پژوهشنامه حمل و نقل

پژوهشنامه حمل و نقل

ارائه مدل اثرگذاری حواس‌پرتی بر روی رفتار ناهنجار رانندگی برای رانندگان وسایل نقلیه سنگین

نوع مقاله : مقاله پژوهشی

نویسندگان
محمدرضا کارگرخبازی سردرود 1
عبدالرضا شیخ الاسلامی 2
علی خانپور 3
1 دانشجوی کارشناسی ارشد، دانشکده مهندسی عمران، دانشگاه علم و صنعت ایران، تهران، ایران
2 استادیار، دانشکده مهندسی عمران، دانشگاه علم و صنعت ایران، تهران، ایران
3 دانشکده مهندسی عمران، دانشگاه علم و صنعت ایران، تهران، ایران
10.22034/tri.2024.415885.3191
چکیده
حواس پرتی راننده به‌طور قابل‌توجهی عملکرد را مختل می کند و احتمال تصادف خودرو را افزایش می دهد. درک دلایل اساسی درگیر شدن حواس پرتی و همچنین حساسیت افراد به انواع مختلف حواس‌پرتی، گامی ضروری در توسعه راه‌حل‌های مؤثر برای کاهش حواس‌پرتی است. مطالعه حاضر باهدف بررسی تأثیر حواس پرتی رانندگان بر روی رفتار ناهنجار رانندگی انجام‌شده است. این مطالعه اطلاعات خود گزارشی 320 راننده حرفه‌ای وسایل نقلیه سنگین را بین سنین 31 تا 69 سال مورد تجزیه‌وتحلیل قرارداد که میزان پاسخ دهی 75 درصد بود. شرکت‌کنندگان علاوه بر پرسشنامه رفتار رانندگی به پرسشنامه حواس پرتی (درگیر شدن در حواس‌پرتی در حین رانندگی) و کیفیت خواب پیتسبورگ (PSQI) نیز پاسخ دادند. برای بررسی رفتار رانندگان وسایل نقلیه سنگین و بررسی اعتبار آن‌ها، از تحلیل عاملی تأییدی استفاده شد. پس از اتمام این مرحله، از مدل‌سازی معادلات ساختاری با استفاده از نرم‌افزار SmartPLS برای شناسایی تأثیر متغیرهایی نظیر حواس‌پرتی، سن، تحصیلات و تجربه رانندگی بر رفتارهای رانندگان وسایل نقلیه سنگین استفاده گردید. نرم‌افزار SmartPLS یکی از برنامه‌های کاربردی برجسته برای مدل سازی معادلات ساختاری حداقل مربعات جزئی (PLS-SEM) است. نتایج نشان داد که هرچقدر رانندگان وسایل نقلیه سنگین مدت بیشتری رانندگی کنند، با حواس‌پرتی بیشتری مواجه می‌شوند. این موضوع ممکن است باعث افزایش خطاها، لغزش‌ها و تخلفات در رانندگی گردد. همچنین، نتایج نشان داد که ویژگی های جمعیت‌شناسی نیز بر روی حواس پرتی در حین رانندگی تأثیر می گذارد.
کلیدواژه‌ها
پرسشنامه رفتار راننده
حواس‌پرتی
رانندگان وسایل نقلیه سنگین
شاخص کیفیت خواب پیتسبورگ

موضوعات

عنوان مقاله English

Presenting a Model of the Driver Distraction's Impact on Aberrant Driving Behavior for Heavy Vehicle Drivers

نویسندگان English

Mohammdreza Karegar Khabbazi Sardroud 1
Abdolreza Sheikholeslami 2
Ali Khanpour 3
1 M.Sc., Student, Department of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.
2 Assistant Professor, Department of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.
3 Department of Civil Engineering, Iran University of Science and Technology, Tehran, Iran.
چکیده English

