EVERGREEN

Joint Journal of Novel Carbon Resource Sciences and Green Asia Strategy

ISSN:2189-0420 (Print until Mar 2020)
ISSN:2432-5953 (Online)

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Mental Workload in Truck Driving: A NASA-TLX and HRV-Based Comparison Across Day-Night and Rural-Urban Conditions

Hastiya Annisa Fitri1, Ludfi Pratiwi Bowo1,*, Siti Hidayanti Mutiara Kurnia1, Indra Kurniawan1, Mutharuddin1, Subaryata1, Ridwan Aji Budi Prasetyo2, Ari Widyanti3
1Research Center for Transportation Technology, National Research and Innovation Agency, Indonesia
2Psychology Department, University of Brawijaya, Indonesia
3Industrial Engineering Department, Bandung Institute of Technology, Indonesia
*Author to whom correspondence should be addressed:
E-mail: ludf001@brin.go.id (LPB)
Evergreen, Vol. 12, Iss. 04, pp. 2086–2099, 2025
DOI: 10.5109/7402639
Creative Commons Attribution 4.0 International Creative Commons Attribution 4.0 International
Received: May 28, 2025 | Revised: September 26, 2025 | Accepted: December 15, 2025 | Published: December 2025
Abstract
Truck-involved accidents often result in severe consequences due to the vehicle’s size and mass, making driver fatigue and cognitive overload critical risk factors. Mental workload reflects real-time cognitive demands during driving, where excessive levels accelerate fatigue, impair response time, and increase accident risk. This research investigates the effects of four driving scenarios (rural daytime, rural nighttime, urban daytime, urban nighttime) on the mental workload of 50 professional male truck drivers, using NASA Task Load Index (NASA-TLX) assessment and Heart Rate Variability (HRV) metrics (aLF, aHF, ln-aLF, ln-aHF, nLF, nHF, and HR in bpm). Performance was measured through response time, hit rate, and error rate. The NASA-TLX results indicate a proportional increase in mental workload from rural daytime to urban nighttime. Mental Demand (p = 0.025) and Temporal Demand (p = 0.047) are significantly higher in urban nighttime compared to rural daytime, while other dimensions, though not significant, show a general pattern of increasing mean scores, with Performance showing the opposite pattern. Heart rate (bpm) significantly increases, and nLF power shows a significant reduction during urban nighttime scenario compared to rural daytime (p < 0.03), indicating increased mental workload. Two-way ANOVA further revealed that road condition (urban vs. rural) significantly affected HRV indices (nLF, nHF, and HR), while driving time (nighttime vs. daytime) significantly elevated Mental Demand and Frustration, highlighting the distinct contribution of environmental and temporal factors to driver workload. Significantly, response times were slowest in rural nighttime scenarios, hit rates were higher at night than during the day, and error rate peaked in urban nighttime driving, suggesting that reduced visibility and traffic density contribute to increased mental workload. These findings provide a more structured and advanced methods for measuring truck drivers' mental workload, benefiting company management in optimizing driver performance and safety.
