Examination of drivers' cell phone use behavior at intersections by using naturalistic driving data

IntroductionMany driving simulator studies have shown that cell phone use while driving greatly degraded driving performance. In terms of safety analysis, many factors including drivers, vehicles, and driving situations need to be considered. Controlled or simulated studies cannot always account for the full effects of these factors, especially situational factors such as road condition, traffic density, and weather and lighting conditions. Naturalistic driving by its nature provides a natural and realistic way to examine drivers' behaviors and associated factors for cell phone use while driving.MethodIn this study, driving speed while using a cell phone (conversation or visual/manual tasks) was compared to two baselines (baseline 1: normal driving condition, which only excludes driving while using a cell phone, baseline 2: driving-only condition, which excludes all types of secondary tasks) when traversing an intersection.ResultsThe outcomes showed that drivers drove slower when using a cell for both conversation and visual/manual (VM) tasks compared to baseline conditions. With regard to cell phone conversations, drivers were more likely to drive faster during the day time compared to night time driving and drive slower under moderate traffic compared to under sparse traffic situations. With regard to VM tasks, there was a significant interaction between traffic and cell phone use conditions. The maximum speed with VM tasks was significantly lower than that with baseline conditions under sparse traffic conditions. In contrast, the maximum speed with VM tasks was slightly higher than that with baseline driving under dense traffic situations.Practical applicationsThis suggests that drivers might self-regulate their behavior based on the driving situations and demand for secondary tasks, which could provide insights on driver distraction guidelines. With the rapid development of in-vehicle technology, the findings in this research could lead the improvement of human-machine interface (HMI) design as well.     

驾驶中使用手机对驾驶员行为安全绩效影响综述

驾驶中使用手机与交通事故之间存在着高度相关性。为揭示使用手机对驾驶行为安全绩效的影响,探索影响驾驶安全的理论机制,采取更有效的干预措施,结合近10 a来相关研究,综述了与驾驶安全密切相关的驾驶分心问题,主要包括:驾驶员分心的定义及其分类;使用手机对驾驶行为安全绩效的影响,如反应时(RT)、行车速度、路线保持和跟车距离;手机使用对驾驶员分心影响的理论机制,如信息加工理论和计划行为理论(TPB)。分析表明,使用手机会导致驾驶员的反应时延长15%～40%,驾驶路线发生明显偏移,对于行车速度减缓和跟车距离延长的假设需结合驾驶员主客观数据进行比较做进一步验证;驾驶过程中使用手机会增加驾驶员的认知负荷,TPB能够对使用手机行为进行有效的解释和预测,但对该理论中基于信念测量的研究还很少;除手机操作任务,影响驾驶员分心的其他操作任务还需做进一步的研究。     

Driving context influences drivers' decision to engage in visual–manual phone tasks: Evidence from a naturalistic driving study

IntroductionVisual–manual (VM) phone tasks (i.e., texting, dialing, reading) are associated with an increased crash/near-crash risk. This study investigated how the driving context influences drivers' decisions to engage in VM phone tasks in naturalistic driving.MethodVideo-recordings of 1,432 car trips were viewed to identify VM phone tasks and passenger presence. Video, vehicle signals, and map data were used to classify driving context (i.e., curvature, other vehicles) before and during the VM phone tasks (N = 374). Vehicle signals (i.e., speed, yaw rate, forward radar) were available for all driving.ResultsVM phone tasks were more likely to be initiated while standing still, and less likely while driving at high speeds, or when a passenger was present. Lead vehicle presence did not influence how likely it was that a VM phone task was initiated, but the drivers adjusted their task timing to situations when the lead vehicle was increasing speed, resulting in increasing time headway. The drivers adjusted task timing until after making sharp turns and lane change maneuvers. In contrast to previous driving simulator studies, there was no evidence of drivers reducing speed as a consequence of VM phone task engagement.ConclusionsThe results show that experienced drivers use information about current and upcoming driving context to decide when to engage in VM phone tasks. However, drivers may fail to sufficiently increase safety margins to allow time to respond to possible unpredictable events (e.g., lead vehicle braking).Practical applicationsAdvanced driver assistance systems should facilitate and possibly boost drivers' self-regulating behavior. For instance, they might recognize when appropriate adaptive behavior is missing and advise or alert accordingly. The results from this study could also inspire training programs for novice drivers, or locally classify roads in terms of the risk associated with secondary task engagement while driving.     

