Modeling safety risk perception due to mobile phone ...Modeling safety risk perception due to mobile phone distraction among four wheeler drivers Raghunathan Rajesha,⁎, R. Srinatha,R.Sasikumara,B.Subinb

IATSS Research xxx (2016) xxx–xxx

IATSSR-00131; No of Pages 8

Contents lists available at ScienceDirect

IATSS Research

Modeling safety risk perception due to mobile phone distraction amongfour wheeler drivers

Raghunathan Rajesh a,⁎, R. Srinath a, R. Sasikumar a, B. Subin b

a Department of Mechanical Engineering, Rajiv Gandhi Institute of Technology, Velloor P.O., Kottayam, Kerala, Indiab National Transportation Planning and Research Centre, Thiruvananthapuram, Kerala, India

⁎ Corresponding author.E-mail addresses: [email protected] (R. Rajesh)

(R. Srinath), [email protected] (R. Sasikumar), mailmesuPeer review under responsibility of International A

Sciences.

http://dx.doi.org/10.1016/j.iatssr.2016.08.0020386-1112/© 2016 International Association of Traffic andcreativecommons.org/licenses/by-nc-nd/4.0/).

Please cite this article as: R. Rajesh, et al., Mosearch (2016), http://dx.doi.org/10.1016/j.ia

a b s t r a c t
a r t i c l e i n f o
Available online xxxx
Nowadays, there is an increasing trend in the use of information and communication technology devices in newvehicles. Due to these increasing service facilities, driver distraction has become a major concern for transporta-tion safety. To reduce safety risks, it is crucial to understand how distracting activities affect driver behavior atdifferent levels of vehicle control. The objective of this work is to understand how the vehicle and driver charac-teristics influence mobile phone usage while driving and associated risk perception of road safety incidents.Based on literature review, a man–machine framework for distracted driving and a mobile phone distractionmodel is presented. The study highlights the findings from a questionnaire survey conducted in Kerala, India.The questionnaire uses a 5-point Likert scale. Responses from 1203 four-wheeler drivers are collected using ran-dom sampling approach. The questionnaire items associated with three driver-drive characteristics are:(i) Human Factors (age, experience, emotional state, behavior of driver), (ii) Driver space (meter, controls,light, heat, steering, actuators of vehicle), (iii) Driving conditions (speed, distance, duration, traffic, signals).Thismobile phone distractionmodel is tested using structural equationmodeling procedure. The study indicatesthat among the three characteristics, ‘Human Factors’ has the highest influence on perceived distraction due tomobile phones. It is also observed that safety risk perception due tomobile phone usage while driving is moder-ate. The practical relevance of the study is to place emphasis on behavior-based controls and to focus on strategiesleveraging perception of distraction due to mobile phones while driving.© 2016 International Association of Traffic and Safety Sciences. Production and hosting by Elsevier Ltd. This is an
open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords:Four wheelerMobile phoneDistracted drivingMan–machine frameworkStructural equation model

1. Introduction

The use of information and communication technology devices infour wheelers is on the rise. Such technological developments, whileadding ease to life, increase the potential for drivers to engage in sec-ondary tasks while driving [1,2]. Recent studies report phone use expo-sure estimates in the range 30–60% in a few developed countries [3].The proportion of drivers using mobile phones has been increasedover the past 5–10 years [4]. Driver distraction has become amajor con-cern for transportation safety.

Nature, severity, and frequency of distractions affect the safetyof drivers, passengers, and vulnerable road users [5]. There is agrowing body of evidence which shows that the distraction causedby mobile phones can impair performance in a number of ways,

, [email protected]@gmail.com (B. Subin).ssociation of Traffic and Safety

Safety Sciences. Production and hos

deling safety risk perception dtssr.2016.08.002

e.g., longer reaction time to external stimuli (notably brakingtime, response to traffic signals), impaired ability to maintain thecorrect lane, shorter following distances and an overall reductionin awareness of the driving situation [4,6]. Most secondary taskslead to a decrease in driving speed, while visual–manual tasks ad-ditionally take driver's eyes of the road, deteriorating the lateralperformance [2]. The impact of using a mobile phone on crashrisk is difficult to ascertain, but studies suggest that drivers usingmobile phones are approximately four times more likely to be in-volved in crashes [4].

To reduce safety risks, it is crucial to understandhowdistracting activ-ities affect driver behavior at different levels of vehicle control. Theremaybe more than one reason or factor that motivates a person to involve insome secondary activity. The objective of the paper is to examine therole of vehicle and driver factors on risk perception of road safety inci-dents arising from the use of mobile phones while driving. First, a shortliterature review of a man–machine framework for distracted driving ispresented (Section 2). Next section deals with distracted driving due tomobile phones by proposing a ‘mobile phone distraction model’. InSection 3, the study methodology highlighting ‘mobile phone distractionmodel’, questionnaire survey and the analysis procedure are presented.

ting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://

ue tomobile phone distraction among four wheeler drivers, IATSS Re-

0opyright_ulicense

http://dx.doi.org/10.1016/j.iatssr.2016.08.002

mailto:[email protected]

Journal logo


0opyright_ulicense

0opyright_ulicense

http://www.sciencedirect.com/science/journal/723942


2 R. Rajesh et al. / IATSS Research xxx (2016) xxx–xxx

Results and discussion from the structural equation model (SEM) areshown in Sections 4 and 5 respectively. Finally, the conclusions of thisstudy are presented in Section 6.