Distracted driving significantly hampers performance and escalates the risk of vehicular accidents. It is crucial to comprehend the fundamental causes of driver distraction, as well as individuals' susceptibilities to various types of distractions, in order to develop effective interventions aimed at mitigating distraction. The present study seeks to explore the impact of driver distraction on deviant driving behaviors by examining a cohort of 320 professional drivers operating heavy vehicles, aged between 31 and 69, with a response rate of 75 percent. Apart from a driving behavior questionnaire, participants also completed a distraction questionnaire (assessing engagement in distractions while driving) and the Pittsburgh Sleep Quality Index (PSQI) questionnaire. Confirmatory factor analysis was employed to investigate the driving behaviors of the heavy vehicle drivers and assess the psychometric properties of the measurement instrument. Subsequently, structural equation modeling utilizing the SmartPLS software was conducted to discern the effects of variables such as distraction, age, education, and driving experience on the driving behaviors of heavy vehicle drivers. SmartPLS emerges as a prominent software application for conducting structural equation modeling based on the partial least squares technique (PLS-SEM). The findings revealed a positive association between the duration of heavy vehicle driving and the occurrence of driver distraction. This correlation may contribute to an increase in errors, slippages, and traffic violations. Moreover, the results demonstrated that demographic factors also exert an influence on driver distraction during the act of driving.