视觉分心程度对跟车运动状态的影响

为明确驾驶人在不同复杂度的交通环境下视觉分心程度对跟车运动状态的影响,在驾驶模拟器上搭建城市、乡村和高速道路3种交通环境,利用视线遮挡方法,设置O、1和2s等3种固定时长,量化被试视觉分心程度;对26位被试进行模拟器驾驶试验,采集车辆速度、纵向加速度和车辆位置等参数;采用重复测量一般线性模型,分析视觉分心程度对上述参数...     

Effects of naturalistic cell phone conversations on driving performance

PROBLEM: The prevalence of automobile drivers talking on cell phones is growing, but the effect of that behavior on driving performance is unclear. Also unclear is the relationship between the difficulty level of a phone conversation and the resulting distraction. METHOD: This study used a driving simulator to determine the effect that easy and difficult cell phone conversations have on driving performance. RESULTS: Cell phone use caused participants to have higher variation in accelerator pedal position, drive more slowly with more variation in speed, and report a higher level of workload regardless of conversation difficulty level. CONCLUSIONS: Drivers may cope with the additional stress of phone conversations by enduring higher workloads or setting reduced performance goals. IMPACT ON INDUSTRY: Because an increasing number of people talk on the phone while driving, crashes caused by distracted drivers using cell phones will cause disruptions in business, as well as injury, disability, and permanent loss of personnel.     

Self-regulation of driving speed among distracted drivers: An application of driver behavioral adaptation theory

Objective: The adaptive behavior of mobile phone–distracted drivers has been a topic of much discussion in the recent literature. Both simulator and naturalistic studies suggest that distracted drivers generally select lower driving speeds; however, speed adaptation is not observed among all drivers, and the mechanisms of speed selection are not well understood. The aim of this research was to apply a driver behavioral adaptation model to investigate the speed adaptation of mobile phone–distracted drivers.

Methods: The speed selection behavior of drivers was observed in 3 phone conditions including baseline (no conversation) and hands-free and handheld phone conversations in a high-fidelity driving simulator. Speed adaptation in each phone condition was modeled as a function of secondary task demand and self-reported personal/psychological characteristics with a system of seemingly unrelated equations (SURE) accounting for potential correlations due to repeated measures experiment design.

Results: Speed adaptation is similar between hands-free and handheld phone conditions, but the predictors of speed adaptation vary across the phone conditions. Though perceived workload of secondary task demand, self-efficacy, attitude toward safety, and driver demographics were significant predictors of speed adaptation in the handheld condition, drivers' familiarity with the hands-free interface, attitude toward safety, and sensation seeking were significant predictors in the hands-free condition. Drivers who reported more positive safety attitudes selected lower driving speeds while using phones.

Conclusion: This research confirmed that behavioral adaptation models are suitable for explaining speed adaptation of mobile phone distracted drivers, and future research could be focused on further theoretical refinement.     

Driver hand-held cellular phone use: a four-year analysis

INTRODUCTION: The use of hand-held cellular (mobile) phones while driving has stirred more debate, passion, and research than perhaps any other traffic safety issue in the past several years. There is ample research showing that the use of either hand-held or hands-free cellular phones can lead to unsafe driving patterns. Whether or not these performance deficits increase the risk of crash is difficult to establish, but recent studies are beginning to suggest that cellular phone use elevates crash risk. METHODS: The purpose of this study was to assess changes in the rate of hand-held cellular phone use by motor-vehicle drivers on a statewide level in Michigan. This study presents the results of 13 statewide surveys of cellular phone use over a 4-year period. Hand-held cellular phone use data were collected through direct observation while vehicles were stopped at intersections and freeway exit ramps. Data were weighted to be representative of all drivers traveling during daylight hours in Michigan. RESULTS: The study found that driver hand-held cellular phone use has more than doubled between 2001 and 2005, from 2.7% to 5.8%. This change represents an average increase of 0.78 percentage points per year. The 5.8% use rate observed in 2005 means that at any given daylight hour, around 36,550 drivers were conversing on cellular phones while driving on Michigan roadways. The trend line fitted to these data predicts that by the year 2010, driver hand-held cellular phone use will be around 8.6%, or 55,000 drivers at any given daylight hour. CONCLUSIONS: These results make it clear that cellular phone use while driving will continue to be an important traffic safety issue, and highlight the importance of continued attempts to generate new ways of alleviating this potential hazard.     

