research on brt driver jakarta fatigue evaluationieomsociety.org/ieom_2016/pdfs/555.pdf · research...

Proceedings of the 2016 International Conference on Industrial Engineering and Operations Management Kuala Lumpur, Malaysia, March 8-10, 2016

Research on BRT Driver Jakarta Fatigue Evaluation Rida Zuraida

Industrial Engineering Department, Bina Nusantara University, Indonesia [email protected]

Ketut Gita Ayu, Caroline Nike Septivani Industrial Engineering Department, Bina Nusantara University, Jakarta-Indonesia

Hardianto Iridiastadi Faculty of Industrial Technology, Institut Teknologi Bandung, Indonesia

Bandung, Indonesia

Abstract— Driving in Jakarta as capital of Indonesia can be very exhausting due to high traffic density. The burden of public vehicle drivers such bus driver is heavier, since they are dealing with the traffic during their work. Nowday, Bus Rapid Transport (BRT) becomes one of highly demanded transportation system for Jakartans and worker from urban area. In this background, research on fatigue from driving addressing BRT’s driver becomes a critical issue in ensuring safe operation. In this study a workload evaluation, sleepiness, motivation level, self-rated fatigue and salivary amylase test was established to evaluate fatigue level of BRT driver from 2 different shifts (45 participants). Data collected directly during BRT’s driver was on duty, before and after their services. Based on anova analysis, it reveals that different shift could affect fatigue levels (p<0,05) based on SOFI result, although workload each shift is similar. The study also show that 27% more driver are in stress condition before starting their task in morning-shift. Suprisingly, driver’s motivation level among shift is not differ. As a conclusion, early shift had a larger influence on stress and sleepiness level on BRT driver, a rotation schedule may help to overcome the problem.

Keywords— Driver fatigue, time-of-day, shift, stress

I. INTRODUCTION

Bus Rapid Transit (BRT)- Jakarta known as TransJakarta in Indonesia, has been available since 2004 which aims to improve the quality of transport services for Jakartans. There are 12 route services provided, and each route has different service route length, dedicated road space availaible to be passed through by BRT buses, different traffic and passanger density. Number of passengers it self keeps increasing for each route specifically when route pass through the Jakarta’s main roads. So this day, BRT become one of important affordable public transportation for Jakartans and other city near to Jakarta[1].

Working as a public transport driver such as BRT driver is very challenging sometimes. The driver has to deal with unbelievable traffic in a certain time, and at the same time they required to maintain its safety during the trip. An increasing traffic flow from urban areas in weekdays is one of contributor to the traffic density, in additon of traffic flow from the city. Higher traffic density could increase driver workload during weekdays. This study is focusing on the BRT driver condition, and how a workload affects to their fatigue level. Understanding about the BRT driver work condition is crucial to create a conducive working system for the drivers.

As an illustration, BRT drivers work schedule is available in two shifts. Drivers who work in morning shift has to wake up very early, while afternoon-shift driver has finished their duty after 9 pm and back home late. During their duty, morning-shift driver has to deal with a heavy traffic at 7-9 am and around lunch time, while afternoon shift usually has to face it after office hour at 4.30-7.00 pm and sometime until 9 pm. In addition, BRT driver have number of cycle route to fulfill, and when heavy traffic density occur, a driver has difficulty to achieve the target. This condition can induced the increasing of driver’s workload and can lead them to stressful situations.

It should be noted, drivers get additional workload when they distract or have secondary task such as phoning, talking to passanger, listening to the music, etc. The brain translates driving as main task and the secondary tasks or distractions as a stimulus that requires a specific response. Receiving stimulus and giving a response will use resources such as visual, auditory, cognitive and psychomotor resources (VACP).

1929© IEOM Society International


VACP concept [2] divides information processing in human into four dimensions of the resource. This concept is later used by Bierbaum et al (1989) [3] to approximate the weight of a task based on the utilization of resources during task execution. Driver’s workload can be asses from visual, auditory, cognitive and psychomotor resource utilization [4].

Driving job is categorize as multitasking job, and this kind of job is need extra resources utilization [5]. However, there is a limit of the total amount of resources and the amount that can be allocated to specific activities. This resource concept is closely related to workload, performance level, work attention, and safety. Work performance tends to decline when some resources are used simultaneously; this also influences the person’s ability to process information. Excess work load triggers an increasing work risk, induced slower performance [5], and affected worker fatigue level [6].