کلیدواژه‌ها English

Driver Behavior Questionnaire
Driver Distraction
Heavy Vehicle Drivers
Pittsburgh Sleep Quality Index
-Beanland, V., Fitzharris, M., Young, K. L., & Lenné, M. G. (2013). Driver inattention and driver distraction in serious casualty crashes: Data from the Australian National Crash In-depth Study. Accident Analysis & Prevention, 54, 99-107.
-Chapman, P., Roberts, K., & Underwood, G. (2001). A study of the accidents and behaviours of company car drivers. Paper presented at the Behavioural Research In Road Safety.Proceedings Of The 10th Seminar On Behavioural Research In Road Safety.
-Dimmer, A., & Parker, D. (1999). The accidents, attitude and behaviour of company car drivers. Paper presented at the Behavioural Research in Road Safety Ix. pa3524/99.
-Divekar, G., Pradhan, A. K., Pollatsek, A., & Fisher, D. L. (2012). Effect of external distractions: behavior and vehicle control of novice and experienced drivers evaluated. Transportation Research Record, 2321(1), 15-22.
-Farrahi Moghaddam, J., Nakhaee, N., Sheibani, V., Garrusi, B., & Amirkafi, A. (2012). Reliability and validity of the Persian version of the Pittsburgh Sleep Quality Index (PSQI-P). Sleep and Breathing, 16, 79-82.
-Feng, J., Marulanda, S., & Donmez, B. (2014). Susceptibility to driver distraction questionnaire: development and relation to relevant self-reported measures. Transportation Research Record, 2434(1), 26-34.
-Foley, J. P., Young, R., Angell, L., & Domeyer, J. E. (2013). Towards operationalizing driver distraction. Paper presented at the Driving Assessment Conference.
-Hwang, H., Malhotra, N. K., Kim, Y., Tomiuk, M. A., & Hong, S. (2010). A comparative study on parameter recovery of three approaches to structural equation modeling. Journal of Marketing Research, 47(4), 699-712.
-Kircher, K. (2007). Driver distraction: A review of the literature.
-Klauer, S. G., Dingus, T. A., Neale, V. L., Sudweeks, J. D., & Ramsey, D. J. (2006). The impact of driver inattention on near-crash/crash risk: An analysis using the 100-car naturalistic driving study data. Retrieved from .
-Koppel, S., Charlton, J. L., & Fildes, B. (2009). Distraction and the older driver.
-Lassmann, P., Fischer, M. S., Bieg, H.-J., Jenke, M., Reichelt, F., Tuezuen, G.-J., & Maier, T. (2020). Keeping the balance between overload and underload during partly automated driving: relevant secondary tasks. Paper presented at the Automatisiertes Fahren 2019: Von der Fahrerassistenz zum autonomen Fahren 5. Internationale ATZ-Fachtagung.
-Lawton, R., Parker, D., Manstead, A. S., & Stradling, S. G. (1997). The role of affect in predicting social behaviors: The case of road traffic violations. Journal of Applied Social Psychology, 27(14), 1258-1276.
-Martinussen, L. M., Lajunen, T., Møller, M., & Özkan, T. (2013). Short and user-friendly: The development and validation of the Mini-DBQ. Accident Analysis & Prevention, 50, 1259-1265.
-Naderi, H., Nassiri, H., & Sahebi, S. (2018). Assessing the relationship between heavy vehicle driver sleep problems and confirmed driver behavior measurement tools in Iran. Transportation research part F: traffic psychology and behaviour, 59, 57-66.
-National Transportation Safety Board. (1995). National Transportation Safety Board, 1995.
-NHTSA. (2014). National Highway Traffic Safety Administration.
https://nhtsa.gov/risky-driving/distracted-driving.
-NHTSA. (2019). National Highway Traffic Safety Administration Distracted Driving 2016. https://nhtsa.gov/risky-driving/distracted-driving.
-NHTSA. (2020). National Highway Traffic Safety Administration. Distracted Driving 2017.
http://gov/risky-driving/distracted-driving
-Parker, D., Lajunen, T., & Stradling, S. (1998). Attitudinal predictors of interpersonally aggressive violations on the road. Transportation research part F: Traffic Psychology and Behaviour, 1(1), 11-24.
-Qin, L., Li, Z. R., Chen, Z., Bill, M. A., & Noyce, D. A. (2019). Understanding driver distractions in fatal crashes: An exploratory empirical analysis. Journal of Safety Research, 69, 23-31.
-Ranney, T. A. (2008). Driver distraction: A review of the current state-of-knowledge.
-Reason, J., Manstead, A., Stradling, S., Baxter, J., & Campbell, K. (1990). Errors and violations on the roads: a real distinction? Ergonomics, 33(10-11), 1315-1332.
-Regan, M. A., Lee, J. D., & Young, K. (2008). Driver distraction: Theory, effects, and mitigation: CRC press.
-Rowe, R., Maughan, B., Gregory, A. M., & Eley, T. C. (2013). The development of risky attitudes from pre-driving to fully-qualified driving. Injury Prevention, 19(4), 244-249.
-Rowe, R., Roman, G. D., McKenna, F. P., Barker, E., & Poulter, D. (2015). Measuring errors and violations on the road: A bifactor modeling approach to the Driver Behavior Questionnaire. Accident Analysis & Prevention, 74, 118-125.
-Sadeghniiat-Haghighi, K., Yazdi, Z., & Kazemifar, A. M. (2016). Sleep quality in long haul truck drivers: A study on Iranian national data.Chinese Journal of Traumatology, 19(04), 225-228.
-Sheridan, T. B. (2004). Driver distraction from a control theory perspective. Human Factors, 46(4), 587-599.
-Singh, S. (2010). Distracted driving and driver, roadway, and environmental factors. Retrieved from .
-Sullman, M. J., Meadows, M. L., & Pajo, K. B. (2002). Aberrant driving behaviours amongst New Zealand truck drivers. Transportation research part F: traffic psychology and behaviour, 5(3), 217-232.
-Washington, D. C. N. (2013). What Is Distracted Driving?
http://www. distraction.gov/content/get-the-facts/facts-and-statistics.html.
-Wong, K. K.K. (2013). Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin, 24(1), 1-32.
-Wu, J., & Xu, H. (2018). The influence of road familiarity on distracted driving activities and driving operation using naturalistic driving study data. Transportation Research Part F: Traffic Psychology and Behaviour, 52, 75-85.
-Zhou, R., Zhang, Y., & Shi, Y. (2020). Driver's distracted behavior: The contribution of compensatory beliefs increases with higher perceived risk. International Journal of Industrial Ergonomics, 80, 103009.