مهندسی سیستم و بهره‌وری

مهندسی سیستم و بهره‌وری

شناسایی عوامل مؤثر بر بروز احساسات منفی و رفتار رانندگی با به‌کارگیری رویکرد معادلات ساختاری

نوع مقاله : پژوهشی

نویسندگان
مریم کاوئی 1
ناصر صفایی 2
حامد سلمان‌زاده 3
1 دانشجوی دکتری، گروه مهندسی صنایع، دانشگاه صنعتی خواجه‌نصیرالدین طوسی، تهران، ایران
2 نویسنده مسئول: استادیار، گروه مهندسی صنایع، دانشگاه صنعتی خواجه‌نصیرالدین طوسی، تهران، ایران
3 استادیار، گروه مهندسی صنایع، دانشگاه صنعتی خواجه‌نصیرالدین طوسی، تهران، ایران
10.22034/sep.2025.2058439.1314
چکیده
رانندگی یک فعالیت روزمره پیچیده است که نیازمند پردازش اطلاعات و تصمیم‌گیری‌های سریع است. این فرایند، به علت احتمال وقوع خطای انسانی، می‌تواند منبعی خطرناک و پرمخاطره باشد. تحقیقات نشان می‌دهند که عامل انسانی، به‌عنوان مهم‌ترین و پیچیده‌ترین مؤلفه در مثلث ایمنی رانندگی (وسیله نقلیه-محیط-راننده)، تأثیر قابل‌توجهی بر رفتار رانندگی دارد. این مطالعه به تحلیل عوامل مؤثر بر برانگیختگی احساسات منفی و رفتار رانندگی پرداخته است. رانندگی می‌تواند احساسات منفی مانند عصبانیت و استرس را ایجاد کند که هم سلامت روانی افراد و هم ایمنی جاده‌ها را به خطر می‌اندازد. تحقیق حاضر با استفاده از پرسشنامهٔ استاندارد رفتار رانندگی منچستر و پرسشنامه طراحی‌شده بر اساس ادبیات موضوع و داده‌های جمع‌آوری‌شده از ۴۳۶ دانشجوی دانشگاه صنعتی خواجه‌نصیرالدین طوسی، به بررسی عوامل مؤثر بر رفتار رانندگی پرداخته است. تحلیل داده‌ها با روش مدل‌سازی معادلات ساختاری و تکنیک حداقل مربعات جزئی (PLS-SEM) انجام‌شده است. عواملی مانند سن، جنسیت، تجربه رانندگی، ویژگی‌های شخصیتی و شرایط محیطی نظیر آب‌وهوا، روشنایی و زمان روز، نقش کلیدی در برانگیختگی احساسات منفی و شکل‌گیری رفتار رانندگی دارند. نتایج پژوهش نشان داد که مدل ارائه‌شده از اعتبار و پایایی بالایی برخوردار بوده و مقدار آلفای کرونباخ بالاتر از 7/0 تأیید شد. همچنین متغیرهای پیش‌بینی‌کننده مستقل بوده و هم‌پوشانی ندارند. تمام ۱۱ فرضیه مطرح‌شده پذیرفته شدند و مقدار Q2 نشان‌دهنده قدرت پیش‌بینی مدل است. این تحقیق، با ارائه بینش‌های کلیدی درباره رفتار رانندگی، زمینه‌ای ارزشمند برای پژوهش‌های آتی فراهم کرده است و می‌تواند در کاهش خطرات رانندگی و بهبود ایمنی جاده‌ها مؤثر باشد.

تازه های تحقیق

  • هدف شناسایی عوامل کلیدی مؤثر بر برانگیختگی احساسات منفی و رفتار رانندگی است.
  • با استفاده از پرسشنامه طراحی‌شده و پرسشنامه رفتار رانندگی منچستر اطلاعات لازم جمع‌آوری‌شده است.
  • نتیجه روش حداقل مربعات در رویکرد معادلات ساختاری نشان داد مدل ارائه‌شده از اعتبار بالایی برخوردار است و مدل قدرت پیش‌بینی خوبی دارد.

