crashes, risk is 7.70 per cent, which is marginal (P = 0.06), while no as well as others of similar severity which were not reported) significant difference is reported between the young and the 3 Crashes involving physical contact with another object senior. In addition, according to Table AI, ORs were extremely 4 Tire strike; low-risk crashes high for most specific activities related to the cell phone for Source: Dingus et al. (2015) senior drivers, such as cell phone texting, holding and dialing 3 SHRP 2 naturalistic driving data Journal of Intelligent and Connected Vehicles Haotian Cao et al. Volume 3 · Number 1 · 2020 · 1–16 Table II Each specific cell phone task and its definition Cell phone-related task Definition in SHRP 2 Overall cell phone use All secondary tasks related to cell engagements during driving Cell phone visual- Including dialing hand-held, locating/reaching/answering, texting manual task Dialing hand-held The subject driver is pushing a number buttons on a cell phone or touch screen to dial/browse/check something else on their phone; the subject driver is holding a phone but not manipulating it Holding The subject driver is holding a phone but not manipulating it Locating/reaching/ The subject driver is glancing at find phone, reaching toward his/her cell phone and flipping phone open or pressing a button to answering answer a call Texting The subject driver is pressing buttons on cell phone or touch screen to create and/or send a text message Browsing The subject driver is pressing buttons on cell phone or touch screen to browse the internet or phone applications Talking/listening, hand- The subject driver is talking on a handheld phone or has a phone up to ear as if listening to a phone conversation or waiting for the held person they are calling to pick up the phone confront higher crash risks when they were distracted by cell Table III The numbers of crashes and baseline epochs where each cell phones, the difference of the EMM of crash risk is 4.80 per cent phone-related task was present and absent (P < 0.01). Furthermore, as shown in Table IV and Figure 2,it Present Absent is interesting to observe that the point estimate of PAR Secondary task Baseline Crash Baseline Crash percentage regarding the overall cell phone use for experienced Overall cell phone use 1,639 186 18,075 1,246 drivers (PAR per cent 0.93) is much lower than that of less- Cell visual-manual 517 87 19,197 1,345 experienced drivers (PAR per cent 7.07), as well as for each Browsing 164 16 19,550 1,416 subtask. That indicates cell phone use contributes to a much Dialing handheld 24 8 19,690 1,424 lower percentage of total crashes for experienced drivers when Holding 430 95 19,284 1,337 compared with less-experienced drivers. Locating/reaching 123 21 19,591 1,411 To investigate whether the combined effect of the age and Talking/listening, hand-held 631 42 19,083 1,390 driving experience regarding the safety impact would be Texting 385 65 19,329 1,367 significantly associated with the cell phone, drivers who are middle-aged, as well as have over 10 years’ driving experience are further categorized as one group, while rest of handheld. Nevertheless, point estimates of the PAR percentage drivers are treated as the other one. And we found that the showed in Table IV,aswellasin Figure 1, indicate that the interaction effect of the combination of the age and driving overall cell phone usage only accounts for a smaller percentage experience is statistically significant (P < 0.05) with the overall of total crashes for senior drivers (PAR per cent 1.86) or cell phone use, cell phone visual-manual tasks, as well as middle-aged drivers (PAR per cent 1.39) when compared with subtasks including the cell phone holding and cell phone texting that of young drivers (PAR per cent 7.73). when regarding the crash risk. As depicted in Figure 4(a); As shown in Figure 4(a), EMMs of the crash risk regarding EMMs of the crash risk for those two groups are 12.10 and 6.20 the overall cell phone use for less-experienced and experienced per cent. More specifically, experienced-middle-aged drivers drivers are 12.00 and 7.20 per cent, respectively. Compared engaging in any cell phone-related tasks would confront lower with experienced drivers, less-experienced drivers would crash risks than other drivers; the difference of the EMM Figure 1 PAR percentage and prevalence analysis results of the age 4 SHRP 2 naturalistic driving data Journal of Intelligent and Connected Vehicles Haotian Cao et al. Volume 3 · Number 1 · 2020 · 1–16 Figure 2 PAR percentage and prevalence analysis results of the driving experience Figure 3 PAR percentage and prevalence analysis results of the combination of age and driving experience regarding the crash risk is 5.90 per cent (P < 0.001). In engaged in cell phone visual-manual demand tasks would addition, as shown in Table IV and Figure 3, the point estimate also face lower crash risks than other drivers; the of PAR percentage regarding the overall cell phone use for difference of the EMM regarding the crash risk is 9.90 per experienced-middle-aged drivers is much lower than that of cent (16.70 vs 6.80 per cent, P < 0.001). And point other drivers (PAR per cent: 0.87 vs 6.15), which indicates the estimates of PAR percentages regarding this task for percentage of all crashes that is attributable to cell phone usages experienced-middle-aged drivers are also higher than for experienced-middle-aged drivers is much lower than to those of other drivers (PAR per cent: 0.59 vs 4.25). other drivers. The interaction effect regarding the crash risk between the What is more, there are some other interesting findings cell phone holding and age is statistically significant (P < regarding cell phone use, which are listed as follows: 0.05). More specifically, young and senior drivers are The interaction effect between cell phone visual-manual more adversely impacted by holding a cell phone than