Inclusion of phone use while driving data in predicting distraction-affected crashes

Introduction: Given the tremendous number of lives lost or injured, distracted driving is an important safety area to study. With the widespread use of cellphones, phone use while driving has become the most common distracted driving behavior. Although researchers have developed safety performance functions (SPFs) for various crash types, SPFs for distraction-affected crashes are rarely studied in the literature. One possible reason is the lack of critical distracted behavior information in the commonly used safety data (i.e., roadway inventory, traffic, and crash counts). Recently, the frequency of phone use while driving (referred to as phone use data) is recorded by mobile application companies and has become available to safety researchers. The primary objective of this study is to examine if phone use data can potentially predict distracted-affected crashes. Method: The authors first integrated phone use data with roadway inventory, traffic, and crash data in Texas. Then, the Random Forest (RF) algorithm was applied to assess the significance of the feature - phone use while driving - for predicting the number of distraction-affected crashes on a road segment. Further, this study developed two SPFs for distraction-affected crashes with and without the phone use data, separately. Both SPFs were assessed in terms of model fitting and prediction performances. Results: RF results rank the frequency of phone use as an important factor contributing to the number of distraction-affected crashes. Performance evaluations indicated that the inclusion of phone use data in the SPFs consistently improved both fitting and prediction abilities to predict distracted-affected crashes. Practical Applications: The phone use data provide new insights into the safety analyses of distraction-affected crashes, which cannot be achieved by only using the conventional roadway inventory and crash data. Therefore, safety researchers and practitioners are encouraged to incorporate the emerging data sources in reducing distraction-affected crashes.     

Effects of mobile phone use on driving performance in a multiresource workload scenario

Objective: This study explores the influence of mobile phone secondary tasks on driving from the perspective of visual, auditory, cognitive, and psychomotor (VACP) multiple resource theory, and it is anticipated to benefit the human-centered design of mobile phone use while driving.

Methods: The present study investigated 6 typical phone use scenarios while driving and analyzed the effects of phone use distractions on driving performance. Thirty-six participants were recruited to participate in this experiment. We abandoned traditional secondary tasks such as conversations or dialing, in which cognitive resources can become interference. Instead, we adopted an arrow secondary task and an n-back delayed digit recall task.

Results: The results show that all mobile phone use scenarios have a significant influence on driving performance, especially on lateral vehicle control. The visual plus psychomotor resource occupation scenario demonstrated the greatest deterioration of driving performance, and there was a significant deterioration of driving speed and steering wheel angle once the psychomotor resource was occupied.

Conclusions: Phone use distraction leads to visual, cognitive, and/or motor resource functional limitations and thus causes lane violations and traffic accidents.     

The role of self-regulation in the context of driver distraction: A simulator study

Objective: There is considerable evidence for the negative effects of driver distraction on road safety. In many experimental studies, drivers have been primarily viewed as passive receivers of distraction. Thus, there is a lack of research on the mediating role of their self-regulatory behavior. The aim of the current study was to compare drivers' performance when engaged in a system-paced secondary task with a self-paced version of this task and how both differed from baseline driving performance without distraction.

Methods: Thirty-nine participants drove in a simulator while performing a secondary visual–manual task. One group of drivers had to work on this task in predefined situations under time pressure, whereas the other group was free to decide when to work on the secondary task (self-regulation group). Drivers' performance (e.g., lateral and longitudinal control, brake reaction times) was also compared with a baseline condition without any secondary task.

Results: For the system-paced secondary task, distraction was associated with high decrements in driving performance (especially in keeping the lateral position). No effects were found for the number of collisions, probably because of the lower driving speeds while distracted (compensatory behavior). For the self-regulation group, only small impairments in driving performance were found. Drivers engaged less in the secondary task during foreseeable demanding or critical driving situations.

Conclusions: Overall, drivers in the self-regulation group were able to anticipate the demands of different traffic situations and to adapt their engagement in the secondary task, so that only small impairments in driving performance occurred. Because in real traffic drivers are mostly free to decide when to engage in secondary tasks, it can be concluded that self-regulation should be considered in driver distraction research to ensure ecological validity.     