کلیدواژه‌ها
عامل انسانی
برانگیختگی احساسات منفی
معادلات ساختاری
پرسشنامه رفتار رانندگی
ایمنی جاده

عنوان مقاله English

Identifying Factors Affecting the Occurrence of Negative Emotions and Driving Behavior Using the Structural Equation Modeling

نویسندگان English

Maryam Kaveie 1
Nasser Safaie 2
Hamed Salmanzadeh 3
1 Ph.D. Student, Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
2 Corresponding author: Assistant Professor, Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
3 Assistant Professor, Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
چکیده English

Driving is a complex daily activity that requires rapid information processing and decision-making. Due to the potential for human error, this process can pose significant risks. Research indicates that the human factor—the most critical and complex component in the driving safety triangle (vehicle-environment-driver)—profoundly influences driving behavior. This study analyzes factors contributing to the arousal of negative emotions and their impact on driving behavior. Driving often triggers negative emotions such as anger and stress, jeopardizing both mental health and road safety. Using the standardized Manchester Driving Behavior Questionnaire and a custom survey based on literature, data were collected from 436 students at K. N. Toosi University of Technology. Structural equation modeling (SEM) and partial least squares SEM (PLS-SEM) were employed for analysis. Key factors—including age, gender, driving experience, personality traits, and environmental conditions (e.g., weather, lighting, time of day)—were found to significantly influence negative emotions and driving behavior. Results demonstrated high validity and reliability of the proposed model, with Cronbach’s alpha exceeding 0.7. Independent predictor variables showed no multicollinearity. All 11 hypotheses were supported, and the Q² value confirmed the model’s predictive power. By offering critical insights into driving behavior, this study provides a valuable foundation for future research and practical interventions to mitigate driving risks and enhance road safety.

کلیدواژه‌ها English

Human Factor
Negative Emotion Arousal
Structural Equations
Driving Behavior Questionnaire
Road safety

Copyright © Maryan Kaveie. Nasser Safaie, Hamed Salmanzadeh

 

License

This article is released under the Creative Commons Attribution (CC BY 4.0) license. Anyone is free to copy, share, translate, and adapt this article for any purpose, whether commercial or non-commercial, as long as proper citation is given to the authors and original publication.

