بررسی عوامل موثر بر شدت تصادفات وسایل‌نقلیه سنگین در راه‌های برون‌شهری با استفاده از رگرسیون لاجیت

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشکده مهندسی عمران، دانشکده فنی، دانشگاه گیلان

2 دانشکده عمران، راه و ترابری، دانشکده فنی، دانشگاه گیلان

3 گروه مهندسی عمران، دانشکده مهندسی، دانشگاه فردوسی مشهد، مشهد، ایران

4 دانشکده مهندسی عمران، دانشگاه علم و صنعت ایران

چکیده

با توجه به رشد جمعیت و افزایش تعداد وسایل‌نقلیه در راه‌های برون‌شهری، تصادفات رانندگی یکی از مهمترین مشکلات سیستم حمل و نقل می باشد. وسایل‌نقلیه سنگین تاثیر زیادی در پیشرفت اقتصادی کشور‌های در‌حال توسعه مانند ایران دارند. وسایل‌نقلیه سنگین حجم زیادی از کالا را در داخل کشور جا‌به‌جا می‌کند. راه‌های برون‌شهری در استان گیلان، به‌دلیل امکان بازرگانی با کشور‌های حاشیه دریای‌خزر و وجود زمین‌های کشاورزی، تردد حجم زیادی از وسایل‌نقلیه سنگین را تجربه می‌کنند. این پژوهش به‌ بررسی تصادفات وسایل‌نقلیه سنگین در راه‌های برون‌شهری و بررسی عوامل تاثیرگذار بر شدت تصادف با استفاده از آزمون‌های آماری کولموگروف اسمیرنوف و آزمون فریدمن و همچنین رگرسیون لاجیت می‌پردازد. نتایج نشان می‌دهد وجود قوس در هندسه‌راه، برخورد جلو‌به‌جلو، تصادفات ساعات غیر‌اوج ( یک تا شش صبح) و ساعات اوج عصر ( 19 تا نیمه شب)، بومی‌بودن راننده و برخورد با ماشین آلات‌کشاورزی باعث افزایش شدت‌ تصادفات می‌شود. همچنین موتورسواران آسیب‌پذیرترین کاربران راه در برخورد با وسایل‌نقلیه سنگین هستند. از سوی دیگر، عواملی نظیر وجود میانه‌راه، نوع برخورد بغل‌به‌بغل و آب و هوای صاف باعث کاهش شدت تصادفات می‌شود.

کلیدواژه‌ها

عنوان مقاله [English]

Discovering the Factors Affecting the Severity of Heavy Vehicle Crashes on Rural Roads Using Logit Regression

نویسندگان [English]

  • Iraj Bargegol 1
  • Mohammad Rahmaninezhad Asil 2
  • Saeed Fatemi 3
  • Seyed Amir Saadatjoo 4
1 Department of Civil Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran
2 Department of Civil Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran
3 Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
4 Faculty of Civil Engineering, Iran University of Science and Technology
چکیده [English]

Due to population growth and the increasing number of vehicles on rural roads, traffic accidents have become one of the most important problems in the transportation system. Heavy vehicles have a great impact on the economic progress of developing countries such as Iran. Due to the possibility of trade with the Caspian littoral countries and the existence of agricultural lands, rural roads in Guilan province experience a large volume of heavy vehicles. This study aims to investigate the factors that influence the severity of heavy vehicle crashes on rural roads using Kolmogorov-Smirnov and Friedman tests as well as a logit regression model. The results show that the presence of curves in road geometry, head-on collisions, off-peak crashes (1 AM to 6 AM), and peak crashes (7 PM-midnight), familiar drivers, and agricultural vehicles are associated with more severe injuries. Furthermore, motorcyclists are the most vulnerable road users in crashes involving heavy vehicles. On the other hand, some factors such as the presence of road median, side-swipe collision type, and clear weather reduce the severity of crashes.

کلیدواژه‌ها [English]