2. Literature review

Driving is a very critical task that requires undivided attention andcomplete commitment of associated skills of the driver. Driver distrac-tion is defined as a diversion of attention from activities critical tosafe-driving for performing any secondary competing activity [2]. Dis-traction to the driver occurs from any secondary physical or mental ac-tivity that shifts the attention of the driver from safe handling of theautomobile [7]. Some distractions are initiated by the driver, and othersare acute situations that demand a quick response from the driver [8].Most in-vehicle distractions belong to the former, whereasmost outsidevehicle distractions are of the latter.

Man–machine interactions between the driver and device may in-volve inputs such as visual, audio, or tactile inputs from the driver; andoutputs like manual or voice responses. The secondary tasks which po-tentially distract the driver from the safe operation of the vehicle includeinteracting with a passenger, conversing on mobile phone, text messag-ing, use of smartphones and other office devices, navigation aids, back-ground music, adjusting audio-video players, eating and drinking,manipulating in-vehicle environmental attributes, and seeking for ob-jects. Fig. 1 presents theman–machine framework [9] for distracted driv-ing. The major four-wheeler driver and driving characteristics that affectsafety include ‘Human characteristics’, ‘Driving Conditions’, ‘Driver Space’and ‘Interaction characteristics’ due to in-vehicle device and their use(Table 1).

The chain of events in the man–machine model are factors affectingthe driving task (Human Factors, Driver space and Driving conditions),immediate-state and end-state (Fig. 1). ‘Driver Space’ is part of driver-vehicle interaction (inside vehicle) and ‘Driving Conditions’ is a part ofvehicle-environment interaction (outside vehicle). Driver–vehicle in-teractions along with secondary tasks cause driving distraction and re-sult in the immediate-state (loss of focus, steering control or pedalresponse). Driving distraction occurs in visual, manual, cognitive andaudio forms [1], and can be studied through the multiple resource the-ory to examine the driver performance decrement [2]. According tothemultiple resource theory, the resources allocated for visual attentionand central processingwhile driving are forced to be divided by second-ary tasks. Secondary tasks demanding these two types of resources(e.g., use of cell phone and navigation aid) pose visual and cognitive

Fig. 1.Man–machine mode

Please cite this article as: R. Rajesh, et al., Modeling safety risk perception dsearch (2016), http://dx.doi.org/10.1016/j.iatssr.2016.08.002

distractions while driving. Both visual and cognitive distractions in-crease driver workload and thereby influence the vehicle control andgaze behavior [2]. When distracted, drivers tend to place less emphasison the visual scanning in favor of activities related to vehicle control[32]. Distraction enhances the chance of driving errors [33] and reducessituational awareness [34]. Theman–machine interactions are indicatedby the immediate states or driver performance indicators such as num-ber of glances & glance time, speed, lateral position, posture, steeringerror, mental effort, NASA Task Load Index, time for detection of infor-mation, reaction time, lag distance, heart rate and committing errors.The end states are events or conditions that reflect a state of higher safe-ty risk.

Intervention strategies to address distracted driving include legisla-tion, enforcement, blocking technologies, using social media, educationand transforming social norms [31,35–37]. Education related interven-tion strategy is based on risk perception of drivers who undertake dis-tracted driving. Numerous literature indicate the difference in riskperception by different age groups, gender, in-vehicle devices or natureof secondary tasks [3,38,39]. Overall, the Man–machine model for dis-tracted driving provides a useful framework for examining the role ofmajor dimensions of driver-drive characteristics on distraction due tosecondary tasks while driving.

3. Methodology

Considering the Man–machine model this paper specifically focuseson one of the secondary tasks, i.e., use ofmobile phones. Amobile phonedistraction model is proposed and validated.

3.1. Mobile phone distraction model

The mobile phone distraction model shown in Fig. 2 has threeconstructs, ‘Human Factors’, ‘Driver Space’ and ‘Driving Conditions’that influence the driver's distraction due to a secondary task,i.e., ‘Distraction_Mobile’. The focus in ‘Distraction_Mobile’ is on the im-mediate effect due to its use, i.e., loss of focus or steering control. This isrepresented as the immediate-state in Fig. 1. The driver's responsewhile driving with secondary tasks results in end states such as ‘acci-dents’, ‘near miss’ and ‘erratic driving’. Overall the model representsthe influence of ‘Human Factors’, ‘Driver Space’ and ‘Driving Conditions’on the risk perception of the driver towards safety incidents arisingfrom the use ofmobile phoneswhile driving (i.e., ‘MobileUse’). Four hy-potheses are tested in the mobile distraction model. First, the model

l for distracted driving.