Ahmed, M. M., & Ghasemzadeh, A. (2018). The impacts of heavy rain on speed and headway behaviors: An investigation using the SHRP2 naturalistic driving study data. Transportation Research Part C: Emerging Technologies, 91, 371–384. https://doi.org/10.1016/j.trc.2018.04.012
Beyer, F. R., & Ker, K. (2009). Street lighting for preventing road traffic injuries. Cochrane Database of Systematic Reviews, (1), Article CD004728. https://doi.org/10.1002/14651858.CD004728.pub2
Borghans, L., Duckworth, A. L., Heckman, J. J., & ter Weel, B. (2008). The economics and psychology of personality traits. Journal of Human Resources, 43(4), 972–1059. https://doi.org/10.3368/jhr.43.4.972
Bozorg, S., Tetri, E., Kosonen, I., & Luttinen, T. (2018). The effect of dimmed road lighting and car headlights on visibility in varying road surface conditions. Leukos, 14(4), 259–273. https://doi.org/10.1080/15502724.2018.1452152
Brodsky, H., & Hakkert, A. S. (1988). Risk of a road accident in rainy weather. Accident Analysis & Prevention, 20(3), 161–176. https://doi.org/10.1016/0001-4575(88)90001-2
Byrne, B. M. (2013). Structural equation modeling with Mplus: Basic concepts, applications, and programming. Routledge. https://doi.org/10.4324/9780203807644
Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In Modern methods for business research (pp. 295-336). Psychology Press.
Chung, E. K., Park, S. Y., & Sohn, Y. W. (2021). Effects of occupational drivers’ traffic accident history on risk perception, work attitudes, and unstable driving. Transportation Research Record, 2675(10), 734–744. https://doi.org/10.1177/03611981211012695
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates. https://doi.org/10.4324/9780203771587
Das, A., Ghasemzadeh, A., & Ahmed, M. M. (2019). Analyzing the effect of fog weather conditions on driver lane-keeping performance using the SHRP2 naturalistic driving study data. Journal of Safety Research, 68, 71–80. https://doi.org/10.1016/j.jsr.2018.12.015
Davtalab Esmaeili, E., Kalankesh, L. R., Ghaffari, A., Zeinalzadeh, A. H., & Dastgiri, S. (2025). Familial aggregation of high-risk driving behaviors in Northwestern Iran: A cross-sectional study. Journal of Advanced Transportation, 2025, Article 9969847. https://doi.org/10.1155/atr/9969847
Deniz, P., Lajunen, T., Özkan, T., & Gaygısız, E. (2021). Masculinity, femininity, and angry drivers: Masculinity and femininity as moderators between driver anger and anger expression style among young drivers. Accident Analysis & Prevention, 161, Article 106347. https://doi.org/10.1016/j.aap.2021.106347
Disassa, A., & Kebu, H. (2019). Psychosocial factors as predictors of risky driving behavior and accident involvement among drivers in Oromia Region, Ethiopia. Heliyon, 5(6), Article e01876. https://doi.org/10.1016/j.heliyon.2019.e01876
Dotse, J. E., & Rowe, R. (2021). Modelling Ghanaian road crash risk using the Manchester Driver Behaviour Questionnaire. Safety Science, 139, Article 105213. https://doi.org/10.1016/j.ssci.2021.105213
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Gruber, R., & Cassoff, J. (2014). The interplay between sleep and emotion regulation: Conceptual framework, empirical evidence and future directions. Current Psychiatry Reports, 16(11), Article 500. https://doi.org/10.1007/s11920-014-0500-x
Gutmann, M., Grausberg, P., & Kyamakya, K. (2015). Detecting human driver’s physiological stress and emotions using sophisticated one-person cockpit vehicle simulator. In 2015 Information Technologies in Innovation Business Conference (ITIB) (pp. 15–18). IEEE. https://doi.org/10.1109/ITIB.2015.7355064
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. https://doi.org/10.1007/978-3-030-80519-7
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
Hassan, H. M., & Al-Faleh, H. (2013). Exploring the risk factors associated with the size and severity of roadway crashes in Riyadh. Journal of Safety Research, 47, 67–74. https://doi.org/10.1016/j.jsr.2013.09.002
Healey, J. A., & Picard, R. W. (2005). Detecting stress during real-world driving tasks using physiological sensors. IEEE Transactions on Intelligent Transportation Systems, 6(2), 156–166. https://doi.org/10.1109/TITS.2005.848368