Keywords
driver performance; Heart Rate Variability; mental workload; NASA-TLX; truck drivers
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References
  1. 1) Dwi Phalita Upahita, R. Pujiwat, Maharani Almira Salsabilla, A. Roschyntawati, M. Rosyidi, Djoko Prijo Utomo, Sucipto, Siti Hidayanti Mutiara Kurnia, D. Arianto, and H. Putra, "Implementation of risk factor on optimisation model to minimise transportation cost of fatty acid methyl ester (fame) as biodiesel raw material," Evergreen, 11 (3) 2650-2658 (2024) doi:10.5109/7236904
  2. 2) K. Lookman, N. Pujawan, and R. Nadlifatin, "Improving Innovative Capabilities of Trucking Company: Action Research Approach," in: Proceedings of the International Conference on Industrial Engineering and Operations Management, IEOM Society International, Manila, Philippines, 2023 doi:10.46254/AN13.20230629
  3. 3) Y. Zifei, R. Minjares, M. Kusumaningkatma (ICCT), F. Sehlleier, L. Herliana, Y. Priatama (GIZ), A. Yani, A. Wahyudi, E. Simbolon, D. Indah, and D. Ayu (Directorate of Road Transport Ministry of Transportation Republic of Indonesia), "Mitigation Action Outline on Truck Fleet Modernization in Indonesia," Federal Ministry for the Environment, Nature Cons and Nuclear Safety, Germany, Jakarta, 2021
  4. 4) S.I. (BPS), "Number of motor vehicles by type, 2017-2022," n.d. https://www.bps.go.id/indicator/17/1003/1/number-of-motor-vehicles-by-type.html
  5. 5) M.M. Ahmed, R. Franke, K. Ksaibati, and D.S. Shinstine, "Effects of truck traffic on crash injury severity on rural highways in wyoming using bayesian binary logit models," Accident Analysis & Prevention, 117 106-113 (2018) doi:10.1016/j.aap.2018.04.011
  6. 6) M. Mutharuddin, M. Rosyidi, D.W. Karmiadji, H.A. Fitri, N. Irawati, D.H. Waskito, T.S. Mardiana, S. Subaryata, and S. Nugroho, "The road safety: utilising machine learning approach for predicting fatality in toll road accidents," AE, 7 (2) 236-251 (2024) doi:10.31603/ae.11082
  7. 7) The Corps of the Indonesian National Police. Korlantas Polri, "Traffic Accident Statistics Report," 2021
  8. 8) M. and T. European Commision, "Road Traffic Fatalities in the EU in 2022," 2022
  9. 9) Ludfi Pratiwi Bowo, Ahmad Muhtadi, Feronika Sekar Puriningsih, Sinung Nugroho, Hastiya Annisa Fitri, Siti Hidayanti Mutiara Kurnia, and Apid Rustandi, "Understanding the role of conspicuity and behavior in motorcycle safety," Evergreen, 12 (1) 191-216 (2025) doi:10.5109/7342449
  10. 10) The Corps of the Indonesian National Police. Korlantas Polri, "Traffic Accident Statistics Report," 2025
  11. 11) The Ministry of Transportation of the Republic of Indonesia. Kemenhub RI, "Road Traffic Accident Data Report," 2025
  12. 12) Lalit N. Patil and Hrishikesh P. Khairnar, "Investigation of human safety based on pedestrian perceptions associated to silent nature of electric vehicle," Evergreen, 8 (2) 280-289 (2021) doi:10.5109/4480704
  13. 13) World Health Organization, "Global status report on road safety 2023," (2023). https://www.who.int/publications/i/item/9789240086517
  14. 14) P. Panwar, P. Roshan, R. Singh, M. Rai, Asha Rani Mishra, and Sansar Singh Chauhan, "DDNet- a deep learning approach to detect driver distraction and drowsiness," Evergreen, 9 (3) 881-892 (2022) doi:10.5109/4843120
  15. 15) Amol Shinde, Raju Kumar Swami, and Sameer Nanivadekar, "A review of the autonomous electric vehicles: challenges and future scope," Evergreen, 12 (1) 227-249 (2025) doi:10.5109/7342451
  16. 16) J. Monios, and R. Bergqvist, "The transport geography of electric and autonomous vehicles in road freight networks," Journal of Transport Geography, 80 102500 (2019) doi:10.1016/j.jtrangeo.2019.102500
