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.     

Assessing characteristics related to the use of seatbelts and cell phones by drivers: Application of a bivariate probit model

IntroductionThe effects of cell phone use and safety belt use have been an important focus of research related to driver safety. Cell phone use has been shown to be a significant source of driver distraction contributing to substantial degradations in driver performance, while safety belts have been demonstrated to play a vital role in mitigating injuries to crash-involved occupants.MethodThis study examines the prevalence of cell phone use and safety belt non-use among the driving population through direct observation surveys. A bivariate probit model is developed to simultaneously examine the factors that affect cell phone and safety belt use among motor vehicle drivers.ResultsThe results show that several factors may influence drivers' decision to use cell phones and safety belts, and that these decisions are correlated.Practical applicationsUnderstanding the factors that affect both cell phone use and safety belt non-use is essential to targeting policy and programs that reduce such behavior.     

Creation of the Naturalistic Engagement in Secondary Tasks (NEST) distracted driving dataset

ProblemDistracted driving has become a topic of critical importance to driving safety research over the past several decades. Naturalistic driving data offer a unique opportunity to study how drivers engage with secondary tasks in real-world driving; however, the complexities involved with identifying and coding relevant epochs of naturalistic data have limited its accessibility to the general research community.MethodThis project was developed to help address this problem by creating an accessible dataset of driver behavior and situational factors observed during distraction-related safety-critical events and baseline driving epochs, using the Strategic Highway Research Program 2 (SHRP2) naturalistic dataset. The new NEST (Naturalistic Engagement in Secondary Tasks) dataset was created using crashes and near-crashes from the SHRP2 dataset that were identified as including secondary task engagement as a potential contributing factor. Data coding included frame-by-frame video analysis of secondary task and hands-on-wheel activity, as well as summary event information. In addition, information about each secondary task engagement within the trip prior to the crash/near-crash was coded at a higher level. Data were also coded for four baseline epochs and trips per safety-critical event.Results1,180 events and baseline epochs were coded, and a dataset was constructed. The project team is currently working to determine the most useful way to allow broad public access to the dataset.DiscussionWe anticipate that the NEST dataset will be extraordinarily useful in allowing qualified researchers access to timely, real-world data concerning how drivers interact with secondary tasks during safety-critical events and baseline driving.Practical applicationsThe coded dataset developed for this project will allow future researchers to have access to detailed data on driver secondary task engagement in the real world. It will be useful for standalone research, as well as for integration with additional SHRP2 data to enable the conduct of more complex research.     

Assessing drivers' response during automated driver support system failures with non-driving tasks

IntroductionWith the increase in automated driver support systems, drivers are shifting from operating their vehicles to supervising their automation. As a result, it is important to understand how drivers interact with these automated systems and evaluate their effect on driver responses to safety critical events. This study aimed to identify how drivers responded when experiencing a safety critical event in automated vehicles while also engaged in non-driving tasks.MethodIn total 48 participants were included in this driving simulator study with two levels of automated driving: (a) driving with no automation and (b) driving with adaptive cruise control (ACC) and lane keeping (LK) systems engaged; and also two levels of a non-driving task (a) watching a movie or (b) no non-driving task. In addition to driving performance measures, non-driving task performance and the mean glance duration for the non-driving task were compared between the two levels of automated driving.ResultsDrivers using the automated systems responded worse than those manually driving in terms of reaction time, lane departure duration, and maximum steering wheel angle to an induced lane departure event. These results also found that non-driving tasks further impaired driver responses to a safety critical event in the automated system condition.ConclusionIn the automated driving condition, driver responses to the safety critical events were slower, especially when engaged in a non-driving task.Practical applicationTraditional driver performance variables may not necessarily effectively and accurately evaluate driver responses to events when supervising autonomous vehicle systems. Thus, it is important to develop and use appropriate variables to quantify drivers' performance under these conditions.     

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。     

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.     

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.     

Are cellular phone blocking applications effective for novice teen drivers?