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. In R. R. Sinkovics & P. N. Ghauri (Eds.), New challenges to international marketing (Vol. 20, pp. 277–319). Emerald Group Publishing. https://doi.org/10.1108/S1474-7979(2009)0000020014
Iversen, H., & Rundmo, T. (2002). Personality, risky driving and accident involvement among Norwegian drivers. Personality and Individual Differences, 33(8), 1251–1263. https://doi.org/10.1016/S0191-8869(02)00010-7
Jing, L., Shan, W., & Zhang, Y. (2023). Risk preference, risk perception as predictors of risky driving behaviors: The moderating effects of gender, age, and driving experience. Journal of Transportation Safety & Security, 15(5), 467–492. https://doi.org/10.1080/19439962.2022.2086953
Johansson, Ö., Wanvik, P. O., & Elvik, R. (2009). A new method for assessing the risk of accident associated with darkness. Accident Analysis & Prevention, 41(4), 809–815. https://doi.org/10.1016/j.aap.2009.04.003
Jovanović, D., Lipovac, K., Stanojević, P., & Stanojević, D. (2011). The effects of personality traits on driving-related anger and aggressive behaviour in traffic among Serbian drivers. Transportation Research Part F: Traffic Psychology and Behaviour, 14(1), 43–53. https://doi.org/10.1016/j.trf.2010.09.005
Kashanian Monfared, N., Safaie, N., & Hosseini Nezhad, S. J. (2025). A decision-making model for the problem of designing the layout of medical centers considering uncertainty. System Engineering and Productivity, 5(2), 95–118. https://doi.org/10.22034/sep.2025.2049327.1252 (In Persian)
Khan, M. N., Ghasemzadeh, A., & Ahmed, M. M. (2018). Investigating the impact of fog on freeway speed selection using the SHRP2 naturalistic driving study data. Transportation Research Record, 2672(16), 93–104. https://doi.org/10.1177/0361198118774748
Kline, R. B. (2023). Principles and practice of structural equation modeling (5th ed.). Guilford Publications.
Koob, G. F. (2015). The dark side of emotion: The addiction perspective. European Journal of Pharmacology, 753, 73–87. https://doi.org/10.1016/j.ejphar.2014.11.044
Köster, E. P., & Mojet, J. (2015). From mood to food and from food to mood: A psychological perspective on the measurement of food-related emotions in consumer research. Food Research International, 76(3), 180–191. https://doi.org/10.1016/j.foodres.2015.04.006
Kuipers, J., de Winter, J., & Mulder, M. (2023). From fear to forecast: The role of simulators, accompanied driving, age, gender, and information-processing style in driver training and beyond. Transportation Research Part F: Traffic Psychology and Behaviour, 99, 389–407. https://doi.org/10.1016/j.trf.2023.10.003
Lajunen, T., & Summala, H. (1995). Driving experience, personality, and skill and safety-motive dimensions in drivers’ self-assessments. Personality and Individual Differences, 19(3), 307–318. https://doi.org/10.1016/0191-8869(95)00068-H
Lee, S. E., Simons-Morton, B. G., Klauer, S. G., Ouimet, M. C., & Dingus, T. A. (2011). Naturalistic assessment of novice teenage crash experience. Accident Analysis & Prevention, 43(4), 1472–1479. https://doi.org/10.1016/j.aap.2011.02.026
Leguina, A. (2015). A primer on partial least squares structural equation modeling (PLS-SEM). International Journal of Research & Method in Education, 38(2), 220–221. https://doi.org/10.1080/1743727X.2015.1005806
Marillier, M., & Verstraete, A. G. (2019). Driving under the influence of drugs. WIREs Forensic Science, 1(3), Article e1326. https://doi.org/10.1002/wfs2.1326
Martinez, C. M., Heucke, M., Wang, F.-Y., Gao, B., & Cao, D. (2018). Driving style recognition for intelligent vehicle control and advanced driver assistance: A survey. IEEE Transactions on Intelligent Transportation Systems, 19(3), 666–676. https://doi.org/10.1109/TITS.2017.2706978
Mohammadi Zaidi, I., Akbari, M., & Morshidi, H. (2024). Predicting safe driving behaviors in intercity taxi drivers using the Theory of Planned Behavior framework: A cross-sectional descriptive study. Iranian Biomedical Journal, 28(7), 431. https://doi.org/10.61186/ibj.25th-11th-IACRTIMSS
Murphy, A., Taylor, E., & Elliott, R. (2012). The detrimental effects of emotional process dysregulation on decision-making in substance dependence. Frontiers in Integrative Neuroscience, 6, Article 101. https://doi.org/10.3389/fnint.2012.00101