  17. 17) I.O. Olayode, B. Du, A. Severino, T. Campisi, and F.J. Alex, "Systematic literature review on the applications, impacts, and public perceptions of autonomous vehicles in road transportation system," Journal of Traffic and Transportation Engineering (English Edition), 10 (6) 1037-1060 (2023) doi:10.1016/j.jtte.2023.07.006
  18. 18) 6Wresearch, "Indonesia Semi-Autonomous & Autonomous Truck Market (2025-2030)," 2025. https://www.6wresearch.com/industry-report/indonesia-semi-autonomous-autonomous-truck-market
  19. 19) O. Yerembayev, G. Espayeva, A. Kiyalbayev, S. Kiyalbay, and A. Sagybekova, "Snow pressure zoning for road safety: snow mechanical characteristics and climatic factors," Evergreen, 11 (3) 1602-1612 (2024) doi:10.5109/7236815
  20. 20) C. Sitinjak, Z. Tahir, M.E. Toriman, N. Lyndon, V. Simic, C. Musselwhite, W.F. Simanullang, and F.M. Hamzah, "Assessing public acceptance of autonomous vehicles for smart and sustainable public transportation in urban areas: a case study of jakarta, indonesia," Sustainability, 15 (9) 7445 (2023) doi:10.3390/su15097445
  21. 21) K. Lookman, N. Pujawan, and R. Nadlifatin, "Innovative capabilities and competitive advantage in the era of industry 4.0: a study of trucking industry," Research in Transportation Business & Management, 47 100947 (2023) doi:10.1016/j.rtbm.2023.100947
  22. 22) N.I. Abd Rahman, S.Z. Md Dawal, and N. Yusoff, "Driving mental workload and performance of ageing drivers," Transportation Research Part F: Traffic Psychology and Behaviour, 69 265-285 (2020) doi:10.1016/j.trf.2020.01.019
  23. 23) D. Gopher, and E. Donchin, "Workload: An examination of the concept.," in: Handbook of Perception and Human Performance, Vol. 2: Cognitive Processes and Performance., John Wiley & Sons, Oxford, England, 1986: pp. 1-49
  24. 24) A. Sudiarno, A.M.D. Ma’arij, I.P. Tama, A. Larasati, and D. Hardiningtyas, "Analyzing cognitive load measurements of the truck drivers to determine transportation routes and improve safety driving: a review study," Automotive Experiences, 6 (1) 149-161 (2023) doi:10.31603/ae.8301
  25. 25) X. Ren, E. Pritchard, C. van Vreden, S. Newnam, R. Iles, and T. Xia, "Factors associated with fatigued driving among australian truck drivers: a cross-sectional study," International Journal of Environmental Research and Public Health, 20 (3) 2732 (2023) doi:10.3390/ijerph20032732
  26. 26) D.H. Waskito, L.P. Bowo, S. Hidayanti, M. Kurnia, I. Kurniawan, and S. Nugroho, "Analysing the impact of human error on the severity of truck accidents through hfacs and bayesian network models," Safety, 10 (8) (2024). safety10010008 doi:10.3390/
  27. 27) Y. Yuan, M. Yang, Y. Guo, S. Rasouli, Z. Gan, and Y. Ren, "Risk factors associated with truck-involved fatal crash severity: analyzing their impact for different groups of truck drivers," Journal of Safety Research, 76 154-165 (2021) doi:10.1016/j.jsr.2020.12.012
  28. 28) P. Piranveyseh, R. Kazemi, A. Soltanzadeh, and A. Smith, "A field study of mental workload: conventional bus drivers versus bus rapid transit drivers," Ergonomics, 65 (6) 804-814 (2022) doi:10.1080/00140139.2021.1992021
  29. 29) F. Sekkay, D. Imbeau, P.-A. Dubé, Y. Chinniah, N. de Marcellis-Warin, N. Beauregard, and M. Trépanier, "Assessment of physical work demand of short distance industrial gas delivery truck drivers," Applied Ergonomics, 89 103222 (2020) doi:10.1016/j.apergo.2020.103222