ProblemDistracted driving is a significant concern for novice teen drivers. Although cellular phone bans are applied in many jurisdictions to restrict cellular phone use, teen drivers often report making calls and texts while driving.MethodThe Minnesota Teen Driver Study incorporated cellular phone blocking functions via a software application for 182 novice teen drivers in two treatment conditions. The first condition included 92 teens who ran a driver support application on a smartphone that also blocked phone usage. The second condition included 90 teens who ran the same application with phone blocking but which also reported back to parents about monitored risky behaviors (e.g., speeding). A third control group consisting of 92 novice teen drivers had the application and phone-based software installed on the phones to record cellular phone (but not block it) use while driving.ResultsThe two treatment groups made significantly fewer calls and texts per mile driven compared to the control group. The control group data also demonstrated a higher propensity to text while driving rather than making calls.DiscussionSoftware that blocks cellular phone use (except 911) while driving can be effective at mitigating calling and texting for novice teen drivers. However, subjective data indicates that some teens were motivated to find ways around the software, as well as to use another teen's phone while driving when they were unable to use theirs.Practical applicationsCellular phone bans for calling and texting are the first step to changing behaviors associated with texting and driving, particularly among novice teen drivers. Blocking software has the additional potential to reduce impulsive calling and texting while driving among novice teen drivers who might logically know the risks, but for whom it is difficult to ignore calling or texting while driving.     

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.     

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.     

Using SHRP 2 naturalistic driving data to assess drivers' speed choice while being engaged in different secondary tasks

IntroductionThe engagement in secondary tasks while driving has been found to result in considerable impairments of driving performance. Texting has especially been suspected to be associated with an increased crash risk. At the same time, there is evidence that drivers use various self-regulating strategies to compensate for the increased demands caused by secondary task engagement. One of the findings reported from multiple studies is a reduction in driving speed. However, most of these studies are of experimental nature and do not let the drivers decide for themselves to (not) engage in the secondary task, and therefore, eliminate other strategies of self-regulation (e.g., postponing the task). The goal of the present analysis was to investigate if secondary task engagement results in speed adjustment also under naturalistic conditions.MethodOur analysis relied on data of the SHRP 2 naturalistic driving study. To minimize the influence of potentially confounding factors on drivers' speed choice, we focused on episodes of free flow driving on interstates/highways. Driving speed was analyzed before, during, and after texting, smoking, eating, and adjusting/monitoring radio or climate control; in a total of 403 episodes.ResultsData show some indication for speed adjustment for texting, especially when driving with high speed. However, the effect sizes were small and behavioral patterns varied considerably between drivers. The engagement in the other tasks did not influence drivers' speed behavior significantly.Conclusions and practical applicationsWhile drivers might indeed reduce speed slightly to accommodate for secondary task engagement, other forms of adaptation (e.g., strategic decisions) might play a more important role in a natural driving environment. The use of naturalistic driving data to study drivers' self-regulatory behavior at an operational level has proven to be promising. Still, in order to obtain a comprehensive understanding about drivers' self-regulatory behavior, a mixed-method approach is required.     

Quantifying driver cell phone use at signalized intersections using software-defined radio

Objective: This article outlines a data collection process that quantifies driver cell phone use using a software-defined radio (SDR) at a signalized intersection. Cell phone use while driving has been shown to be factor that increases the risk of a crash incident. Both operational and enforcement strategies can be applied at locations where high driver cell phone use is identified.

Methods: A baseline driver cell phone use observation was made at the intersection, where 9,699 vehicles were observed at the intersection of Carlton Road and State Route 31 (Pennington Road) in Ewing, New Jersey. An SDR cell phone detection device created as part of this study was then deployed at the same intersection to determine whether the SDR device could detect an active cell phone signal. The identification of vehicle cell phone activity using the SDR was conducted a sample of 4,000 vehicles. A visual observation, along with a motion detection camera, was made alongside the SDR to visually confirm cell phones use.

Results: Of the 4,000 vehicles sampled using the SDR cell phone detection device, 6.1% of the a.m. peak travel time and 7.6% of the p.m. peak travel time had an active cellular device. A concurrent visual field verification of driver cell phone use showed that approximately 57% (a.m. peak) and 67% (p.m. peak) of the SDR-detected cell phones were visually confirmed to be associated with distracted cell phone use.

Conclusions: Once characterized, the frequency of driver cell phone use can be used to justify changes to signal timing protocols. These adjustments could include extending the signal’s “all-red time” or holding “yellow time” longer in order to properly clear the intersection. These data can also be used to identify locations that may require more enforcement measures to dissuade driver cell phone use. Furthermore, the impact of anti–cell phone campaigns or new laws can be quantified by measuring before and after cell phone use in the near term rather than waiting for crash studies at intersections to be completed and analyzed.     