Model fatigue and safety relation mention task related factors as contributor to occupational fatigue [6]. Time-of-day and time- awake are also mention as fatigue’s influence factors. Sufficient time to recover (short break or sleep), will help overcome the problem, and conversely, rest deficiency inhibits the recovery and lead to impaired performance capabilities. Working condition faced by BRT drivers could lead to this rest deficiency especially for morning-shift driver who work in very early shift [7].

Morning shift pushes the driver to work while human biological clock is in sleeping period, and thus increase the urge to sleep [8] [9]. From fatigue point of view, sleepiness is one of fatigue indicators and it's hard to be apart from the human circadian rhythm [8] [9] [10] [11] [12] [13]. Working hours not only can disrupt biological clock, but also influence work stress [14]. And work stress analysis suggested as one of indicator to investigate to complete fatigue evaluation [15][16][17]18].

The combination of an objective and a subjective measurement is also suggested in fatigue evaluation, and an practical objective measurement is avalaible with technologies nowadays. Stress assessment, for example, can be evaluated through salivary amylase use a techloogy called Nipro Cocorometer [19]. Salivary amylase level on kU/L is used to determine when someone in stress mode or not [19] [20].

This paper discusses of BRT driver fatigue evaluation. Current research on BRT drivers working condition measurement such as workload, stress level or work motivation aspect is very limited. Finding on this research can give new perspective on driving job fatigue. In this study BRT driver condition was analysis by comparing subjective fatigue level, workload, stress and motivation level between morning-shift and afternoon-shift driver.

II. METHOD

A. Participants A total of forty five BRT drivers involved in the study, 37 male (age 26-51) and 8 female (age 37-44), 23 worked in the 1st

shift and the rest is in the 2nd shift. They joined BRT at least a year ago up to 4 year. All participant have an experience as public transport driver at least 2 years before they joined BRT.

B. Data collection This research is a naturalistic study, and data collected during BRT drivers on duty for both shift. Collection data started

before drivers starting their job, it took place in the bus garage before bus head to first stop. Before data collected, driver was brief about the collection data process, and they signed inform consent. Salivary amylase each drivers who participate in the study took by researcher, and tested using cocorometer nipro. The result recorded along with driver’s personal data. Sleepiness level also asked, based on Karolinska Sleepiness Scale (KSS) that has been used in literatur [7][17].

Researcher observed and recorded all acitivities of driver since bus was started until the driver finishes their shift. After they finish their services, salivary amylase test carried out, and researcher help them to fill in motivation questionnaire consist of 24 question. After realibility and validity test, questionnaire result for each driver categorized into high, good, sufficient, low and very low as indicating motivation level. Sleepiness level also asked , and recorded.

In the morning shift, data collection was conducted from 4.00 am and done at 01.30 pm. The afternoon shift started at 11.00 am and complete at 8.00 pm.

Observation on drivers’ work condition was conducted via direct observation on the drivers’ activities during duties and was noted manually based on predetermined activity list. The list was based on the concept developed by Yee et al (2006) [4]. Then, the proportion of resource utilization, i.e. visual, auditory, cognitive and psychomotor resources was determined. After



data was collected, frequency of each activity was multiplied with the proportion of resource utilization to obtain information of which resource is the most utilized and is the biggest burden for the driver.

Level fatigue of driver was also measured using a Swedish Occupational Fatigue Index (SOFI) [21]. SOFI is one of fatigue subjective measurement tools. SOFI has been used to assess perceived fatigue related to work [22] [23] and translated into several languages with good reliability and validity [24] [25]. SOFI questionnaire consist of 25 item tests (questions) which grouped in 5 dimensions as follows: (1) Physical Exertion/PE, (2) Physical Discomfort/PD, (3) Lack of Energy/LE (4) Lack of Motivation /LM, (5) Sleepiness/SL; as SOFI original proposed.

Salivary amylase test was conducted to determine whether the driver was in a good condition (non-stress) before and after duty [18][19], based on the saliva amylase level. Saliva is took using disposable tip and it test using Nipro Cocorometer. This research used > 60kU/L as the limit to determine driver on stress condition [19]. Assessment was conducted to find out whether there are fatigue differences experienced by driver shift 1 and shift 2 .