demanded tasks and age groups is statistically significant middle-aged drivers, as shown in Figure 4(c); EMMs of for safety impacts (P < 0.05). As illustrated in Figure 4(b), the crash risk for three age groups are 21.10, 10.20 and EMMs of the crash risk regarding cell phone visual- 36.40 per cent. Differences of the EMM between the manual tasks for three age groups are 16.70, 8.40 and young and the middle-aged is 11.00 per cent (P < 0.01), 27.30 per cent. Young and senior drivers confront higher and that between the senior and the middle-aged is 26.20 crash risks than middle-aged drivers when engaging in cell per cent (P < 0.05); however, no significant difference phone visual-manual related tasks. Their differences in the between the young and the senior is reported. In addition, EMM of crash risk for young and middle-aged drivers, the PAR percentage of cell phone holding is the largest senior and middle-aged drivers are 8.30 per cent (P < among all cell phone-related engagements for each group. 0.01) and 18.90 per cent (P < 0.05), respectively, while The point estimate of the PAR percentage varies among the difference between the young and the senior does not 2.19-6.74, especially the cell phone holding contributes to differ much. Further, experienced-middle-aged drivers a relatively large percentage of total crashes for some 5 SHRP 2 naturalistic driving data Journal of Intelligent and Connected Vehicles Haotian Cao et al. Volume 3 · Number 1 · 2020 · 1–16 Figure 4 EMMs of the crash risk regarding the cell phone use (significance level: P < 0.001, P< 0.01, P < 0.05) groups (e.g. 6.74 of young drivers and 6.08 of less- Again, that indicates cell phone talking/listening hand- experienced drivers). Thus, holding a cell phone needs to held also contributes to very few percentages of total be paid more attention to their daily driving. crashes for all drivers except the senior. As depicted in Figure 4(c), middle-aged-experienced drivers are easier to be impacted by cell phone holding and 3.2 Analysis of prevalence of engagements cell phone texting, differences of the EMM regarding the As shown in Table VI, the prevalence of being engaged in cell crash likelihood are 8.90 per cent (20.10 vs 11.20 per phone engagements during driving is 8.31 per cent. The cell phone cent, P < 0.05) and 10.30 per cent (16.70 vs 6.40 per talking/listening hand-held (3.20 per cent) is the most frequently cent, P < 0.01). Compared with experienced-middle-aged happened task, followed by the cell phone holding (2.18 per cent) drivers, point estimates of PAR percentages regarding and the cell phone texting (1.95 per cent). Remaining prevalence of these two types of distractions account for a larger distractions associated with the cell phone, which includes the cell percentage of total crashes for non-experienced-middle- phone browsing (0.83 per cent), cell phone locating/reaching/ aged drivers (PAR per cent of cell phone holding: 2.63 vs answering (0.62 per cent) and cell phone dialing hand-held (0.12 4.99; cell phone texting: 0.22 vs 3.20). Besides, PAR per cent), were observed less than 1.0 per cent. Mixed logistic percentages of cell phone talking/listening handheld for regression models were performed to examine whether driver both groups of drivers are NA. That indicates this demographic characteristics (gender, age and driving distraction task only accounts for very few percentages of experience), as well as environmental conditions (light, traffic total crashes for all drivers. density, weather and time of the day), were statistically significant As shown in Table IV, PAR percentages with respect to for various cell phone engagements while driving. As a result, the cell phone dialing hand-held stay low as all groups of most environment variables did not appear to be related to cell drivers scarcely dial hand-held while driving, which means phone engagements, except for the weather condition (P < 0.05). it only contributes to a rather low percentage of total crashes for all drivers. Thus, only demographic variables and weather conditions were According to Table V, cell phone talking/listening hand- considered in the regression model. According to analysis results, gender did not appear to be held has a relatively high prevalence which varies within related to most types of cell phone engagements while driving, 1.75 4.38 per cent for all groups of drivers except the senior; however, as shown in Table IV, corresponding except for the overall cell phone use (P < 0.05) and cell PAR percentages for all drivers except the senior are NA. phone talking/listening hand-held (P < 0.001). As shown in 6 SHRP 2 naturalistic driving data Journal of Intelligent and Connected Vehicles Haotian Cao et al. Volume 3 · Number 1 · 2020 · 1–16 Table IV The PAR percentage (with 95% CI) of cell phone use by driver demographics Young Middle-aged Senior Secondary task PAR% (CI) PAR% (CI) PAR% (CI) Overall cell phonecell phone use 7.73 (4.35,11.59) 1.39 (1.53,5.35) 1.86 (0.40,4.54) Cell phoneCell phone visual-manual tasks 5.22 (2.98,8.03) 1.56 (0.06,4.22) 1.38 (0.36,3.92) Holding 6.74 (4.40,9.66) 2.34 (0.58,5.13) 2.01 (0.70,5.05) Texting 4.49 ( 2.48,7.09) 0.35 (0.01,2.50) 0.68 (0.07,2.93) Locating/Reaching/Answering 1.36 (0.36,3.02) 0.55 (0.13,2.41) NA Dialing Handheld 0.20 (0.04,1.32) 0.56 (0.01,2.41) 0.87 (0.07,7.92) Browsing 0.29 (0.45,1.44) NA 0.53 (0.06,6.25) Talking/Listening, hand-held NA NA NA Secondary task Less-experienced Experienced PAR% (CI) PAR% (CI) Overall cell phone use 7.07 (3.89,10.65) 0.93 (0.72,3.19) Cell phone visual-manual tasks 5.21 (3.11,7.82) 0.90 (0.01,2.45) Holding 6.08 (3.95,8.72) 2.19 (0.99,4.05) Texting 4.05 (2.22,6.40) 0.38 (0.25,1.67) Locating/reaching/answering 1.32 (0.39,2.84) 0.14 (0.17,1.16) Dialing handheld 0.56 (0.11,1.86) 0.31 (0,1.39) Browsing 0.38 (0.39,1.71) NA Talking/listening, hand-held NA NA Secondary task Non-middle-aged- experienced Middle-aged-experienced