基于驾驶模拟的自动驾驶接管行为研究*

为探索有条件自动驾驶对非驾驶相关任务的允准边界,基于实车驾驶模拟器,设计自动驾驶接管试验典型场景,招募30名被试者开展驾驶模拟试验;要求驾驶人执行3种分心形式的驾驶次任务,系统发出接管请求提示后,驾驶人接管车辆控制权以避免险情发生,并分析驾驶人接管反应时间、驾驶负荷以及驾驶绩效等相关数据。结果表明:驾驶次任务涉及的分心形式越复杂,接管过程安全性越差,视觉分心任务与操作分心任务对接管行为影响显著;驾驶人更倾向于选择制动操作接管车辆,次任务分心程度越高,制动接管比例越大;与乘客聊天对接管行为影响不显著,看视频和玩手机游戏均会显著延长接管反应时间,增加工作负荷与车辆纵向减速度,玩手机游戏还会显著提升车辆横向加速度。     

驾驶人视觉注意力需求与驾驶行为特性的关系

采用驾驶模拟系统平台,以不同车道类型及不同交通流状态(自由流/稳定流/不稳定流/强制流)为虚拟试验场,应用心理试验设计方法,测试得到21名被试在不同交通流状态下的视觉注意力需求及驾驶行为。对5种车道类型及4种交通流状态下被试驾驶时的视觉注意力需求特性和驾驶行为特性数据进行分析,得到不同车道不同交通流状态下的视觉注意力需求变化情况。利用多元回归方法分析了不同交通流状态下驾驶人视觉注意力需求与驾驶行为之间的关系,并构建了基于驾驶行为特性的驾驶人视觉注意力需求预测模型。结果表明,驾驶人视觉注意力需求与制动次数、油门踏板位移和车辆轨迹偏差之间存在相关关系。     

Classification of driving workload affected by highway alignment conditions based on classification and regression tree algorithm

Objective: Guaranteeing a safe and comfortable driving workload can contribute to reducing traffic injuries. In order to provide safe and comfortable threshold values, this study attempted to classify driving workload from the aspects of human factors mainly affected by highway geometric conditions and to determine the thresholds of different workload classifications. This article stated a hypothesis that the values of driver workload change within a certain range.

Methods: Driving workload scales were stated based on a comprehensive literature review. Through comparative analysis of different psychophysiological measures, heart rate variability (HRV) was chosen as the representative measure for quantifying driving workload by field experiments. Seventy-two participants (36 car drivers and 36 large truck drivers) and 6 highways with different geometric designs were selected to conduct field experiments. A wearable wireless dynamic multiparameter physiological detector (KF-2) was employed to detect physiological data that were simultaneously correlated to the speed changes recorded by a Global Positioning System (GPS) (testing time, driving speeds, running track, and distance). Through performing statistical analyses, including the distribution of HRV during the flat, straight segments and P-P plots of modified HRV, a driving workload calculation model was proposed. Integrating driving workload scales with values, the threshold of each scale of driving workload was determined by classification and regression tree (CART) algorithms.

Results: The driving workload calculation model was suitable for driving speeds in the range of 40 to 120 km/h. The experimental data of 72 participants revealed that driving workload had a significant effect on modified HRV, revealing a change in driving speed. When the driving speed was between 100 and 120 km/h, drivers showed an apparent increase in the corresponding modified HRV. The threshold value of the normal driving workload K was between ?0.0011 and 0.056 for a car driver and between ?0.00086 and 0.067 for a truck driver.

Conclusion: Heart rate variability was a direct and effective index for measuring driving workload despite being affected by multiple highway alignment elements. The driving workload model and the thresholds of driving workload classifications can be used to evaluate the quality of highway geometric design. A higher quality of highway geometric design could keep driving workload within a safer and more comfortable range. This study provided insight into reducing traffic injuries from the perspective of disciplinary integration of highway engineering and human factor engineering.     