Table 1Key findings from literature review.

Factor Authors Key findings

Human characteristics(Age, Experience, Gender,Behaviour)

Young & Lenne [3]; Lam [8]; Singh [10]; Simons-Morton et al. [11]; Younger drivers are most distracted and are more prone to incidents, theirrisk perception and strategies differ from older drivers.

Knapper et, al. [1]; Shinar et al. [12]; Driving experience or experience in using in-vehicle devices has impact ondistraction.

Singh [10] Exposure to secondary tasks and safety risks are different among male andfemale drivers.

Chen [13]; Regan et al. [14]; Vardaki et al. [15] Behaviour and states of driver influence exposure to secondary tasksDriving conditions(Speed, Traffic, Duration)

Johnson et al. [16]; Cooper et al. [17]; Stavrinos et al. [18] External condition such as traffic, duration etc., places higher vehicle handlingdemands and affect driver responses or compensating strategies adopted.

Driver space(Seating, Driving controls &Display, Heat)

Golias et al. [19]; Lenzuni et al. [20] In vehicle condition potentially affects the state of the driverLamble et al. [21]; Dukic et al. [22]; Ryu & Lee [23] Position of displays and controls affect the driver responses

Interaction characteristics(In vehicle devices, Humanresources)

Kaber et al. [2]; Collet et al. [24]; Unal et al. [25]; Lansdown et al.[26]; Young et al. [27]; Cuenen et al. [28]

Secondary tasks compete for limited resource of driver, influence this resourceallocation and consequently affect the driver response.

Knapper et al. [1]; Young & Lenne [3]; Shinohara et al. [6]; Colletet al. [24]; Brodsky et al. [29]

Nature of in-vehicle devices & tasks influence the physical or mental facultiesemployed and therefore the driver responses or safety incidents and risks.

Knapper et al. [1]; Kaber et al. [2]; Shinohara et al. [6]; Cooper at al[17].; Patten et al. [30]; Birrell et al. [37]; Briggs et al. [43];Cairdet al. [47]

Nature of immediate states or driver performance indicators include numberof glances & glance time, speed, lateral position, posture, steering error, mentaleffort/ NASA Task Load Index, time for detection of information, reaction time,lag distance, heart rate, errors

Safety impact Bingham [7]; Lam [8]; Stavrinos et al. [18]; Overton et al. [31] Nature of end results or indicators include fatality, injuries, driver performance,negatively influenced traffic flow, increased congestion

3R. Rajesh et al. / IATSS Research xxx (2016) xxx–xxx

hypothesizes that unfavorable ‘Human Factors’, ‘Driver Space’ and‘Driving Conditions’ dimensions lead to stronger distraction perception(i.e., loss of control & focus) due tomobile phone use. Second, themodelhypothesizes that stronger distraction perception from mobile use im-parts a higher safety-risk perception. Third, the model hypothesizesthat ‘Human Factors’, ‘Driver Space’ and ‘Driving Conditions’ dimen-sions are enablers to stronger safety-risk perception due to mobilephones. Fourth, the model hypothesizes that a stronger distractionand safety-risk perception due to mobile phones reduce the intentionof the driver to use it. In this study ‘Risk Perception’ represent theend-state of the four wheeler drivers as shown in the man–machinemodel (Fig. 1). ‘Risk Perception–Mobile Use’ link is the test of hypothe-sis that ‘increased risk’ leads to reducedmobile use. This link is exploredto leverage ‘education’ based intervention strategies (i.e., emphasizingon risk perception) to improve transport safety.

3.2. Instrument

From the literature review, critical factors leading to distracted drivingwere identified. An initial questionnaire was prepared and a pilot surveywas conducted from a sample of drivers (n = 110) drawn from one dis-trict. Items with low Cronbach's alpha (b0.6) were used to modify thequestionnaire and the survey was conducted again. The final question-naire was divided into three parts; Part A focused on extent of secondarytasks undertaken by drivers and included questions on the extent towhich the respondents experience distractions inside the vehicle whiledriving. Part A used 5-point Likert scale with assigned values ranging

Fig. 2.Mobile phone distr


from 1 being ‘Never’ to 5 being ‘Regularly’. Part B incorporated questionson perception towards immediate response state due to in-vehicle dis-tractions and Part C contained questions on the risk perception associatedwith distractions mentioned in part B. Parts B and C also used 5-pointLikert scale with assigned values ranging from 1 being ‘Strongly disagree’to 5 being ‘Strongly Agree’. Considering the mobile phone distractionmodel (Fig. 2), Part B is used to model ‘Human Factors’, ‘Driver Space’,‘Driving Conditions’ and ‘Distraction_Mobile’; Part C is used to model‘Risk Perception’; and Part A is used tomodel ‘Mobile Use’. A set of samplequestions are presented in Table 2. For data collection drivers wereapproached personally and in addition, an online survey was conducted.The final Cronbach's alpha of the questionnaire was found to be 0.862.