  30. 30) F. Sekkay, D. Imbeau, P.-A. Dubé, Y. Chinniah, N. de Marcellis-Warin, N. Beauregard, and M. Trépanier, "Assessment of physical work demands of long-distance industrial gas delivery truck drivers," Applied Ergonomics, 90 103224 (2021) doi:10.1016/j.apergo.2020.103224
  31. 31) P. D’Addario, and B. Donmez, "The effect of cognitive distraction on perception-response time to unexpected abrupt and gradually onset roadway hazards," Accident Analysis & Prevention, 127 177-185 (2019) doi:10.1016/j.aap.2019.03.003
  32. 32) D. Kaber, S. Jin, M. Zahabi, and C. Pankok, "The effect of driver cognitive abilities and distractions on situation awareness and performance under hazard conditions," Transportation Research Part F: Traffic Psychology and Behaviour, 42 177-194 (2016) doi:10.1016/j.trf.2016.07.014
  33. 33) D. Ruscio, "Response Time to Hazard: The Role of Attention, Decision Making and Emotions on Expectations In Real-Life and Virtual Driving," 2014
  34. 34) J. Huang, Q. Zhang, T. Zhang, T. Wang, and D. Tao, "Assessment of drivers’ mental workload by multimodal measures during auditory-based dual-task driving scenarios," Sensors, 24 (3) (2024) doi:10.3390/s24031041
  35. 35) Y.-C. Liu, and T.-J. Wu, "Fatigued driver’s driving behavior and cognitive task performance: effects of road environments and road environment changes," Safety Science, 47 (8) 1083-1089 (2009) doi:10.1016/j.ssci.2008.11.009
  36. 36) D.M. Cerwick, K. Gkritza, and M.S.B. Shaheed, "A comparison of the mixed logit and latent class methods for crash severity analysis," Analytical Methods in Accident Research, 3 (4) 11-27 (2014) doi:10.1016/j.amar.2014.09.002
  37. 37) C. Ahlström, A. Anund, C. Fors, and T. Åkerstedt, "The effect of daylight versus darkness on driver sleepiness: a driving simulator study," Journal of Sleep Research, 27 (3) (2018) doi:10.1111/jsr.12642
  38. 38) J. Paxion, E. Galy, and C. Berthelon, "Mental workload and driving," Frontiers in Psychology, 5 (2014) doi:10.3389/fpsyg.2014.01344
  39. 39) A. Jhingran, D. Mathur, and C. Kumar, "Key challenges of sustainability index development for urban transport system of jaipur city," Evergreen, 10 (4) 2498-2505 (2023) doi:10.5109/7162013
  40. 40) C. Ahlström, A. Anund, C. Fors, and T. Åkerstedt, "Effects of the road environment on the development of driver sleepiness in young male drivers," Accident Analysis & Prevention, 112 127-134 (2018) doi:10.1016/j.aap.2018.01.012
  41. 41) J. Ma, J. Gu, H. Jia, Z. Yao, and R. Chang, "The relationship between drivers’ cognitive fatigue and speed variability during monotonous daytime driving," Frontiers in Psychology, 9 (2018) doi:10.3389/fpsyg.2018.00459
  42. 42) A. Balaji, U. Tripathi, V. Chamola, A. Benslimane, and M. Guizani, "Toward safer vehicular transit: implementing deep learning on single channel eeg systems for microsleep detection," IEEE Transactions on Intelligent Transportation Systems, 24 (1) 1052-1061 (2023) doi:10.1109/TITS.2021.3125126
  43. 43) B. Mehler, B. Reimer, L. D’Ambrosio, A. Pina, and J. Coughlin, "An Evaluation of Time of Day Influences on Simulated Driving Performance and Physiological Arousal," 2010
  44. 44) N.S.S. Al-Bdairi, S. Hernandez, and J. Anderson, "Contributing factors to run-off-road crashes involving large trucks under lighted and dark conditions," J. Transp. Eng., Part A: Systems, 144 (1) 04017066 (2018) doi:10.1061/JTEPBS.0000104
  45. 45) I. Marando, R.W. Matthews, L. Grosser, C. Yates, and S. Banks, "The effect of time on task, sleep deprivation, and time of day on simulated driving performance," Sleep, 45 (9) (2022) doi:10.1093/sleep/zsac167