C. Analysis method Fatigue condition data of BRT’s driver was analysed using two-way ANOVA to see whether the differences is exist

between morning and afternoon task. On analysis of fatigue level that indicated by workload, two-way Anova was conducted to see whether workload of each resource is different. This analysis also was conducted to see whether workload morning-shift and afternoon-shift are different.

Stress level data is analysis using descriptive analysis to find out how many morning-shift driver in good condition compare to afternoon-shift driver, indicate by salivary amylase value < 60 kU/l. The same analysis is conducted on motivation data among driver. In addition, one-way anova is also performed to find out whether the differences was exist.

III. RESULT

A. Workload and fatigue level Driver‘s workload assessed through estimation of resource utilization, it assess based on examination of activities carried

out by driver during their duty. Each activity has different resources weight. For instance when a driver has to slow down the car, usage of psychomotor resources is higher than visual, cognitive, and auditory resources. Examination of activities is refers to certain activities list and frequency of each activity is recorded. Total frequency of activities multiplied by weight of each resources usage to determine estimation of resource utilization.

Resource utilization for each shift analysis using two-way Anova to see whether there are differences between the shift. Analysis of the workload revealed a significant main effect of each resource (F3, 8) =8, 887, p value=0, 0063, and but not for the interaction and differences for each shift (p>0.05). This indicates that the workload of each resources is different significant, and as data showed, visual workload is the highest compare to other. Further more, analysis result revealed that workload for each resources in the morning-shift driver is similar with afternoon-shift driver.

Fig 1. Workload estimation based on resources utilization shift 1 and shift 2

The workload perceived differently by driver based on Swedish Occupational Fatigue Index (SOFI) result. Although driver

perceived their job is not a fatigued, but increasing level of fatigue consisten for each 5 dimension after 8 hour services (see Fig. 2). Anova analysis showed significant difference fatigue level before and after driver services for both shift. This result can be conclude that although estimation of workload between shift is similar, starting working early is more fatigued perceived by drivers compare to working in the afternoon shift.



Fig. 2 Fatigue level before and after duty shift 1 and 2

Sleepiness level assessment (KSS) has give similar result with SL in SOFI. KSS after duty is higher compare to before duty, and level of sleepiness on shift 1 is higher than shift 2.

B. Stress level Salivary amylase level test using nipro cocorometer is conduct to determine stress level of driver before and after their

duty. Measurement apply for both shift. Salivary amylase level > 60kU/l is limit to determine stress level. There are 87% of shift 1 driver who categorized post-stress, contrary only 60% shift 2 driver are in this category. We can conclude that working in early shift tend to create higher stress compare to work in normal time.

The result of salivary amylase test after duty, showed that percentage of post-stress driver does not differ much for each shift (54% for shift 1 and 60% for shift 2). But we can say that the percentage before duty is higher compare to data after duty. This indicate that driver feels releave after their finish their job. The phenomen doesn’t result significant differences betwen shift 1 and shift 2. Anova analysis show that stress level among morning-shift driver and afternoon-shift driver is similar statistically.

C. Motivation level Measurement of work motivation was conducted using 24-question questionnaire. Validity test shows that question 1,3,7

and 19 must be omitted and the level of instrument reliability of the 20 item test is 0.667 which means that the reliability of this instrument is accepted or > 0,6. Work motivation level determine based on total value answer of each respondent, and categorized into high, good, sufficient, low, and very low. Result of work motivation morning-shift and afternoon shift can be seen below :

Table 1 Work Motivation Level of Driver

Work Motivation Level Percentage

High 87%

Good 21%

Sufficient 9%

Low 0%

Very Low 0% A work motivation questionnaire result between drivers in shift 1 and 2 is analyzed to confirm the presence of differences. Using one-way anova, test results of p value was >0.05 thus can be concluded that there is no difference in the work motivation level between drivers in shift 1 and 2. It can be concluded that work condition and workload do not influence the drivers’ work motivation. Other compounding factors such as work environment should be considered for further investigation.

0,240,32

0,190,27

0,51

1,07 1,010,86

0,56

0,89

0,07 0,04 0,03 0,100,25

0,94

0,70

0,430,32

1,39

PE PD LE LM SL

Fatigue Level

Before1 After1 Before2 After2



IV. DISCUSSION The aim of this study was to investigate a workload, sleepiness and motivation level, self-rated fatigue and stress level to

evaluate fatigue level BRT’s driver from 3 services route and 2 different shifts. This study demonstrated that fatigue level of driver in both shifts increasing after 8 hours duty, although the increasing perceived very small. Based on workload analysis, resource utilization of visual is the highest among other 3 resources (psychomotor, cognitive and auditory) for both shifts. In driving job capability to give respond after visual stimulus given can give significant effect to safety.