  • Crash severity
  • Heavy vehicles
  • Rural roads
  • Logit regression
  1. Hosseinian, S.M. and Gilani, V.N.M., "Analysis of factors affecting urban road accidents in Rasht metropolis", Eng Transactions, Vol. 1, pp. 1-4. (2020).
  2. Hosseinian, S.M., Najafi Moghaddam Gilani, V., Mirbaha, B. and Abdi Kordani, A., "Statistical analysis for study of the effect of dark clothing color of female pedestrians on the severity of accident using machine learning methods", Mathematical Problems in Engineering, Vol. 2021, pp. 1-21. (2021).
  3. Rahimi, E., Shamshiripour, A., Samimi, A. and Mohammadian, A.K., "Investigating the injury severity of single-vehicle truck crashes in a developing country", Accident Analysis & Prevention, Vol. 137, pp. 105444. (2020).
  4. Effati, M. and Saheli, M.V., "Examining the influence of rural land uses and accessibility-related factors to estimate pedestrian safety: The use of GIS and machine learning techniques", International journal of transportation science and technology, Vol. 11, pp. 144-157. (2022).
  5. Vahedi Saheli, M. and Effati, M., "Segment-based count regression geospatial modeling of the effect of roadside land uses on pedestrian crash frequency in rural roads", International journal of intelligent transportation systems research, Vol. 19, pp. 347-365. (2021).
  6. Saheli, M.V. and Effati, M., "Examining the impact of land-use related factors on rural traffic collisions", Journal of Injury and Violence Research, Vol. 11, (2019).
  7. Wang, Y., Luo, Y. and Chen, F., "Interpreting risk factors for truck crash severity on mountainous freeways in Jiangxi and Shaanxi, China", European transport research review, Vol. 11, pp. 1-11. (2019).
  8. Chang, L.-Y. and Chien, J.-T., "Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model", Safety science, Vol. 51, pp. 17-22. (2013).
  9. Kaplan, S. and Prato, C.G., "Risk factors associated with bus accident severity in the United States: A generalized ordered logit model", Journal of safety research, Vol. 43, pp. 171-180. (2012).
  10. Azimi, G., Rahimi, A., Asgari, H. and Jin, X., "Severity analysis for large truck rollover crashes using a random parameter ordered logit model", Accident Analysis & Prevention, Vol. 135, pp. 105355. (2020).
  11. Behnood, A. and Al-Bdairi, N.S.S., "Determinant of injury severities in large truck crashes: A weekly instability analysis", Safety science, Vol. 131, pp. 104911. (2020).
  12. Ruxton, G.D., Wilkinson, D.M. and Neuhäuser, M., "Advice on testing the null hypothesis that a sample is drawn from a normal distribution", Animal Behaviour, Vol. 107, pp. 249-252. (2015).
  13. Eisinga, R., Heskes, T., Pelzer, B. and Te Grotenhuis, M., "Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers", BMC bioinformatics, Vol. 18, pp. 1-18. (2017).
  14. Kamboozia, N., Ameri, M. and Hosseinian, S.M., "Statistical analysis and accident prediction models leading to pedestrian injuries and deaths on rural roads in Iran", International journal of injury control and safety promotion, Vol. 27, pp. 493-509. (2020).
  15. Najafi Moghaddam Gilani, V., Hosseinian, S.M., Ghasedi, M. and Nikookar, M., "Data-driven urban traffic accident analysis and prediction using logit and machine learning-based pattern recognition models", Mathematical problems in engineering, Vol. 2021, (2021).
  16. Ghasedi, M., Sarfjoo, M. and Bargegol, I., "Prediction and analysis of the severity and number of suburban accidents using logit model, factor analysis and machine learning: a case study in a developing country", SN Applied Sciences, Vol. 3, pp. 1-16. (2021).

مراجع

  1. Hosseinian, S.M. and Gilani, V.N.M., "Analysis of factors affecting urban road accidents in Rasht metropolis", Eng Transactions, Vol. 1, pp. 1-4. (2020).
  2. Hosseinian, S.M., Najafi Moghaddam Gilani, V., Mirbaha, B. and Abdi Kordani, A., "Statistical analysis for study of the effect of dark clothing color of female pedestrians on the severity of accident using machine learning methods", Mathematical Problems in Engineering, Vol. 2021, pp. 1-21. (2021).
  3. Rahimi, E., Shamshiripour, A., Samimi, A. and Mohammadian, A.K., "Investigating the injury severity of single-vehicle truck crashes in a developing country", Accident Analysis & Prevention, Vol. 137, pp. 105444. (2020).
  4. Effati, M. and Saheli, M.V., "Examining the influence of rural land uses and accessibility-related factors to estimate pedestrian safety: The use of GIS and machine learning techniques", International journal of transportation science and technology, Vol. 11, pp. 144-157. (2022).
  5. Vahedi Saheli, M. and Effati, M., "Segment-based count regression geospatial modeling of the effect of roadside land uses on pedestrian crash frequency in rural roads", International journal of intelligent transportation systems research, Vol. 19, pp. 347-365. (2021).
  6. Saheli, M.V. and Effati, M., "Examining the impact of land-use related factors on rural traffic collisions", Journal of Injury and Violence Research, Vol. 11, (2019).
  7. Wang, Y., Luo, Y. and Chen, F., "Interpreting risk factors for truck crash severity on mountainous freeways in Jiangxi and Shaanxi, China", European transport research review, Vol. 11, pp. 1-11. (2019).
  8. Chang, L.-Y. and Chien, J.-T., "Analysis of driver injury severity in truck-involved accidents using a non-parametric classification tree model", Safety science, Vol. 51, pp. 17-22. (2013).
  9. Kaplan, S. and Prato, C.G., "Risk factors associated with bus accident severity in the United States: A generalized ordered logit model", Journal of safety research, Vol. 43, pp. 171-180. (2012).
  10. Azimi, G., Rahimi, A., Asgari, H. and Jin, X., "Severity analysis for large truck rollover crashes using a random parameter ordered logit model", Accident Analysis & Prevention, Vol. 135, pp. 105355. (2020).
  11. Behnood, A. and Al-Bdairi, N.S.S., "Determinant of injury severities in large truck crashes: A weekly instability analysis", Safety science, Vol. 131, pp. 104911. (2020).
  12. Ruxton, G.D., Wilkinson, D.M. and Neuhäuser, M., "Advice on testing the null hypothesis that a sample is drawn from a normal distribution", Animal Behaviour, Vol. 107, pp. 249-252. (2015).
  13. Eisinga, R., Heskes, T., Pelzer, B. and Te Grotenhuis, M., "Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers", BMC bioinformatics, Vol. 18, pp. 1-18. (2017).
  14. Kamboozia, N., Ameri, M. and Hosseinian, S.M., "Statistical analysis and accident prediction models leading to pedestrian injuries and deaths on rural roads in Iran", International journal of injury control and safety promotion, Vol. 27, pp. 493-509. (2020).
  15. Najafi Moghaddam Gilani, V., Hosseinian, S.M., Ghasedi, M. and Nikookar, M., "Data-driven urban traffic accident analysis and prediction using logit and machine learning-based pattern recognition models", Mathematical problems in engineering, Vol. 2021, (2021).
  16. Ghasedi, M., Sarfjoo, M. and Bargegol, I., "Prediction and analysis of the severity and number of suburban accidents using logit model, factor analysis and machine learning: a case study in a developing country", SN Applied Sciences, Vol. 3, pp. 1-16. (2021).
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