  46. 46) S. Grassini, A. Revonsuo, S. Castellotti, I. Petrizzo, V. Benedetti, and M. Koivisto, "Processing of natural scenery is associated with lower attentional and cognitive load compared with urban ones," Journal of Environmental Psychology, 62 1-11 (2019) doi:10.1016/j.jenvp.2019.01.007
  47. 47) A. Cao, K.K. Chintamani, A.K. Pandya, and R.D. Ellis, "NASA tlx: software for assessing subjective mental workload," Behavior Research Methods, 41 (1) 113-117 (2009) doi:10.3758/BRM.41.1.113
  48. 48) E.H. Jang, A.Y. Kim, and H.Y. Yu, "Relationships of psychological factors to stress and heart rate variability as stress responses induced by cognitive stressors," Korean Society for Emotion and Sensibility, 21 (1) 71-82 (2018) doi:10.14695/KJSOS.2018.21.1.71
  49. 49) K. Čulík, A. Kalašová, and V. Štefancová, "Evaluation of driver’s reaction time measured in driving simulator," Sensors, 22 (9) 3542 (2022) doi:10.3390/s22093542
  50. 50) G. Casutt, N. Theill, M. Martin, M. Keller, and L. Jäncke, "The drive-wise project: driving simulator training increases real driving performance in healthy older drivers," Front. Aging Neurosci., 6 (2014) doi:10.3389/fnagi.2014.00085
  51. 51) A. Bojko, "Eye tracking the user experience: a practical guide to research," Rosenfeld Media, Brooklyn, New York, 2013
  52. 52) P. Green, "Estimating the workload of driving using video clips as anchors," SAE Int. J. Adv. & Curr. Prac. in Mobility, 4 (6) 2316-2334 (2022) doi:10.4271/2022-01-0805
  53. 53) H. Jiang, S. Mizobuchi, and M. Chignell, "Scenario Fidelity and Perceived Driver Mental Workload: Can Workload Assessment be Crowdsourced?," in: Proceedings of the 13th International Conference on Advances in Information Technology, ACM, Bangkok Thailand, 2023: pp. 1-6 doi:10.1145/3628454.3628458
  54. 54) P. Pillai, B. Balasingam, and F.N. Biondi, "Model-Based Estimation of Mental Workload in Drivers Using Pupil Size Measurements," in: 2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), IEEE, Seattle, WA, USA, 2023: pp. 815-821 doi:10.1109/AIM46323.2023.10196230
  55. 55) J.J. Saleem, and D.T. Weiler, "Performance, workload, and usability in a multiscreen, multi-device, information-rich environment," PeerJ Computer Science, 4 e162 (2018) doi:10.7717/peerj-cs.162
  56. 56) S. Park, G. Kyung, D. Choi, J. Yi, S. Lee, B. Choi, and S. Lee, "Effects of display curvature and task duration on proofreading performance, visual discomfort, visual fatigue, mental workload, and user satisfaction," Applied Ergonomics, 78 26-36 (2019) doi:10.1016/j.apergo.2019.01.014
  57. 57) M.D.R. Evans, P. Kelley, and J. Kelley, "Identifying the best times for cognitive functioning using new methods: matching university times to undergraduate chronotypes," Front. Hum. Neurosci., 11 (2017) doi:10.3389/fnhum.2017.00188
  58. 58) S. Folkard, "Shift work, safety and productivity," Occupational Medicine, 53 (2) 95-101 (2003) doi:10.1093/occmed/kqg047
  59. 59) P. Valdez, C. Ramírez, A. García, J. Talamantes, P. Armijo, and J. Borrani, "Circadian rhythms in components of attention," Biological Rhythm Research, 36 (1-2) 57-65 (2005) doi:10.1080/09291010400028633
  60. 60) D.H. Taan Al. Awar, A.H. Al Yafei, and A.A.S. Al Fehaidi, "Smoking and its effects on heart rate variability," Int J Res Med Sci, 10 (4) 933 (2022) doi:10.18203/2320-6012.ijrms20220988
  61. 61) H.P. Sondermeijer, A.G.J. Van Marle, P. Kamen, and H. Krum, "Acute effects of caffeine on heart rate variability," The American Journal of Cardiology, 90 (8) 906-907 (2002) doi:10.1016/S0002-9149(02)02725-X
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