PAR% (CI) PAR% (CI) Overall cell phone use 6.15 (3.94,8.66) 0.87 (2.12,5.08) Cell phone visual-manual tasks 4.25 (2.75,6.11) 0.59 (0.79,3.33) Holding 4.99 (3.45,6.91) 2.63 (0.71,5.90) Texting 3.20 (1.91,4.86) 0.22 (0.79,2.63) Locating/reaching/answering 0.91 (0.28,1.94) 0.53 (0.17,2.78) Dialing handheld 0.63 (0.21,1.70) NA Browsing 0.42 (0.13,1.36) NA Talking/listening, hand-held NA NA Notes: CI: Confidence interval; PAR%: population attributable risk percentage; NA indicates that the odds ratios of specific cell task for this individual subgroup are less than 1.0 or prevalence of specific cell task for this individual subgroup is null Figure 5(a) and (b), being female was related to an young drivers and middle-aged drivers is 12.00 per cent approximately 18 per cent increase in the probability of (P < 0.001). observed using a cell phone when compared with male drivers; Young and middle-aged drivers also tend to engage in cell phone visual-manual demanded tasks more frequently than the difference of the EMM regarding overall cell phone use is 1.20 per cent (8.80 vs 7.60 per cent, P <0.05). The same pattern can senior drivers, with the increased odds of engaging in this type be observed for talking/listening hand-held, with the increased of distraction being about 1.28 times for young drivers and odds of engaging in this task is about 33 per cent for female almost 76 per cent for middle-aged drivers. As shown in drivers; the difference of the EMM regarding cell phone talking/ Figure 5(c), EMMs of the likelihood of cell phone engagements listening hand-held is 1.60 per cent compared to male drivers for three age groups is 7.90 per cent, 6.20 per cent, and 3.60 per (6.90 per cent for females vs 5.30 per cent for males, P <0.001). cent, and the difference of the EMM between young drivers Age factor has a significant contribution to the likelihood of cell and senior drivers is 4.30 per cent (P <0.001), while that phone engagements for all cell phone-related secondary tasks between the middle-aged and the senior is 2.60 per cent except the cell phone dialing hand-held, cell phone locating/reaching/ (P < 0.001). Moreover: answering and cell phone browsing. In those cases, compared with A similar pattern was also found in talking/listening young and middle-aged drivers, senior drivers were less likely to handheld, with the increased odds of engaging in this engage in any cell phone-related tasks (P < 0.001), more behavior is about 81 per cent for young drivers and nearly 125 per cent for middle-age drivers. As shown in specifically, young drivers were associated with a 7.6 times increase in the odds of using a cell phone, and middle-aged Figure 5(b), EMMs regarding this behavior for three drivers were related to a 6.4 times increase. As shown in age groups are 6.80 per cent, 8.30 per cent, and 3.90 per Figure 5(a), EMMs of the likelihood of cell phone engagements cent. The difference of the EMM between the young for three age groups are 16.20 per cent, 14.20 per cent, and 2.20 and the senior is 2.90 per cent (P <0.01), while that per cent. The difference of the EMM between young drivers and between the middle-aged and the senior is 4.40 per cent senior driversis14.00 percent(P <0.001), while that between (P <0.001). 7 SHRP 2 naturalistic driving data Journal of Intelligent and Connected Vehicles Haotian Cao et al. Volume 3 · Number 1 · 2020 · 1–16 Table V Prevalence of the cell phone usage by different driver demographics Young Middle-aged Senior Secondary task P (%) P (%) P (%) e e e Overall cell phone use 12.98 8.84 1.25 Cell phone visual-manual tasks 4.58 2.37 0.30 Talking/listening, hand-held 4.12 4.07 0.76 Holding 3.65 2.12 0.27 Texting 3.50 1.74 0.15 Browsing 1.45 0.77 0.08 Locating/reaching/answering 1.11 0.50 0.13 Dialing handheld 0.10 0.22 0.02 Secondary task Less-experienced Experienced P (%) P (%) e e Overall cell phone use 13.03 4.78 Cell phone visual-manual tasks 4.44 1.25 Talking/listening, hand-held 4.38 2.32 Holding 3.69 1.06 Texting 2.23 0.84 Browsing 1.43 0.38 Locating/reaching/answering 1.02 0.31 Dialing handheld 0.11 0.13 Secondary task Non-middle-aged- experienced Middle-aged-experienced P (%) P (%) e e Overall cell phone use 8.51 7.86 Cell phone visual-manual tasks 2.85 2.09 Talking/listening, hand-held 2.98 3.72 Holding 2.37 1.75 Texting 2.17 1.45 Browsing 0.91 0.66 Locating/reaching/answering 0.69 0.47 Dialing handheld 0.73 0.24 Table VI Mixed effect of logistic regression model results regarding the likelihood of cell phone engagements Overall cell phone Cell phone VM tasks Dialing handheld Holding Wald OR Wald OR Wald OR Wald OR Intercept 24.822 0.014 21.048 0.029 16.449 0.028 20.174 0.029 Gender (F.) 2.160 1.176 0.364 1.030 0.282 1.035 1.091 0.909 Age (Young) 11.119 8.645 4.062 2.284 0.237 1.072 2.650 1.719 Age (Mid.) 13.703 7.411 4.308 1.761 0.557 1.089 3.731 1.649 a a a a Age (Senior) 0 Ref. 0 Ref. 0 Ref. 0 Ref. Less-exp. 3.691 1.684 2.380 1.486 0.130 0.968 3.245 1.713 Wea. 2.096 1.250 0.836 1.125 0.174 1.036 1.026 1.166 L/R/A Texting Browsing T/L, hand-held Wald OR Wald OR Wald OR Wald OR Intercept 17.356 0.030 20.013 0.029 17.991 34.81 22.139 0.025 Gender (F.) 0.271 0.970 0.171 1.015 0.109 1.012 3.652 1.332 Age (Young) 1.413 1.481 2.672 1.781 1.135 1.332 3.330 1.813 Age (Mid.) 0.927 1.147 3.060 1.527 1.520 1.249 6.873 2.246 a a a a Age (Senior) 0 Ref. 0 Ref. 0 Ref. 0 Ref Less-exp. 0.966 1.010 2.574 1.581 1.122 1.273 2.780 1.470 Wea. 0.761 1.058 0.462 1.073 0.763 1.054 2.157 1.358 Notes: VM: visual-manual; T/L: talking/listening; F.: female; Mid.: middle-aged; Less-exp.: less-experienced; Wea. represents adverse weather conditions were recorded. L/R/A: locating/reaching/answering. Significance level: P < 0.001; P < 0.01; P < 0.05 8 SHRP 2 naturalistic driving data Journal of Intelligent and Connected Vehicles Haotian Cao et al. Volume 3 · Number 1 · 2020 · 1–16 Figure 5 EMM of the likelihood of the cell phone engagement (F.: female, M.: male, Y.: young, Mid.: middle-aged, S.: senior, Less-: less-experienced, Exp.: experienced, Wea.: weather. Wea. represents adverse weather conditions were recorded. EMM: Estimated Marginal Mean. Significance level: P < 0.001, P < 0.01, P < 0.05) The likelihood of being observed holding a cell phone while Driving experience also emerged as a significant predictor for the driving was dramatically higher for young drivers (more than likelihood of overall cell phone use, cell phone visual-manual demanded 71 per cent) and middle-age drivers (more than 64 per cent) tasks, as well as subtasks such as cell phone holding, cell phone texting when compared to senior drivers. As shown in Figure 5(d), and cell phone talking/listening handheld. Experienced drivers were EMMs regarding this activity for three age groups are 6.30 associated with a larger than 68 per cent decrease in the odds of per cent, 6.10 per cent, and 3.80 per cent. The difference of being observed engaged in all cell phone-related tasks when the EMM between the young and the senior is 2.50 per cent compared with less-experienced drivers, as shown in Figure 5(a); (P<0.01), while that between the middle-aged and the the difference of the EMM is 4.00 per cent (6.20 vs 11.20 per cent, senior is 2.30 per cent (P<0.001). P<0.001). Moreover, the likelihood of being observed in cell phone Senior drivers are associated with a larger than 53 per cent visual-manual tasks was also much lower for experienced drivers, as decrease in the likelihood of being observed texting. As shown in Figure 5(c); the difference of the EMM is 2.10 per cent shown in Figure 5(e), corresponding EMMs regarding (4.70vs6.80per cent, P < 0.05) when compared with less- this behavior for three age groups are 6.30 per cent, 5.50 experienced drivers. Similar patterns were also found for cell phone per cent, and 3.70 per cent, with the difference of the holding, cell phone texting and cell phone talking/listening handheld;in EMM between the young and the senior is 2.70 per cent those cases, likelihoods of cell phone engagements were lower for (P<0.01), while that between the middle-aged and the experienced drivers, with the decreased odds of being 71.3, 58.1 senior is 1.80 per cent (P<0.001). and 47.0 per cent. More specifically: 9 SHRP 2 naturalistic driving data Journal of Intelligent and Connected Vehicles Haotian Cao et al. Volume 3 · Number 1 · 2020 · 1–16 Experienced drivers were associated with an approximately large number of crashes for older drivers, it would increase the 30 per cent decrease in the odds of being observed talking/ crash risk for them. That means, compared with young drivers, listening handheld. As shown in Figure 5(b), EMMs of the using a cell phone was much more dangerous but was likelihood regarding this task for the experienced and performed less frequent by the senior. Additionally, most PAR the less-experienced are 7.20 and 5.00 per cent, and the percentages of cell phone-related tasks for young drivers were difference of the EMM is 2.20 per cent (P<0.01). the highest, which indicates that using cell phone might Engagements of being observed holding a cell phone while contribute to more crashes for young drivers; one explanation driving were lower for experienced drivers, with the could be they might be likely to underestimate road risks decreased odds of being about 71 per cent. As shown in (Taubman-Ben-Ari and Lotan, 2011). Figure 5(d), the EMM regarding this activity for The degree of crash risk respecting to cell phone experienced and less-experienced drivers is 4.10 per cent engagements varies with the driving experience. More and 6.70 per cent, respectively, and the difference of the specifically, less-experienced drivers confront a larger crash risk EMM is 2.60 per cent (P<0.01). compared to experienced drivers when distracted by cell phone. The likelihood of being observed texting while driving was This finding is also consistent with the conclusions from Klauer dramatically higher for less-experienced drivers (about 58 et al. (2014). In addition, most PAR percentages of specific cell per cent) when compared to experienced drivers. As phone-related subtask were obviously lower for experienced shown in Figure 5(e), the EMM regarding this activity for drivers than that of less-experienced drivers, which indicates the experienced and the less-experienced are 6.20 and cell phone usage accounted for fewer crashes for experienced 4.00 per cent, respectively. What is more, the difference of drivers. Potential reasons could be mature risk-management the EMM is about 2.20 per cent (P<0.05). skills may take a longer time to develop than driving skills (Stavrinos et al.,2013), therefore, drivers with less than Finally, as shown in Figure 5(a) and (b), the likelihood of 10 years of driving experience might be easily over-confident in engaging in all cell phone-related tasks, as well as cell phone their driving skills while multitasking and over-estimating their talking/listening hand-held, was lower when adverse weather risk-management capabilities. In addition, a previous study conditions (e.g. fog, rain, snow) were recorded, with the indicates that young novice drivers had higher crash risk while incensement being between 25 and 36 per cent, and engaged in texting compared with experienced drivers (Klauer corresponding differences of the EMM regarding these two et al.,2014). types of engagements are 1.70 per cent (9.10 vs 7.40 per cent, The prevalence of the overall cell phone use in this study is P < 0.05), and 1.80 per cent (7.00 vs 5.20 per cent, P < 0.05). considerably higher than that reported in Albans (Hertfordshire), England by Sullman et al. (2015),aswellasin 4. Discussion and conclusion Spain by Prat et al. (2015); however, it is lower than that reported in Alabama, USA by Huisingh et al. (2015). These Secondary task related to cell phone usages during driving is a differences may come from multiple reasons such as the major