Examining drivers' eye glance patterns during distracted driving: Insights from scanning randomness and glance transition matrix

IntroductionVisual attention to the driving environment is of great importance for road safety. Eye glance behavior has been used as an indicator of distracted driving. This study examined and quantified drivers' glance patterns and features during distracted driving.MethodData from an existing naturalistic driving study were used. Entropy rate was calculated and used to assess the randomness associated with drivers' scanning patterns. A glance-transition proportion matrix was defined to quantity visual search patterns transitioning among four main eye glance locations while driving (i.e., forward on-road, phone, mirrors and others). All measurements were calculated within a 5 s time window under both cell phone and non-cell phone use conditions.ResultsResults of the glance data analyses showed different patterns between distracted and non-distracted driving, featured by a higher entropy rate value and highly biased attention transferring between forward and phone locations during distracted driving. Drivers in general had higher number of glance transitions, and their on-road glance duration was significantly shorter during distracted driving when compared to non-distracted driving.ConclusionsResults suggest that drivers have a higher scanning randomness/disorder level and shift their main attention from surrounding areas towards phone area when engaging in visual-manual tasks.Practical applicationsDrivers' visual search patterns during visual-manual distraction with a high scanning randomness and a high proportion of eye glance transitions towards the location of the phone provide insight into driver distraction detection. This will help to inform the design of in-vehicle human-machine interface/systems.     

Naturalistic teenage driving study: Findings and lessons learned

IntroductionThis paper summarizes the findings on novice teenage driving outcomes (e.g., crashes and risky driving behaviors) from the Naturalistic Teenage Driving Study.MethodSurvey and driving data from a data acquisition system (global positioning system, accelerometers, cameras) were collected from 42 newly licensed teenage drivers and their parents during the first 18 months of teenage licensure; stress responsivity was also measured in teenagers.ResultOverall teenage crash and near-crash (CNC) rates declined over time, but were > 4 times higher among teenagers than adults. Contributing factors to teenage CNC rates included secondary task engagement (e.g., distraction), kinematic risky driving, low stress responsivity, and risky social norms.ConclusionsThe data support the contention that the high novice teenage CNC risk is due both to inexperience and risky driving behavior, particularly kinematic risky driving and secondary task engagement.Practical ApplicationsGraduated driver licensing policy and other prevention efforts should focus on kinematic risky driving, secondary task engagement, and risky social norms.     

In-vehicle workload assessment: effects of traffic situations and cellular telephone use

PROBLEM: Assessment of drivers' on-road workload is an important traffic safety consideration. This study was conducted to examine the effects of cellular phone communication on driving performance, with particular emphasis on variations in task demand in different traffic situations. METHOD: Twelve participants were asked to drive on urban roads and motorways with or without concomitant mathematical-addition tests relayed via cellular phone. Measurements included task and driving performance, physiological responses, and compensatory behavior. RESULTS: Analysis of task performance revealed that mean response time was markedly increased (11.9%) for driving on urban roads compared to motorways. The mean driving speed only decreased 5.8% in the presence of phone tasks in comparison to normal driving without distractions. In addition, overall physiological workload increased through compensatory behavior in response to the phone tasks. CONCLUSIONS: Driving with phone use in different traffic environments induced measurable variations in driver workload. IMPACT ON INDUSTRY: When faced with heavy traffic, a greater safety margin is typically adopted, with more lowered driving speed and restricted phone use, and it can be assumed that there is a general trade-off between tasks to preserve driving safety.     

Crashes and near-crashes on horizontal curves along rural two-lane highways: Analysis of naturalistic driving data

IntroductionPrior research has shown the probability of a crash occurring on horizontal curves to be significantly higher than on similar tangent segments, and a disproportionally higher number of curve-related crashes occurred in rural areas. Challenges arise when analyzing the safety of horizontal curves due to imprecision in integrating information as to the temporal and spatial characteristics of each crash with specific curves.MethodsThe second Strategic Highway Research Program(SHRP 2) conducted a large-scale naturalistic driving study (NDS),which provides a unique opportunity to better understand the contributing factors leading to crash or near-crash events. This study utilizes high-resolution behavioral data from the NDS to identify factors associated with 108 safety critical events (i.e., crashes or near-crashes) on rural two-lane curves. A case-control approach is utilized wherein these events are compared to 216 normal, baseline-driving events. The variables examined in this study include driver demographic characteristics, details of the traffic environment and roadway geometry, as well as driver behaviors such as in-vehicle distractions.ResultsLogistic regression models are estimated to discern those factors affecting the likelihood of a driver being crash-involved. These factors include high-risk behaviors, such as speeding and visual distractions, as well as curve design elements and other roadway characteristics such as pavement surface conditions.ConclusionsThis paper successfully integrated driver behavior, vehicle characteristics, and roadway environments into the same model. Logistic regression model was found to be an effective way to investigate crash risks using naturalistic driving data.Practical ApplicationsThis paper revealed a number of contributing factors to crashes on rural two-lane curves, which has important implications in traffic safety policy and curve geometry design. This paper also discussed limitations and lessons learned from working with the SHRP 2 NDS data. It will benefit future researchers who work with similar type of data.     