3.3. Data analysis

Missing values and outliers of all measured variables is examined topurify the data and reduce systematic errors, i.e., error due to instru-ment ormishandling of the same. Confirmatory factor analysis is under-taken to test and validate the proposed model. A structural equationmodel (SEM) is developed using SPSS AMOS software. Structural equa-tion model is designed to evaluate how well a proposed conceptualmodel consisting of observed indicators and hypothetical constructs ex-plains orfits the collected data. The estimation of parameters is based onthemaximum likelihood method. The following indices have been cho-sen in this study: root mean residual (RMR), goodness of fit index (GFI),adjusted goodness of fit index (AGFI), comparative fit index (CFI), androot mean square error of approximation (RMSEA). The default model

acted driving model.



Table 2Sample questions and scale.

Sample question Scale

Part A Q9: How often do you use a mobile phone while driving? Never Rarely Sometimes Often RegularlyPart B Q24: Looking frequently at the meter console while driving increases the chance of momentary loss offocus.

Strongly disagree Disagree Neutral Agree Strongly agree

Part B Q25: Attending/making a cell phone call (hand held), reading/typing text messages, emails and socialupdates while driving can cause the driver lose attention and driving control momentarily.


Part B Q32: Undertaking a secondary task while driving in congested traffic conditions enhance the probabilityof losing focus and steering control momentarily.


Part C Q45: Increased chance of crashes/near misses/sudden braking situations or uneven driving patterns/lanedeviations because of using a mobile phone while driving



is checked for fitness and improved by carrying outmodifications basedon the ‘p’ (significance) value of the regression coefficients and themodification indices.

4. Structural equation modelling

4.1. Descriptive statistics

The survey is conducted over a period of 3 months and a total of1203 responses are collected from 5 districts in Kerala, India. Themean age (standard deviation) of the respondents is 33.14 (SD =10.66) years. The sample consisted of largely male drivers (84.8%) andmost of them are private drivers (79.6%). Table 3 shows the summaryof responses pertaining to age, driving experience and speed. Amongthe respondents, 15.6% have been penalized for driving faults and 16%have reported involvement in accidents. Further descriptive statisticsfrom the survey can be found in Srinath et al. [40].

4.2. Modelling

Fig. 3 shows the mobile phone distracted driving model with four la-tent constructs ‘Human Factors’, ‘Driving Conditions’, ‘Driver Space’ and‘Distraction Mobile’ and its items. The measurement model is developedand shown in Fig. 4. The measurement model fitness indices are, χ2 =234.034 (df = 59, p = 0.000), RMR = 0.034, GFI = 0.955, AGFI =0.931, CFI = 0.918 and RMSEA = 0.062. The value of RMR should beless than 0.1 and that of GFI, AGFI and CFI should be greater than 0.9which indicate good fitness and acceptability of the model. The value ofRMSEA indicates a reasonable fit since it is between 0.05 and 0.08.

The various goodness-of-fit statistics indicate that themeasurementhas a good fit with the obtained data. Themeasurementmodel has beentransformed to a structural model to test the stated hypotheses(Section 3.1). The default SEM is shown in Fig. 5. Modification indicesand critical ratios of path coefficients from AMOS have been examinedto modify the default SEM. First, the insignificant links ‘Human Fac-tors–Risk Perception’, ‘Driving condition–Distraction_Mobile’, ‘DriverSpace–Risk Perception’ and ‘Risk Perception–Mobile Use’ are removedto improvemodel fitness. Next, the error terms of ‘emotion’ and ‘behav-ior’ are covaried, and that of ‘speed’ and ‘duration’ are covaried to im-prove the model fitness. The final SEM is shown in Fig. 6 and themodel fit indices are given in Table 4. In the model, it is seen thatthere are direct effects of human factors and drive space on perceptionlevel of distraction due to mobile phones. The effect of ‘Human Factors’on mobile distraction is 0.62 and that of ‘Driver Space’ on the latter is

Table 3Summary of responses.

Age 18–30 years 31–40 years 41–50 years N50 years50.59% 23.44% 27.93% 7.64%

Experience b 5 years 5–10 years 10–15 years N15 years41.97% 32.83% 13.96% 10.05%

Driving speed b40 kph 40–60 kph 60–80 kph N80 kph9.64% 65.5% 21.6% 1.5%


0.2. ‘Driver Space’ has an inverse relationship with the risk of traffic in-cidents due to use of mobile phones. The loading of perceived distrac-tion due to mobile phones on risk of incidents is 0.33. The influence of‘Driving Conditions’ has no significant direct relation to mobile usebeing perceived as a distraction and is hence removed in the finalmodel. The model fit indices are found to be acceptable (Table 4). Theregression coefficients for the paths in the mobile phone distractionmodel (Fig. 6) obtained through the maximum likelihood method isshown in Table 5. The critical ratios for estimates of covariance betweenthe constructs and variance of indicators were observed to be greaterthan 1.96 and p b 0.05.