cause of traffic accidents. On the basis of the SHRP 2 NDS data, this research quantitatively evaluated the cell phone method of data collection (roadside observation and use on driving safety impacts both at the individual and large- naturalistic in-vehicle observation), cultural factors or population level. One interesting finding in this study is that differences in legislation and the level of enforcement (e.g. handheld mobile phone use was illegal in St. Albans and Spain, talking/listening to a cell phone handheld seems not to whereas it is legitimate in Alabama). significantly increase the crash risk, which is consistent with In addition, our results also suggest that the prevalence of some other naturalistic driving studies (Klauer et al.,2014). certain distracting activities related to cell phones varies with The reason might be talking/listening to a cell phone handheld demographic factors such as gender, age and driving is generally considered as a cognitive task that may not require experience. More specifically, female drivers are more likely to too much attention from the driver. engage in cell phone while driving than male drivers. Indeed, in Analysis results in this study indicate that risks of the cell the periodic studies conducted in the USA, the prevalence of phone engagement may substantially differ because of driver cell phone use of women was higher (Glassbrenner, 2005; demographic characteristics such as age, driving experience or their combinations. The impact of cell phone engagement Pickrell et al.,2016). While a majority of studies conducted regarding the crash risk is significantly higher for young drivers outside the USA show that male drivers were more likely to use than middle-aged drivers, which is in accordance with the cell phone while driving than female drivers (Sullman et al., results of Guo et al. (2017). Moreover, senior drivers increased 2015; Vollrath et al.,2016; Wundersitz, 2014). Similar patterns crash odds when engaged in cell phone dialing hand-held, are also observed for specific cell phone-related tasks. For texting, holding or browsing. One of the potential reasons instance, talking/listening to the cell phone handheld was more could be senior drivers perceived a higher level of hazard for frequently seen for female drivers, whereas the opposite was most mobile phone-related tasks when compared to the young true for conversing on a handheld mobile phone, as reported by Sullman et al. (2015). Gender did not appear to be related to and the middle-aged drivers (Ferreira et al.,2013). Generally, the frequency of cell phone visual-manual task engagements they need more time to text by hand, thus, they made longer internal glances which degraded the control of the vehicle to a (i.e. texting, dialing, reaching). O’Brien et al. (2010) showed greater extent for senior drivers (Owens et al.,2011). However, that there was no obvious difference in texting prevalence nearly all PAR percentages of cell phone-related secondary observed in male and female drivers. Sullman et al. (2015) also tasks were the lowest for senior drivers. Just as Papantoniou found that gender was not a significant predictor for texting; et al. (2015) pointed out, cell phone use did not account for a similarly, Huisingh et al. (2015) concluded that there were no 10 SHRP 2 naturalistic driving data Journal of Intelligent and Connected Vehicles Haotian Cao et al. Volume 3 · Number 1 · 2020 · 1–16 differences in the proportion of male and female drivers percentage of total crashes for experienced-middle-aged observed for texting/dialing a phone. drivers. Age differences found in this study were broadly verified by In view of the above, road safety policymakers could better previous questionnaire-based research (Nurullah et al., 2013; understand the negative effects of cell phone use while driving Young and Lenné, 2010), as well as a roadside observational from this study, with the aim of formulating appropriate study (Huisingh et al., 2015; Prat et al., 2015; Sullman et al., strategies in the field of road safety. Thus, more comprehensive 2015). Age was a significant predictor of engagements in cell training systems should be established to educate potential phone. Drivers older than 60 years were less likely to be drivers, especially for young or novice ones, so that they are engaged in driving distractions related to cell phone compared fully aware of the importance of driving safety. Nevertheless, with younger drivers (<59 years), which is in agreement with the prevalence of cell phone use is still large (Waddell and several previous observational studies (Huisingh et al., 2015; Wiener, 2014; Oviedo-Trespalacios et al., 2017) and current Prat et al., 2015; Sabzevari et al.,2016; Sullman et al.,2015). legislation and enforcement alone do not prevent drivers using However, the prevalence of cell phone-related secondary tasks handheld cell phones (Ehsani et al.,2016; Nevin et al., 2016; between young and middle-aged drivers did not differ much in Oviedo-Trespalacios, 2018). Parnell et al. (2017) argue that this study. Those roadside observational research studies in legislation on distraction is based on driver behavior but England or in Alabama indicated that being middle-aged was overlooks the role of the wider road transport system, e.g. car associated with lower odds of being distracted by cell phone manufacturers and mobile phone designers. Therefore, car (Sullman et al.,2015). The reason for the difference might be manufacturers and mobile phone designers can develop new young people seem to use modern technology more frequently, ideas to reduce the rates of cell phone usage when driving such which also transfers into the driving environment, while as “textalyzer” detection devices (Richtel, 2016), thereby middle-aged drivers may have more driving journeys for work- preventing crashes and injuries. Furthermore, the applications related purposes. of the