Teens' distracted driving behavior: Prevalence and predictors

IntroductionTeen drivers' over-involvement in crashes has been attributed to a variety of factors, including distracted driving. With the rapid development of in-vehicle systems and portable electronic devices, the burden associated with distracted driving is expected to increase. The current study identifies predictors of secondary task engagement among teenage drivers and provides basis for interventions to reduce distracted driving behavior. We described the prevalence of secondary tasks by type and driving conditions and evaluated the associations between the prevalence of secondary task engagement, driving conditions, and selected psychosocial factors.MethodsThe private vehicles of 83 newly-licensed teenage drivers were equipped with Data Acquisition Systems (DAS), which documented driving performance measures, including secondary task engagement and driving environment characteristics. Surveys administered at licensure provided psychosocial measures.ResultsOverall, teens engaged in a potentially distracting secondary task in 58% of sampled road clips. The most prevalent types of secondary tasks were interaction with a passenger, talking/singing (no passenger), external distraction, and texting/dialing the cell phone. Secondary task engagement was more prevalent among those with primary vehicle access and when driving alone. Social norms, friends' risky driving behaviors, and parental limitations were significantly associated with secondary task prevalence. In contrast, environmental attributes, including lighting and road surface conditions, were not associated with teens' engagement in secondary tasks.ConclusionsOur findings indicated that teens engaged in secondary tasks frequently and poorly regulate their driving behavior relative to environmental conditions. Practical applications: Peer and parent influences on secondary task engagement provide valuable objectives for countermeasures to reduce distracted driving among teenage drivers.     

Real-world effects of using a phone while driving on lateral and longitudinal control of vehicles

IntroductionTechnologies able to augment human communication, such as smartphones, are increasingly present during all daily activities. Their use while driving, in particular, is of great potential concern, because of the high risk that distraction poses during this activity. Current countermeasures to distraction from phone use are considerably different across countries and not always widely accepted/adopted by the drivers.MethodsThis study utilized naturalistic driving data collected from 108 drivers in the Integrated Vehicle-Based Safety Systems (IVBSS) program in 2009 and 2010 to assess the extent to which using a phone changes lateral or longitudinal control of a vehicle. The IVBSS study included drivers from three age groups: 20–30 (younger), 40–50 (middle-aged), and 60–70 (older).ResultsResults from this study show that younger drivers are more likely to use a phone while driving than older and middle-aged drivers. Furthermore, younger drivers exhibited smaller safety margins while using a phone. Nevertheless, younger drivers did not experience more severe lateral/longitudinal threats than older and middle-aged drivers, probably because of faster reaction times. While manipulating the phone (i.e., dialing, texting), drivers exhibited larger lateral safety margins and experienced less severe lateral threats than while conversing on the phone. Finally, longitudinal threats were more critical soon after phone interaction, suggesting that drivers terminate phone interactions when driving becomes more demanding.ConclusionsThese findings suggest that drivers are aware of the potential negative effect of phone use on their safety. This awareness guides their decision to engage/disengage in phone use and to increase safety margins (self-regulation). This compensatory behavior may be a natural countermeasure to distraction that is hard to measure in controlled studies.Practical ApplicationsIntelligent systems able to amplify this natural compensatory behavior may become a widely accepted/adopted countermeasure to the potential distraction from phone operation while driving.     

国外乡村公路交通安全水平

收集和整理了美国、加拿大、英国、澳大利亚等国关于乡村公路交通安全的管理和研究成果;归纳国外乡村公路的定义;分析乡村公路在交通事故数量、伤亡人数、死伤率等方面的基本情况。笔者通过对国外乡村公路交通安全水平研究,有如下3个结论:第一,目前还没有一个被普遍接受的乡村公路的定义;第二,乡村公路的交通事故后果比城市道路严重得多;第三,乡村公路交通安全水平提高的速度比城市道路慢;吸取国外乡村公路交通安全管理经验,为我国农村公路交通安全水平的改善提供有益的借鉴。     

The safety effect of open-median management on one-side widened freeways: A driving simulation evaluation

Introduction: Highway expansions and upgrades are often required to increase road network capacity. The widening of one side of a highway, referred to as ‘one-side widening,’ is sometimes implemented in these highway expansion projects. During one-side widening, to save costs, openings can be configured on existing medians (as opposed to removing the existing medians altogether). The median openings allow vehicles in the outer lanes to enter the inner lanes, but they also raise safety concerns and may require alternate open-median management strategies for traffic authorities. There is little existing research that has evaluated the safety effect of these open-median management strategies. Method: To bridge this gap, this study proposes a procedure that evaluates the safety of open-median management strategies for one-side widened highways. The proposed procedure was implemented through driving simulation experiments on a section of Binlai Freeway in Shandong, China. First, the minimum location requirements for median openings were determined by calculating the short length of the weaving segment. Then, simulation tests were carried out to observe driving performance and workload measures. Results: The results indicate that the procedure successfully evaluates the safety effect of open-median management strategies for one-side widened freeways. It was also found that driving performance and workload are sensitive to the opening length and traffic flow. Conclusions: Therefore, median opening placement should be carefully selected in consideration of not only driving performance and workload but also traffic volume predictions. Practical Applications: The findings in this study can guide open-median management strategies for traffic safety one-side widened highways.     