5. Discussion

5.1. Effect of man–machine factors on mobile distraction

The loading weight of 0.62 (Fig. 6) between ‘Human Factors’ and‘Distraction_mobile’ indicates that attributes like age, experience, emo-tion and behavior have significant contribution towards mobile phonedistraction. The effect of ‘Human Factors’ is attributed to Age, Experi-ence, Emotion and Behaviour with loading weights of 0.44, 0.66, 0.59and 0.64 respectively (Table 5). Horberry et al. [41] and Simons-Morton et al. [11] highlighted significant differences in young and olddrivers in undertaking distracting activities. Shinar et al. [12] pointedthat as experience builds up, the drivers get more accustomed to theroutine task of driving and additional activities. Romer et al. [42] ex-plained the role of deficient situation awareness in novice drivers for at-tention failures. Brigs et al. [43] concluded that the more emotionallydrivers are involved in a conversation, the greater potential for distrac-tion exists. Simons-Morton et al. [11] andWestlake& Boyle [44] have at-tributed risk taking behaviour to driver distraction and accidents.

Drivers perceive facilities inside the vehicle such as lighting, meterconsole, equipment controls andmusic as enablers tomobile phonedis-traction with a regression weight of 0.2 (Table 5). The loading weightsfor lighting, meter console, equipment controls and music on ‘DriverSpace’ are 0.62, 0.51, 0.65 and 0.45 respectively (Table 5). Golias et al.[19] pointed out that driver convenience is a key factor that affects per-formance and the interior space has a role in determining the same. In-terestingly, ‘Driver Space–Risk Perception’ (−0.09) link does not turnup significant as hypothesized in our model (Fig. 5). Perhaps the needfor mobile use outweighs the risk perception. Another hypothesis thathas been rejected is the influence of ‘Driving Conditions’ on‘Mobile_distraction’. This finding is rather unexpected and needs to beexamined against the backdrop that ‘Driving Conditions’ influences‘Risk Perception’ (0.20).

5.2. Distracted driving and safety risk perception

The loadingweight of 0.33 (Fig. 6) between ‘Mobile_distraction’ and‘Risk Perception’ indicate that the perceived distraction due to use ofmobile phones does increase the negative safety risk perception. Themoderate level of impact of mobile distraction on risk perception canbe attributed to the counter effect of purpose of using phone on the



Fig. 3.Mobile phone distraction model with constructs and items loaded.


driver's perception of risk. Studies by Atchley et al. [38] and Nelson et al.[45] indicates that distracted driving behavior itself change attitudes to-wards risk. In addition, a number of existing research describes the com-pensatory strategies adopted by drivers in order to allocate the physio-cognitive elements required for carrying out secondary tasks [24,46].

5.3. Effect of man–machine factors on safety risk perception

The third hypothesis of the present study is that ‘Human Factors’,‘Driver Space’ and ‘Driving Conditions’ dimensions are enablers to highersafety-risk perception due to mobile phones. These relationships have

Fig. 4.Measurement mode


been examined in the default model. Two of the paths have turned outto be insignificant. First, the standardized estimates in the default struc-tural model show that the relation between ‘Human Factors’ and ‘RiskPerception’ (0.05) is not significant. This could perhaps be due to thelack of awareness and ability of the respondents to assess the human lim-itations in driving. Another reason could be that of learning effect fromrepeated use of mobile phones reducing their risk perception. Second,the relationship between ‘Driver Space’ and ‘Risk Perception’ is foundto have a regression weight of −0.09 which is also insignificant. But itis interesting to find that ‘Driver Space’ does have an impact on distrac-tion due to mobile use. This means that even though the drivers identify

l for the four factors.



Fig. 5. Default structural equation model of safety risk perception due to mobile phone distraction.


distracting sources and activities inside the vehicle, they do not associatesafety risk perception with it (i.e., direct effect). With these insignificantlinks removed, the only factor that has an influence on the safety riskperception is the ‘Driving Conditions’ (0.2). This relation shows that theprobability of perceiving the safety risk associatedwith the use ofmobilephones while driving is dependent on the traffic density, duration oftravel and driving speed (Fig. 6). Meta analysis done by Caird et al. [47]highlight that cell phone conversation while driving increases reactiontime to events and stimuli, and drivers compensate for potential reactiontime decrements by speed reduction strategies.

5.4. Distracted driving and mobile use

A variable that measures the exposure to mobile phone use whiledriving has been included in the model. The hypothesis in the ‘RiskPerception–Mobile Use’ link is that a driver perceives using mobile

Fig. 6. Final structural equation model of safety ris


phone reduces safety, it leads to reduced mobile use. In this study aloading of −0.27 (Table 5) has been obtained between perceptionof distraction due tomobile phones and the exposure variable of mo-bile use. But the link ‘Risk Perception–Mobile Use’ has turned out tobe insignificant. This implies that the moderating effect on mobileuse is rather due to distraction of mobile use than due to higher safe-ty risk perception. Hence, the fourth hypothesis has been only partlyconfirmed. One possible explanation to the insignificance of the link‘Risk Perception–Mobile Use’ is the emphasis on purpose and habit ofusing phones than on the risk perception towards its use while driv-ing. Similar findings have been previously reported by Nelson et al.[45]. Choosing to engage in the behavior itself changes attitudetowards risk [38]. Probably, the perception and confidence ofdrivers towards such strategies affect the overall risk perception,thereby preventing it from acting as deterrent to mobile use whiledriving.

k perception due to mobile phone distraction.