advanced driver assistance systems, such as autonomous In addition, the prevalence of most tasks related to cell phone emergency braking, collision avoidance system and lane- is the lowest for senior or experienced drivers. The reason departure warning system, are also encouraged to avoid crashes might be related to the drivers’ self-regulation. Self-regulation by cell phone usage while driving. is the dynamic process in which drivers manage competing demands, while simultaneously avoiding collisions (Oviedo- Trespalacios, 2018). Self-regulation in drivers occurs at three References different levels including operational, tactical and strategic. Andrews, E.C. and Westerman, S.J. (2012), “Age differences Tactical self-regulation includes deciding when or where to in simulated driving performance: compensatory processes”, engage in secondary tasks related to mobile phones. According Accident Analysis & Prevention, Vol. 45 No. 1, pp. 660-668. to Young and Regan (2013), tactical control involves adjusting Asbridge, M., Brubacher, J.R. and Chan, H. (2013), “Cell or controlling the time of secondary task engagement such as phone use and traffic crash risk: a culpability analysis”, stopping talking or delaying responses to ringing phones. International Journal of Epidemiology, Vol. 42 No. 1, Hancox et al. 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(2013), “Drivers’ “Driver performance while text messaging using handheld willingness to engage with their mobile phone: the influence and in-vehicle systems”, Accident Analysis & Prevention, of phone function and road demand”, IET Intelligent Vol. 43 No. 3, pp. 939-947. Transport Systems, Vol. 7 No. 2, pp. 215-222. Papantoniou, P., Antoniou, C., Yannis, G., Papadimitriou, E., Harris Interactive (2011), Most U.S. Drivers Engage in Pavlou, D. and Golias, J. (2015), “P10 how cell phone use ‘Distracting’ Behaviors: Poll, November 30. affects reaction time of older drivers”, Journal of Transport & He, J., Chaparro, A., Nguyen, B., Burge, R.J., Crandall, J., Health, Vol. 2 No. 2, pp. S68-S69. Chaparro, B. and Cao, S. (2014), “Texting while driving: is Parnell, K.J., Stanton, N.A. and Plant, K.L. (2017), “What’s speech-based text entry less risky than handheld text entry”, the law got to do with it? 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(2007), “Effects of roads in Spain”, Accident Analysis & Prevention, Vol. 74, distraction and experience on situation awareness and pp. 8-16. simulated driving”, Transportation Research Part F: Traffic Ranney, T. (2008), Driver Distraction: A Review of the Current Psychology and Behaviour, Vol. 10 No. 4, pp. 321-329. State-of-Knowledge, NHTSA, Washington, DC, Research Klauer, S.G., Guo, F., Simons-Morton, B.G., Ouimet, M.C., No. DOT HS 810 704. Lee, S.E. and Dingus, T.A. (2014), “Distracted driving and Reimer, B., Mehler, B. and Donmez, B. (2014), “A study of risk of road crashes among novice and experienced drivers”, young adults examining phone dialing while driving using a New England Journal of Medicine, Vol. 370 No. 1, pp. 54-59. - touchscreen vs a button style flip-phone”, Transportation Lipovac, K., Deric, M., Tešic, M., Andric, Z. and Maric, B. Research Part F: Traffic Psychology and Behaviour, Vol. 23, (2017), “Mobile phone use while driving-literary review”, pp. 57-68. 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(2014), “Keep your eyes on the road: young and Ergonomics Society, Vol. 51 No. 5, pp. 762-770. driver crash risk increases according to duration of Horrey, W.J. and Wickens, C.D. (2006), “Examining the distraction”, Journal of Adolescent Health,Vol. 54 No. 5, impact of cell phone conversations on driving using meta- pp. S61-S67. analytic techniques”, Human Factors: The Journal of the Stavrinos, D., Jones, J.L., Garner, A.A., Griffin, R., Franklin, Human Factors and Ergonomics Society, Vol. 48 No. 1, C.A., Ball, D., Welburn, S.C., Ball, K.K., Sisiopiku, V.P. pp. 196-205. and Fine, P.R. (2013), “Impact of distracted driving on Kidd, D.G., Tison, J., Chaudhary, N.K., Mccartt, A.T. and safety and traffic flow”, Accident Analysis & Prevention, Casanova-Powell, T.D. (2016), “The influence of roadway Vol. 61, pp. 63-70. situation, other contextual factors, and driver characteristics Sullman, M.J.M. (2012), “An observational study of driver on the prevalence of driver secondary behaviors”, distraction in England”, Transportation Research Part F: Transportation Research Part F: Traffic Psychology and Traffic Psychology and Behaviour, Vol. 15 No. 3, pp. 272-278. Behaviour, Vol. 41, pp. 1-9. Sullman, M.J.M., Prat, F. and Tasci, D.K. (2015), “A roadside Klauer, S.G., Dingus, T.A., Neale, V.L., Sudweeks, J.D. and study of observable driver distractions”, Traffic Injury Ramsey, D.J. (2006), The Impact of Driver Inattention on near- Prevention, Vol. 16 No. 6, pp. 552-557. Crash/Crash Risk: An Analysis Using the 100-Car Naturalistic Taubman-Ben-Ari, O. and Lotan, T. (2011), “The Driving Study Data,p.31. contribution of a novel intervention to enhance safe driving National Safety Council (2013), Crashes Involving Cell Phones: among young drivers in Israel”, Accident Analysis & Challenges of Collecting and Reporting Reliable Crash Data, Prevention, Vol. 43 No. 1, pp. 352-359. National Safety Council, Itasca, IL. Tractinsky, N., Ram, E.S. and Shinar, D. (2013), “To call or National Safety Council (2017), Undercounted is Underinvested: not to call—that is the question (while driving)”, Accident; How Incomplete Crash Reports Impact Efforts to save Lives, Analysis and Prevention, Vol. 56, pp. 59-70. National Safety Council, Itasca, IL. Vollrath, M., Huemer, A.K., Teller, C., Likhacheva, A. and NHTSA (2012), “State Laws”, available at: www.distraction. Fricke, J. (2016), “Do German drivers use their smartphones gov/content/getthe-facts/state-laws.html (accessed 18 July safely? Not really!”, Accident Analysis & Prevention, Vol. 96, 2012). pp. 29-38. Oscar, O.T. (2018), “Getting