Effects of time-gap settings of adaptive cruise control (ACC) on driving performance and subjective acceptance in a bus driving simulator

A study was conducted to investigate the effects of time-gap settings and contents of secondary tasks on a fix-based bus driving simulator on drivers’ performance while reclaiming control from ACC in a car-following scenario of emergency brake by the lead vehicle. Thirty professional bus drivers drove on the simulator with the scenario of highway traffic flow under 12 random time-gap settings: from 0.64 s to 2.40 s with the interval of 0.16 s. As for the effects of secondary tasks, subjects were evenly divided into three conditions: no secondary task interference, simple secondary task, and complex task. The results demonstrated that different safety demarcations of time-gaps on subjective acceptance and driving performance can be found out. The integrated demarcations separated time-gaps into divisions that represented different levels of danger. It revealed that the safer time-gaps for different situations were: longer than 1.60 s for none-secondary task distraction and longer than 2.08 s for being continuously distracted by secondary tasks. The demand for simple tasks is relatively high, so a larger time-gap is needed for the driver to remain safe. This research has implications for the time-gap selection of ACC and effects of secondary task distraction on buses. A next logical step will focus on determining time-gaps for lead vehicles on curves or slopes, when multiple vehicles are present ahead, and modeling driver behavior and performance with ACC for cars, buses, and other types of vehicles.     

Gap acceptance behavior at unsignalized intersections: Effects of using a phone and a music player while driving

Objectives: The present study is an attempt to analyze and compare the distraction effects caused by the use of a phone and a music player at unsignalized intersections.

Method: Eighty-eight participants performed simulated driving experiments where they faced a sequence of gaps in the major road traffic at 2 unsignalized intersections. In this process, their driving behavior was evaluated in terms of gap acceptance probability, accepted lag, and maneuver completion time. These parameters were modeled with a generalized estimating equation (GEE) method by considering distraction, demographic factors, driving history, maneuver types, and driving attributes in the approach and completion zones as independent variables.

Results: The results showed that gap acceptance probability decreased by 46% during the conversation task, whereas it increased by 66% during the music player task. Lower gap acceptance could be a compensatory behavior adopted by drivers during the conversation task, whereas no such measure was adapted during the music player task. The results indicate that a higher approach speed during the music player task might have led to increased gap acceptance. Further, though the effect of distraction on the accepted lag was not evident, the completion time was reduced during the conversation task.

Conclusions: Overall, the results suggest that drivers are more likely to adopt a compensatory measure in complex driving situations only if they perceive a high risk. Hence, drivers are exposed to a greater risk while operating a music player, because this is not perceived as risky behavior.     

Using European naturalistic driving data to assess secondary task engagement when stopped at a red light

Problem: Some evidence exists that drivers choose to engage in secondary tasks when the driving demand is low (e.g., when the car is stopped). While such a behavior might generally be considered as rather safe, it could be argued that the associated diversion of attention away from the road still leads to a reduction of situational awareness, which might increase collision risk once the car regains motion. This is especially relevant for texting, which is associated with considerable eyes-off-the-road-time. Nonetheless, it seems that previous research has barely addressed the actual engagement in secondary tasks while waiting at a red light (as compared to just addressing the tasks’ mere prevalence). Objective: The present study investigated secondary task engagement while stopped at a red light using European naturalistic driving data collected through the UDRIVE project. Attention was given to the whole engagement process, including simple prevalence and the tasks’ relation (in terms of start/end) to the red light period. Moreover, given that texting is one of the most problematic forms of distraction, it was characterized in more detail regarding glance behavior. Method: Videos of 804 red light episodes from 159 drivers were annotated. Glance behavior was also coded for a sub-set of 75 texting events and their matched baselines. Results, conclusions and practical applications: Drivers engaged in at least one secondary task across almost half of the annotated red light episodes. Drivers who texted while stopped spent most of the time looking at their cell phone. Consequently, drivers might not have been prepared for potentially unexpected events once the light turned green. Further, drivers concluded texting a considerable number of times well after the red light period, which has potential implications for traffic safety.