Increasing fatigue level after the duty is consistent with an increasing of sleep and stress level after duty. This condition is experienced by 80% drivers after their services for both shifts. The literature also suggests that sleepiness and stress level measurement is largely used in fatigue assessment [6] [10].

Although salivary amylase showed that drivers who work early shift have higher stress levels, the result of motivation level for both shifts is not statistically different. It seems that workload, stress and sleepiness level doesn’t affect the driver’s motivation level.

ACKNOWLEDGMENT This research was supported funding by grants of Higher Education Indonesia (2015) and equipment by Human-Integrated

System (HIS) Laboratory of Bina Nusantara University, Indonesia and Laboratory for Work System Engineering & Ergonomics, Faculty of Industrial Technology, Institut Teknologi Bandung, Indonesia, and Institute for Transportation &

Development Policy, Indonesia

REFERENCES 1. Agus. “In year Sixth become more popular” Bus Transjakarta Semakin Diminati di Usia ke-6”. Retrieved April 23, 2013, from

Jakarta.go.id: www.jakarta.go.id/v2/news/2010/01/Bus-Transjakarta-Semakin-Diminati- di-Usia-ke-6#.UXc1Ia7pbRQ 2. T.B Aldrich, S.M. Szabo, C.R. Bierbaum, “The development and application of model to predict operator workload during

system design. Retrieved 5 20, 2013, from http://sunburst.usd.edu: http://sunburst.usd.edu/~schieber/psyc792/workload/Aldrich1988.pdf” (1988).

3. C.Bierbaum, S. Szabo, and T.B.Aldirch, “Task Analysis of the UH-60 Mission and Decision Rules for Developing a UH-60 Workload Prediction Model”, US Army R&D Activity, Fort Rucker, AL. 1989.

4. S. Yee, , L. Nguyen, P.Green, J. Oberholtzer, B.Miller, “Visual, Auditory, Cognitive, and Psychomotor Demands of Real In-Vehicle Tasks”, Technical Report, Univeristy of Michigan Transportation Research Institute, USA. 2006

5. C. D. Wickens, “Multipe Resources and performance prediction”, Ergonomic Science, vol 3 no. 2, 159-177, 2002 6. A.Williamson, D.A. Lombardi, S.Folkard, J.Stuts, T.K.Courtney, J.L Connor, “The link between fatigue and safety”, Accident

Analysis and Prevention 43, 498-515, 2011. 7. M. Ingre, G.Keclund, T.Åkerstedt, L. Keclund, “Variation in Sleepiness during Early Morning Shifts ; A Mixed Model Approach to an

Experimental Field Study of Train Drivers”, Chronobiology International, Vol 21. No. 6, pp.973-990. 2004. 8. M.W.Johns , “What is Esxcessive Daytime Sleepiness”, Sleep Deprivation : Causes, Effects and Treatment, Nova Science Publisher,

Inc. 2009. 9. P. Philip, P.Sagaspe, N. Moore, J.Taillard,A. Charles, C.Guilleminault, B.Bioulac, “Fatigue, sleep restriction and driving

performance”, Accident Analysis and Prevention 37, pp.473-478, 2005. 10. T.Rupp, Concepts of Fatigue, Sleepiness, and Alertness. Encyclopedia of Sleep, pp.24-26, 2013. 11. A.Shahid, J. Shen, C.M. Shapiro, “Measurement of sleepiness and Fatigue”, Journal of Psychosomatic Research 69, p.81-89, 2010. 12. J.Shen , J.Barbera, C.M.Shapiro, “Distinguish sleepiness and fatigue : focus on definition and measurement”, Sleep Medicine Reviews

10, pp.63-76, 2006. 13. R.D.Kovalevent, A.Hinz, E.Brahler, B.F.Klapp, “Determinant of Fatigue and Stress”, Biomedical Research Note, 4, 238, 2010. 14. J.Hobson, J.R Beach, “An investigation of the relationship between psychological health and workload among managers”, Ocupational