away with texting: behavioural Waddell, L.P. and Wiener, K.K.K. (2014), “What’s driving adaptation of drivers engaging in visual-manual tasks while illegal mobile phone use? Psychosocial influences on drivers’ driving”, Transportation Research Part A: Policy and Practice, intentions to use hand-held mobile phones”, Transportation Vol. 116, pp. 112-121. Research Part F: Traffic Psychology and Behaviour,Vol.22, Oscar, O.T., Mark, K., Mazharul, H.M., Simon, W. and Boris, pp. 1-11. P. (2017), “Risk factors of mobile phone use while driving in Wundersitz, L.N. (2014), “Phone use while driving: results Queensland: prevalence, attitudes, crash risk perception, and from an observational survey”, Traffic Injury Prevention, task-management strategies”, Plos One,Vol. 12 No. 9, Vol. 15 No. 6, pp. 537-541. p. e0183361. Young, K.L. and Lenné, M.G. (2010), “Driver engagement in Oscar, O.T., Mazharul, H.M., Mark, K. and Simon, W. distracting activities and the strategies used to minimise (2018), “Should i text or call here? 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Volume 3 · Number 1 · 2020 · 1–16 Stavrinos, D., Garner, A.A., Franklin, C.A., Johnson, H.D., independent variable of secondary task engagement X is st Welburn, S.C., Griffin, R., Underhill, A.T. and Fine, P.R. defined as: (2015), “Distracted driving in teens with and without > 1; if any of secondary task 1; 2; 3 attention-deficit/hyperactivity disorder”, Journal of Pediatric contains a specific subtask; Nursing, Vol. 30 No. 5, pp. e183-e191. X ¼ (A4) st 0; if any of secondary task 1; 2; 3 Strayer, D.L., Crouch, D.J. and Drews, F.A. (2006), “A contains no secondary task at al: comparison of the cell phone driver and the drunk driver”, Social Science Electronic Publishing, Vol. 48 No. 2, Moreover, X represents a nominal variable of one specific dc pp. 381-391. driver demographic characteristic (age group, driving Sun, D. and Jia, A. (2016), “Impacts of cell phone use on experience group or the combination group of the age and driving safety and drivers’ perception of risk”, Journal of driving experience),½ X X represents the interaction effect st dc Modern Transportation, Vol. 24 No. 2, pp. 61-68, 02. between the exposure of interest and one specific secondary Violanti, J.M. (1997), “Cellular phones and traffic accidents”, task engagement listed in Table III, b i ¼ 0; 1; 2; 3 are the Public Health, Vol. 111 No. 6, pp. 423-428. regression parameters and a is a driver-specific random term. Yager, C.E. (2013), “Driver safety impacts of voice-to-Text If the interaction effect in the regression model is significant at mobile applications”, Human Factors & Ergonomics Society 0.05 level, then the EMM of the interaction effect respecting Meeting, SAGE Publications. to the crash risk would be obtained by using the professional Yagil, D. (1998), “Gender and age differences in attitudes toward statistical software IBM SPSS Statistics©. traffic laws and traffic violations”, Transportation Research Part F: Similar procedures are performed when investigating the Traffic Psychology and Behaviour, Vol. 1 No. 2, pp. 123-135. correlation among the likelihood of the cell phone-related Young, K.L., Rudin-Brown, C.M. and Lenné, M.G. (2010), engagement and demographic characteristics (gender, age and “Look who’s talking! a roadside survey of drivers’ cell phone driving experience) and environment conditions (weather, light, use”, Traffic Injury Prevention, Vol. 11 No. 6, pp. 555-560. traffic density and time of the day). Please follow Table A1 to get more detailed information on predictors and targets by level. Appendix. Details on statistical methods used in this study Population attributable risk percentage As multiple crashes and baselines can be derived from one driver, One possible calculation method for the PAR percentage then another mixed effect random logistic regression model among the population is based on the “population relative including the interaction term between one secondary task and risk” that is determined as the relative risks for the two groups one exposure of interest was adopted to incorporate the driver- comprising the population, and the proportion of the specific correlation. That means analyses were conducted for population exposed that is referred to as “prevalence” in this both crashes and controls overall for each demographic (gender, study. The relative risk is commonly referred to as if it were age group or driving experience group) when considering one the usual measure of risk obtained in a case-control study such specific distraction activity (listed in Table II) while driving, as OR. OR is a measure of association between an outcome which aims to examine whether the driver demographic (e.g. a crash involvement) and an exposure (e.g. one characteristic would significantly affect the driving safety when secondary task engagement), which represents the odds that drivers engaged in cell phone-related secondary tasks during the an outcome will occur, given a particular exposure compared driving. The risk associated with cell phone engagements is to the odds of the outcome occurring in the absence of that evaluated through a comparison with alert, attentive and sober exposure. To obtain the PAR percentage, first, a 2 2 driving episodes. Assuming that, contingency table would be constructed to obtain the OR for a specific secondary task in a specific driver group (gender, age 1 for driver i; event j is a crash Y ¼ (A1) ij or driving experience group) were exposed and unexposed 0 for driver i; event j is a baseline groups combined must form the entire population. And supposing the observed safety consequence Y is expected Algebraically, this equation can be written as shown in (A5), to be followed by a Bernoulli distribution, A D Odds Ratio ¼ (A5) B C Y Bernoulli p (A2) ðÞ ij where, where p is the probability of event j being a crash for driver i. A = the number of crashes where one specific secondary ij Then, the probability p is associated with a set of covariates task was present (while without any other type of ij secondary task); by a logit link function, such that, B = the number of baseline epochs where one specific ij logit p ¼ log ¼ b 1 b X 1 b X ðÞ ij st dc 0 1 2 secondary task was present (while without any other 1 p (A3) ij type of secondary task); 1 b X X 1 a st dc C = the number of crashes where no such secondary task For example, as cell use for distraction in SHRP 2 event data was present (but can have other secondary tasks); and set is determined by three variables, namely, “Secondary Task D = the number of baseline epochs where no such 1”, “Secondary Task 2”, and “Secondary Task 3” (we use secondary task was present (but can have other “secondary task 1/2/3” to denote those variables). Then the secondary tasks). 