Table 4Indices of Model Fit.

Model χ2 (df), p RMR GFI AGFI CFI RMSEA

Default 277.648 (80), .000 0.033 0.953 0.930 0.917 0.057Modification-1:Remove insignificant links

282.167 (83), .000 0.033 0.953 0.932 0.916 0.056

Modification-2:Covariance of error terms

259.010 (82), .000 0.033 0.956 0.936 0.925 0.053

Final 259.010 (82), .000 0.033 0.956 0.936 0.925 0.053


5.5. Scope and limitation

The practical relevance of the study is to place emphasis on behaviorbased controls to reduce mobile phone use. Strategies focusing on ‘Mo-bile distraction’ perception are likely to mitigate the problems arouseddue to use of mobile phones while driving.

There is a need to explore the ‘Risk Perception–Mobile Use’ link to-wards understanding why risk perception does not have a moderatingeffect on mobile use. In addition, the effect of other secondary taskssuch as ‘Passenger conversation’, ‘Eating–Drinking’ and ‘Other in vehicletechnologies’ such as personal assistants, laptops and tablet, computers,iPads, wireless and auxiliary input communication devices, navigationaids on distracted driving need to be examined. Further scope for re-search lies in classifying responses across different age groups, gender,profession, experience, etc., and modeling the effect of in-vehicle dis-traction under those control criteria.

6. Conclusion

Driver distraction due to evolution in vehicle information & commu-nication technology devices has become amajor concern for transporta-tion safety. A Man–machine framework for distracted driving waspresented and a mobile phone distraction model was proposed. The in-fluence of ‘Human factors’, ‘Driver space’ and ‘Driving conditions’ onperception of mobile phone use while driving as a distraction were an-alyzed alongwith the associated risk of safety incidents. A questionnairewas prepared, pilot tested, modified and responses were collected from1203 four wheeler drivers. Structural equation modeling was done toidentify the effect of factors. The final SEM had model fitness parame-ters, χ2 = 252.827 (df = 80, p = 0.000), RMR = 0.032, GFI = 0.957,AGFI = 0.936, CFI = 0.927 and RMSEA = 0.053. The study revealsthat among the three driver-drive characteristics ‘Human Factors’ influ-ence the distraction perception due to mobile phone the most. It is also

Table 5Regression weight estimates for the structural paths of mobile phone distraction model.

Standardizedregressioncoefficient CR p

Emotion ← Human Factors 0.587 12.275 ⁎⁎⁎

Behaviour ← Human Factors 0.642 13.185 ⁎⁎⁎

Experience ← Human Factors 0.660Age ← Human Factors 0.444 10.110 ⁎⁎⁎

Meter ← Driver Space 0.511 10.316 ⁎⁎⁎

Controls ← Driver Space 0.647 11.788 ⁎⁎⁎

Music ← Driver Space 0.449 9.377 ⁎⁎⁎

Light ← Driver Space 0.624Speed ← Driving Conditions 0.480 10.239 ⁎⁎⁎

Duration ← Driving Conditions 0.574 11.896 ⁎⁎⁎

Traffic ← Driving Conditions 0.682Risk perception ← Distraction_mobile 0.334 4.916 ⁎⁎⁎

Hand activity ← Distraction_mobile 0.765Listening ← Distraction_mobile 0.587 12.253 ⁎⁎⁎

Distraction_mobile ← Human_Factors 0.618 8.673 ⁎⁎⁎

Distraction_mobile ← Driver Space 0.195 2.985 0.003Risk perception ← Driving Conditions 0.205 3.132 0.002Mobile use ← Distraction_mobile −0.271 -6.346 ⁎⁎⁎


observed that the safety risk perception due to mobile phone use whiledriving is moderate. The inclusion of the exposure parameter affirmsthe theory that if using a mobile phone while driving is perceived as adistraction, the use of the same by the drivers will be low.

In conclusion, there is a need to emphasize behavior-based controlsto reduce mobile phone use while driving. Strategies focusing on per-ceived distraction due to mobile phones are likely to mitigate thisproblem.

Conflict of interest

The questionnaire surveywas undertaken in southern state of Keralain India. The authors declare no conflict of interest in the studyundertaken.

Acknowledgments

The authors gratefully acknowledge the support of Dr. Biju AugustineP., Associate Professor, Rajiv Gandhi Institute of Technology, Kottayam foradvice and direction in carrying out the analysis and modeling. Supportextended by the staff of National Transportation Planning and ResearchCentre, Thiruvananthapuram in the collection of data through a publicsurvey is valuable. The use of Library Facility of IIT Kharagpur is acknowl-edged for the literature support. Technical editing support of Dr. ReenaMurali is also acknowledged.