Effect of age and secondary task engagement on motor vehicle crashes in a naturalistic setting

ProblemAutomobile crashes are one of the leading causes of death in the United States, especially for younger and older drivers. Additionally, distracted driving is another leading factor in the likelihood of crashes. However, there is little understanding about the interaction between age and secondary task engagement and how that impacts crash likelihood and maneuver safety.MethodData from the Naturalistic Driving Study (NDS), which was part of the Second Strategic Highway Research Program (SHRP2), were used to investigate this issue.ResultsIt was found that the distribution of crashes per one million km driven during the NDS was similar to previous research, but with fewer crashes from older drivers. Additionally, it was found that older and middle-aged drivers engaged in distracted driving more frequently than was expected, and that crashes were significantly more likely if drivers of those age groups were engaged in secondary tasks. However, secondary task engagement did not predict judgment of safe/unsafe vehicle maneuvers.Practical ApplicationsMore research is needed to better understand the interaction of age and distraction on crash likelihood. However, this research could aid future researchers in understanding the likelihood of future use of new in-vehicle technologies for different age groups, as well as provide insight to the engagement patterns of distraction for different age groups.     

Post-trip safety interventions: State-of-the-art,challenges, and practical implications

Introduction: Currently, risky driving behaviour is a major contributor to road crashes and as a result, wide array of tools have been developed in order to record and improve driving behaviour. Within that group of tools, interventions have been indicated to significantly enhance driving behaviour and road safety. This study critically reviews monitoring technologies that provide post-trip interventions, such as retrospective visual feedback, gamification, rewards or penalties, in order to inform an appropriate driver mentoring strategy delivered after each trip. Method: The work presented here is part of the European Commission H2020 i-DREAMS project. The reviewed platform characteristics were obtained through commercially available solutions as well as a comprehensive literature search in popular scientific databases, such as Scopus and Google Scholar. Focus was given on state-of-the-art-technologies for post-trip interventions utilized in four different transport modes (i.e. car, truck, bus and rail) associated with risk prevention and mitigation. Results: The synthesized results revealed that smartphone applications and web-based platforms are the most accepted, frequently and easiest to use tools in cars, buses and trucks across all papers considered, while limited evidence of post-trip interventions in -rail was found. The majority of smartphone applications detected mobile phone use and harsh events and provided individual performance scores, while in-vehicle systems provided delayed visual reports through a web-based platform. Conclusions: Gamification and appropriate rewards appeared to be effective solutions, as it was found that they keep drivers motivated in improving their driving skills, but it was clear that these cannot be performed in isolation and a combination with other strategies (i.e. driver coaching and support) might be beneficial. Nevertheless, as there is no holistic and cross-modal post-trip intervention solution developed in real-world environments, challenges associated with post-trip feedback provision and suggestions on practical implementation are also provided.     

Role of sensation seeking and attitudes as mediators between age of driver and risky driving of Powered Two Wheelers

IntroductionThis study examines the effect of age of driver on risky driving of Powered Two-Wheelers (PTW) employing sensation seeking and safety attitudes as mediators.MethodsA survey was conducted with 1299 PTW drivers (1089 males and 210 females) within the age of 18 and 63 years, living in the state of Kerala, India. The questionnaire consisted of 31 items to measure sensation seeking, safety attitude, and risky driving of the drivers.ResultsMediation models were examined using sensation seeking as mediator and secondly safety attitudes as mediators. The relationship between the driver's age and risky driving was fully mediated by all the three variables.Practical applicationsResults of this study suggest that safety strategies should be employed to reduce risky driving tendencies that could be achieved by shaping or adapting the mediators (reducing sensation seeking and enhancing safety attitudes). This goal could be reached by starting to educate children about this at an early stage when they are at school as well as by social learning and safety awareness campaigns.