14 SHRP 2 naturalistic driving data Journal of Intelligent and Connected Vehicles Haotian Cao et al. Volume 3 · Number 1 · 2020 · 1–16 Table AI Predictors and targets by level in regression models Predictor/Targets Values Gender 0 = female, 1 = male Age 1 = young, 2= middle-aged, 3 = senior Driving experience 0 = less-experienced, 1 = experienced Combination of age and experience 0 = experienced middle-aged, 1 = others (non-experienced-middle-aged) Secondary task (specific) 0 = not engaged any secondary task, 1 = engaged one specific secondary task Weather 0 = no adverse weather recorded, 1 = adverse weather recorded Light 0 = daylight, 1 = Night Traffic density 1 = low, 2 = medium, 3 = high Time of the day 1 = morning rush hour, 2 = evening rush hour, 3 = others Safety consequence (target) 0 = baseline, 1 = crash Cell phone engagement (target) 0 = not engaged, 1 = engaged Table AII shows the OR (with corresponding 95 per cent Moreover, if the OR value of the secondary task for one group confidence intervals) of each cell phone-related secondary task of drivers is over 1.0, then the PAR percentage is evaluated by, by age groups, driving experience groups or their P ðÞ OR 1 combinations. It is important to notice that the resulting odds PAR% ¼ 100 (A6) ðÞ 11 P OR 1 ratio by (A5) only reflects the crash risk of engaging in one specific secondary task compared with not engaging in it Where P denotes the prevalence of one specific cell phone (which could possibly engage in other secondary tasks). use, andORdenotes therelativeriskestimatefor acrash. Table AII ORs (with 95% CI) of the Cell phone use by driver demographics Young Middle-aged Senior Secondary task OR (CI) OR (CI) OR (CI) Overall cell phone use 1.64 (1.35,2.01) 1.16 (0.83,1.64) 2.51 (1.32,4.80) Cell phone visual-manual tasks 2.20 (1.67,2.90) 1.67 (0.98,2.86) 5.63 (2.19,14.47) Dialing handheld 2.98 (0.62,14.36) 3.55 (1.03,12.25) 44.75 (4.64,431.35) Holding 2.98 (2.26,3.93) 2.13 (1.28,3.55) 8.63 (3.59,20.70) Locating/reach/answering 2.24 (1.32,3.81) 2.11 (0.75,5.94) NA Texting 2.34 (1.73,3.18) 1.20 (0.58,2.47) 5.59 (1.48,21.15) Browsing 1.41 (0.80,2.48) 0.34 (0.04,2.42) 3.71 (0.41,33.25) Talking/listening, hand-held 0.87 (0.58,1.33) 0.96 (0.56,1.62) 0.74 (0.18,3.07) Secondary task Less-experienced Experienced OR (CI) OR (CI) Overall cell phone use 1.58 (1.31,1.92) 1.20 (0.85,1.69) Cell phone visual-manual tasks 2.24 (1.72,2.91) 1.72(0.99,3.00) Dialing handheld 6.09 (2.04,18.22) 3.43 (0.99,11.87) Holding 2.75 (2.11,3.59) 3.11 (1.94,4.98) Locating/reaching/answering 2.31 (1.38,3.87) 1.47 (0.45,4.78) Texting 2.23 (1.66,2.99) 1.46 (0.71,3.02) Browsing 1.27 (0.73,2.21) 0.79 (0.19,3.29) Talking/listening, hand-held 0.91 (0.63,1.32) 0.72 (0.39,1.32) Secondary task Non-middle-aged-experienced Middle-aged-experienced OR(CI) OR(CI) Overall cell phone use 1.77 (1.48,2.11) 1.11 (0.74,1.68) Cell phone visual-manual tasks 2.55 (1.99,3.28) 1.29 (0.62,2.65) Dialing handheld 9.80 (3.86,24.87) NA Holding 3.22 (2.51,4.14) 2.54 (1.41,4.58) Locating/reaching/answering 2.33 (1.40,3.87) 2.13 (0.65,7.06) Texting 2.52 (1.90,3.35) 1.15 (0.47,2.86) Browsing 1.47 (0.86,2.51) 0.51 (0.07,3.70) Talking/listening, hand-held 0.98 (0.68,1.40) 0.80 (0.41,1.58) Notes: CI: 95% confidence interval; OR: odds ratio; Italic text indicates statistically significant OR at the 0.05 level; NA indicates that no crash with cell task was observed in the data set or no statistical significance reported between the cell task and the crash 15 SHRP 2 naturalistic driving data Journal of Intelligent and Connected Vehicles Haotian Cao et al. Volume 3 · Number 1 · 2020 · 1–16 When assessing the PAR percentage for a secondary task P ¼ 100% 3:50% (A8) 2511 6927 related to the cell phone use, the baseline prevalence P was calculated by counting the number of baseline epochs where According to Table A2, if the OR of cell phone texting, in this aspecific task associated with the cell phone use was present case, is 2.34, then the resulting PAR percentage is, and counting the total number of baseline epochs (“m of ðÞ 3:50% 2:34 1:00 baseline epochs with a cell phone use present”1 “n of PAR% ¼ 100 ¼ 4:49 1:001 3:50% ðÞ 2:34 1:00 baseline epochs where no such type of cell phone use was (A9) present”), namely, That indicates the cell phone texting contributed to 4.49 per P ¼ 100% (A7) m1 n cent of all crashes for young drivers. For example, baseline epochs of the cell phone texting for young drivers are 251, while the baseline epochs, where no such type of Corresponding author inattention was present, are 6,927, then the baseline prevalence Zhenghao Zhang can be contacted at: zhenghao_0817@163. of the cell phone texting for female drivers is: com For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: permissions@emeraldinsight.com
Journal of Intelligent and Connected Vehicles – Emerald Publishing
Published: Apr 7, 2020
Keywords: Prevalence; Distracted driving related to cell phones; Driver demographic characteristics; Naturalistic driving study; Population attributable risk percentage
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