Ogden, E. J., & Moskowitz, H. (2004). Effects of alcohol and other drugs on driver performance. Traffic Injury Prevention, 5(3), 185–198. https://doi.org/10.1080/15389580490465201
Qiang, W., & Li, F. (2025). Work stressors and aggressive driving: The mediating roles of stress appraisals. Safety Science, 181, Article 106656. https://doi.org/10.1016/j.ssci.2024.106656
Rashmi, B. S., & Marisamynathan, S. (2024). An investigation of relationships between aberrant driving behavior and crash risk among long-haul truck drivers traveling across India: A structural equation modeling approach. Journal of Transport & Health, 38, Article 101871. https://doi.org/10.1016/j.jth.2024.101871
Safaie, N., Heidari, S. Y., & Mirzaee Ghazani, M. (2025). A data-driven hybrid approach for examining the factors influencing the price of EUA during Phase IV of the EUETS. System Engineering and Productivity, 5(1), 113–134. https://doi.org/10.22034/sep.2025.2050780.1253 (In Persian)
Shangguan, Z., Han, X., Mrhasli, Y. E., Lyu, N., & Tapus, A. (2025). Factors influencing emotional driving: Examining the impact of arousal on the interplay between age, personality, and driving behaviors. Frontiers in Psychology, 16, Article 1487493. https://doi.org/10.3389/fpsyg.2025.1487493
Sheykhfard, A., Haghighi, F., Fountas, G., Das, S., & Khanpour, A. (2023). How do driving behavior and attitudes toward road safety vary between developed and developing countries? Evidence from Iran and the Netherlands. Journal of Safety Research, 85, 210–221. https://doi.org/10.1016/j.jsr.2023.02.005
Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111–133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
Tronsmoen, T. (2010). Associations between driver training, determinants of risky driving behaviour and crash involvement. Safety Science, 48(1), 35–45. https://doi.org/10.1016/j.ssci.2009.05.001
Ulleberg, P. (2001). Personality subtypes of young drivers: Relationship to risk-taking preferences, accident involvement, and response to a traffic safety campaign. Transportation Research Part F: Traffic Psychology and Behaviour, 4(4), 279–297. https://doi.org/10.1016/S1369-8478(01)00029-8
Van Der Zwaag, M. D., Dijksterhuis, C., de Waard, D., Mulder, B. L. J. M., Westerink, J. H. D. M., & Brookhuis, K. A. (2012). The influence of music on mood and performance while driving. Ergonomics, 55(1), 12–22. https://doi.org/10.1080/00140139.2011.638403
Wu, C., Chu, W., Zhang, H., & Özkan, T. (2018). Interactions between driving skills on aggressive driving: Study among Chinese drivers. Transportation Research Record, 2672(31), 10–20. https://doi.org/10.1177/0361198118755683
Yang, L., Li, X., Guan, W., & Jiang, S. (2022). Assessing the relationship between driving skill, driving behavior and driving aggressiveness. Journal of Transportation Safety & Security, 14(5), 737–753. https://doi.org/10.1080/19439962.2020.1812785
Yang, Z., Shi, Z., Lu, D., & Liu, J. (2024). Correcting the cognitive bias for commuting time to relieve the driving stress level in snow weather condition: A naturalistic driving study in Harbin, China. Journal of Advanced Transportation, 2024, Article 8474050. https://doi.org/10.1155/2024/8474050
Youssef, D., Salameh, P., Abou-Abbas, L., & Salmi, L. R. (2023). Driving anger dimensions and their relationship with aberrant driver behavior in Lebanon: Results from a national self-reported survey. PLoS ONE, 18(3), Article e0283293. https://doi.org/10.1371/journal.pone.0283293
Zafar, S., Abdullah, M., Javid, M. A., & Ali, N. (2024). Assessing the determinants of crash propensity using structural equation modeling: Role of distractions caused by fellow drivers. Journal of Safety Research, 89, 210–223. https://doi.org/10.1016/j.jsr.2024.02.012
Zhang, Z., Ma, T., Ji, N., Hu, Z., & Zhu, W. (2019). An assessment of the relationship between driving skills and driving behaviors among Chinese bus drivers. Advances in Mechanical Engineering, 11(1). https://doi.org/10.1177/1687814018824916
مهندسی سیستم و بهره‌وری
دوره 5، شماره 4 - شماره پیاپی 17
شماره پیاپی 17، فصلنامه زمستان
زمستان 1404
صفحه 71-99

  • تاریخ دریافت 01 اردیبهشت 1404
  • تاریخ بازنگری 01 خرداد 1404
  • تاریخ پذیرش 15 خرداد 1404
  • تاریخ اولین انتشار 24 تیر 1404
  • تاریخ انتشار 01 دی 1404