References

[1] A.S. Knapper, M.P. Hagenzieker, K.A. Brookhuis, Do in-car devices affect experiencedusers' driving performance? IATSS Res. 39 (2015) 72–78.

[2] D.B. Kaber, Y. Liang, Y. Zhang, M.L. Rogers, S. Gangakhedkar, Driver performance ef-fects of simultaneous visual and cognitive distraction and adaptation behaviour,Transport. Res. F: Traffic Psychol. Behav. 15 (5) (2012) 491–501.

[3] K.L. Young, M.G. Lenné, Driver engagement in distracting activities and the strate-gies used to minimise risk, Saf. Sci. 48 (3) (2010) 326–332.

[4] WHO, Mobile Phone Use: A Growing Problem of Driver Distraction, World HealthOrganization, Geneva, Switzerland, 2011.

[5] J.K. Caird, K.A. Johnston, C.R. Willness, M. Asbridge, P. Steel, A meta-analysis of theeffects of texting on driving, Accid. Anal. Prev. 71 (2014) 311–318.

[6] K. Shinohara, T. Nakamura, S. Tatsuta, Y. Iba, Detailed analysis of distraction inducedby in-vehicle verbal interactions on visual search performance, IATSS Res. 34 (1)(2010) 42–47.

[7] C.R. Bingham, Driver distraction: a perennial but preventable public health threat toadolescents, J. Adolesc. Health 54 (2014) S3–S5.

[8] L.T. Lam, Distractions and the risk of car crash injury, J. Saf. Res. 33 (2002) 411–419.[9] R. Bridger, Introduction to Ergonomics, 3rd ed. CRC Press, Boca Raton, USA, 2008.

[10] S. Singh, Distracted Driving and Driver, Roadway, and Environmental Factors, Math-ematical Analysis Division, National Center for Statistics and Analysis, NationalHighway Traffic Safety Administration, 2010.

[11] B.G. Simons-Morton, S.G. Klauer, M.C. Ouimet, F. Guo, P.S. Albert, S.E. Lee, P.E.Johnathon, K.P. Anuj, T.A. Dingus, Naturalistic teenage driving study: findings andlessons learned, J. Saf. Res. 54 (2015) 29–44.

[12] D. Shinar, N. Tractinsky, R. Compton, Effects of practice, age and task demands on in-terference from a phone task while driving, Accid. Anal. Prev. 37 (2005) 315–326.

[13] Y. Chen, Driver personality characteristics related to self-reported accident involve-ment and mobile phone use while driving, Saf. Sci. 45 (2007) 823–831.

[14] M.A. Regan, C. Hallett, C.P. Gordon, Driver distraction and driver inattention: defini-tion, relationship and taxonomy, Accid. Anal. Prev. 43 (5) (2011) 1771–1781.

[15] S. Vardaki, G. Yannis, Investigating the self-reported behavior of drivers and their at-titudes to traffic violations, J. Saf. Res. 46 (2013) 1–11.

[16] M.B. Johnson, R.B. Voas, J.H. Lacey, A.S. McKnight, J.E. Lange, Living dangerously:driver distraction at high speed, Traffic Inj. Prev. 5 (2004) 1–7.

[17] J.M. Cooper, I. Vladisavljevic, N. Medeiros-Ward, P.T. Martin, D.L. Strayer, An investi-gation of driver distraction near the tipping point of traffic flow stability, Hum. Fac-tors 51 (2009) 261–268.


http://refhub.elsevier.com/S0386-1112(16)30036-X/rf0005







































[18] D. Stavrinos, J.L. Jones, A.A. Garner, R. Griffin, C.A. Franklin, D. Ball, S.C. Welburn, K.K.Ball, V.P. Sisiopiku, P.R. Fine, Impact of distracted driving on safety and traffic flow,Accid. Anal. Prev. 61 (2013) 63–70.

[19] J. Golias, G. Yannis, C. Antoniou, Classification of driver assistance systems accordingto their impact on road safety and traffic efficiency, Transp. Rev. 22 (2002) 179–196.

[20] P. Lenzuni, P. Capone, D. Freda, M. Del Gaudio, Is driving in a hot vehicle safe? Int. J.Hyperth. 30 (2014) 250–257.

[21] D. Lamble, M. Laakso, H. Summala, Detection thresholds in car following situationsand peripheral vision: implications for positioning of visually demanding in-car dis-plays, Ergonomics 42 (1999) 807–815.

[22] T. Dukic, L. Hanson, K. Holmqvist, C.Wartenberg, Effect of button location on driver'svisual behaviour and safety perception, Ergonomics 48 (2005) 399–410.

[23] J.H. Ryu, S. Lee, Improving In-vehicle display and control design for older drivers,Ind. Eng. Manag. Syst. 288-291 (2011).

[24] C. Collet, A. Clarion, M. Morel, A. Chapon, C. Petit, Physiological and behaviouralchanges associated to the management of secondary tasks while driving, Appl.Ergon. 40 (2009) 1040–1046.