Med. Vol 50, no. 7, pp.518-522, 2000. 15. L.A. Reyner, S.J. Wells, V.Mortlock, J.A.Horne, “Post-lunch’ sleepiness during prolonged,monotonous driving-effects of meal size”,

Physiology & Behavior, 105, p.1088-1091, 2012. 16. C.Minowa , K.Koitabashi, “Salivary alpha-amylase activity- an Indicator of relaxation response in perioperative patients”, Open

Journal of Nursing 2, pp.208-214, 2012. 17. J.Connor , G.Whitlock, R.Norton , R. Jackson ,”Role of driver sleepiness in car crashes : a systematic review of epidemiological

studies”, Accident Analysis and Prevention 33, pp.31-41, 2001. 18. K.Kaida, T.Åkerstedt, G.Kecklund, J.P. Nilsson, J.Axesson, “Use of subjective and Physiological Indicators of Sleepiness to Predict

Performance during a Vigilance Task”, Industrial Health 45, 520-526, 2007. 19. M.Yamaguchi , T.Kanmori , M.Kanemaru, N.Takai, Y.Mizuno, H. Yoshida, “Performance evaluation of salivary amylase activity

monitor”, Biosensors and Bioelectronics 20, 491-497 20. Sakabibra, T., Wang, Z., and Kasai Y., (2012), Does going to an amusement park alleviate low back pain? A preliminary study, Journal

Pain Research 5, 409-413, 2004. 21. D.J. Michael,S. Duagherty, A.Santos, B.C.Ruby, J.E.Kalns, “ Fatigue biomarker index : An objective salivery measure of fatigue level,

Accident Analysis and Prevention 45S, 68-73, 2012.



22. E. Ahsberg, Gamberale, F., Gustafsson K., “Perceived fatigue after mental work : an experimental evaluation of a fatigue inventory”,Ergonomics, vol 43, no. 2, 252-268, 2000

23. L.M.B Steege, M.A. Nussabaum, “Dimension of fatigue as predictors of Performance: A structural Equation Modelling Approachamong Registered Nurses, IIE Transaction on Occupational Ergonomics and Human Factors, 16-30, 2013..

24. S.E. Mathiassen, D.M. Hialman, E. Lyskov, S.Hygee, “Can Cognitive Activities during Breaks in Repetitive Manual Work AccelerateRecovery from Fatigue? A Controlled Experiment”, in Plos One, November 6, DOI: 10.1371/journal.pone.0112090, 2014.

25. A.W.S. Leung, C.C.H. Chan, J.He, “Structural stability and reliability of the Swedish occupational fatiue inventory among ChineseVDT workers”, in Applied Ergonomics, 35, 233-241, 2004.

26. L.G. Gutiérrez, B.M. Jiménez, E.G.Hernández, A.L.López,”Spanish version of the Swedish Occupational Fatigue Inventory (SOFI) ;Factorial replication, reliability and validity”, in International Journal of Industrial Ergonomics 35, 737-746, 2005

BIOGRAPHY Rida Zuraida is a Faculty member of Industrial Engineering Department in Bina Nusantara University, and also PhD student at Faculty of Industrial Technology at Institut Teknologi Bandung, Indonesia. She received her Master of Industrial Engineering from Institute Technology Bandung in 2004. Her research interests are in the area Occupational Fatigue, Quality, Safety, and Health Environment. She can be contacted through email at [email protected].

Ketut Gita Ayu, is a Faculty Member of Industrial Engineering Department in Bina Nusantara University, She finished her Master from Georgia Tech, USA. She is a member of IIE. She can be contacted through email at [email protected]

Caroline Nike Septivani, is a Faculty Member of Industrial Engineering Department in Bina Nusantara University, She finished her Master from Universitas Pelita Harapan, Indonesia. She is a member of IIE. Her research interests are in the area Production system, Quality, Safety, and Health Environment. She can be contacted through email at [email protected].

Hardianto Iridiastadi, is an Associate Professor and Head of the Laboratory for Work System Design & Ergonomics at Institut Teknologi Bandung, Indonesia. He earned Masters in Industrial Engineering from Louisiana State University, USA, and Ph.D. from Virginia Polytechnic Institute and Technology, USA. His research interests include occupational biomechanics, occupational physiology, and transportation safety. He is a member of IIE and IEA.he can reach through email at [email protected].


research on brt driver jakarta fatigue evaluationieomsociety.org/ieom_2016/pdfs/555.pdf · research...

Documents