[25] A.B. Ünal, L. Steg, K. Epstude, The influence of music on mental effort and drivingperformance, Accid. Anal. Prev. 48 (2012) 271–278.

[26] T.C. Lansdown, A.N. Stephens, Couples, contentious conversations, mobile telephoneuse and driving, Accid. Anal. Prev. 50 (2013) 416–422.

[27] K.L. Young, P.M. Salmon, M. Cornelissen, Distraction-induced driving error: an on-road examination of the errors made by distracted and undistracted drivers,Accid. Anal. Prev. 58 (2013) 218–225.

[28] A. Cuenen, E.M.M. Jongen, T. Brijs, K. Brijs, M. Lutin, K. Van Vlierden, G. Wets, Doesattention capacity moderate the effect of driver distraction in older drivers? Accid.Anal. Prev. 77 (2015) 12–20.

[29] W. Brodsky, Z. Slor, Background music as a risk factor for distraction among young-novice drivers, Accid. Anal. Prev. 59 (2013) 382–393.

[30] C.J.D. Patten, A. Kircher, J. Ostlund, L. Nilsson, Using mobile telephones: cognitiveworkload and attention resource allocation, Accid. Anal. Prev. 36 (2004) 341–350.

[31] T.L. Overton, T.E. Rives, C. Hecht, S. Shafi, R.R. Gandhi, Distracted driving: prevalence,problems, and prevention, Int. J. Inj. Control Saf. Promot. 22 (2015) 187–192.

[32] K. Young, M. Regan, M. Hammer, Driver distraction—a review of the literature, Re-port No. 206, Monash University 2003, pp. 1–52.

[33] K.L. Young, P.M. Salmon, Examining the relationship between driver distraction anddriving errors: a discussion of theory, studies and methods, Saf. Sci. 50 (2) (2012)165–174.


[34] S.J. Kass, K.S. Cole, C.J. Stanny, Effects of distraction and experience on situationawareness and simulated driving, Transport. Res. F: Traffic Psychol. Behav. 10(2007) 321–329.

[35] J.L. Gifford, ICT and road transportation safety in the United States: a case ofAmerican exceptionalism, IATSS Res. 34 (1) (2010) 1–8.

[36] K.L. Young, P.M. Salmon, M. Cornelissen, Missing links? The effects of distraction ondriver situation awareness, Saf. Sci. 56 (2013) 36–43.

[37] S.A. Birrell, M. Fowkes, Glance behaviours when using an in-vehicle smart drivingaid: a real-world, on-road driving study, Transport. Res. F: Traffic Psychol. Behav.22 (2014) 113–125.

[38] P. Atchley, S. Atwood, A. Boulton, The choice to text and drive in younger drivers:behavior may shape attitude, Accid. Anal. Prev. 43 (2011) 134–142.

[39] A. Backer-Grøndahl, F. Sagberg, Driving and telephoning: relative accident riskwhen using hand-held and hands-free mobile phones, Saf. Sci. 49 (2011) 324–330.

[40] R. Srinath, R. Rajesh, R. Sasikumar, B. Subin, In-vehicle distraction among fourwheeler drivers in Kerala, HWWE2015 International Conference on Ergonomicsand Human Factors, IIT Bombay, India, Dec 6–9 2015.

[41] T. Horberry, J. Anderson, M.A. Regan, T.J. Triggs, J. Brown, Driver distraction: the ef-fects of concurrent in-vehicle tasks, road environment complexity and age on driv-ing performance, Accid. Anal. Prev. 38 (2006) 185–191.

[42] D. Romer, Y.C. Lee, C.C. McDonald, F.K. Winston, Adolescence, attention allocation,and driving safety, J. Adolesc. Health 54 (5) (2014) S6–S15.

[43] G.F. Briggs, G.J. Hole, M.F. Land, Emotionally involving telephone conversations leadto driver error and visual tunnelling, Transport. Res. F: Traffic Psychol. Behav. 14 (4)(2011) 313–323.

[44] E.J. Westlake, L.N. Boyle, Perceptions of driver distraction among teenage drivers,Transport. Res. F: Traffic Psychol. Behav. 15 (6) (2012) 644–653.

[45] E. Nelson, P. Atchley, T.D. Little, The effects of perception of risk and importance ofanswering and initiating a cellular phone call while driving, Accid. Anal. Prev. 41(2009) 438–444.

[46] S. Hosking, K. Young,M. Regan, The Effects of Text Messaging on Young Novice Driv-er Performance, Monash University, 2006 1–40.

[47] J.K. Caird, C.R. Willness, P. Steel, C. Scialfa, A meta-analysis of the effects of cellphones on driver performance, Accid. Anal. Prev. 40 (2008) 1282–1293.











































































Documents

Modeling safety risk perception due to mobile phone ...Modeling safety risk perception due to mobile phone distraction among four wheeler drivers Raghunathan Rajesha,⁎, R. Srinatha,R.Sasikumara,B.Subinb