مدل فراوانی و شدت تصادفات برون‌شهری با به‌کارگیری الگوی رگرسیون پواسون

نوع مقاله : پژوهشی

نویسندگان

1 دانشگاه تربیت مدرس تهران

2 تربیت مدرس

3 دانشگاه صنعتی خواجه نصیر الدین طوسی

چکیده

یکی از راهکارهای مهم شناسایی نقاط داری پتانسیل تصادف در جاده­ها، ممیزی ایمنی مسیر است. پس از شناسایی این نقاط می­توان برای آنها استراتژی‌های مناسبی درنظر گرفت تا فراوانی و شدت تصادفات کاهش یابد. در این پژوهش بااستفاده از ممیزی ایمنی مسیر مشکلات دسترسی‌ها،  روسازی، علائم، کنار جاده، طرح هندسی و تصادفات جرحی و فوتی قطعات راه‌های برون‌شهری شناسایی گردید و بااستفاده از این داده­ها الگوی ساخت مدل تصادفات در آنها ارائه گردید. در این مقاله محور همدان- کرمانشاه به‌عنوان مطالعه موردی انتخاب شد که در محور مذکور مدل رگرسیون پواسونی دارای اعتبار بیشتری بود و مشکلات آب‌روها و قوس قائم به‌ترتیب مهم‌ترین دلایل بروز تصادف فوتی و جرحی بودند.

کلیدواژه‌ها


عنوان مقاله [English]

Poisson Regression Model of Frequency and Severity of Road Accidents in Rural Roads.

نویسندگان [English]

  • morteza asdamraji 1
  • seyedehsan seyedabrishami 2
  • Mahmoud Saffarzadeh 2
  • moin askari 3
1 Tarbiat Modares
2 Tarbiat Modares
3 K. N. Toosi University of Technology
چکیده [English]

One of the important ways of identifying road accident potentialities is road safety audits. After identifying these points, appropriate strategies to reduce the number and severity of accidents can be considered for them. In this study, using road safety audits, access, pavements, signs, roadside, and geometric problems and accidents leading to the deaths of outlying urban road sections were identified and using these data, the model of crashes in them Was presented. In this paper, the Hamadan-Kermanshah axis was chosen as a case study. In this axis, Poisson's regression model was more reliable and the problems of drainage ditches and vertical alignment were the most important reasons for the accident and resulted in death.

کلیدواژه‌ها [English]

  • "Safety Audit
  • Suburban road
  • Accident model
1. Singh, S., "Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey", No. DOT HS 812 115, (2015).
2. سیمای ایمنی راه‌ها (2)، گزارش ملی وضعیت ایمنی رانندگی در ایران، وزارت راه و شهرسازی، (1394).
2. Highway Safety Manual (HSM), American Association of State Highway and Transportation Officials, (2010).
3. Lee, J.Y., Chung, J.-H. and Son, B., "Analysis of Traffic Accident Size for Korean Highway Using Structural Equation Models", Accident Analysis and Prevention, Vol. 40, No. 6, pp. 1955–1963, (2008).
4. Sun, X., Li, Y., Magri, D., & Shirazi, H. H., "Application of Highway Safety Manual Draft Chapter: Louisiana Experience", Transportation Research Record: Journal of the Transportation Research Board, Vol.1950, No. 1, pp. 55-64, (2006).
5. Phimister, J. R., Bier, V. M., & Kunreuther, H. C. (Eds.), "Accident Precursor Analysis and Management: Reducing Technological Risk Through Diligence", National Academies Press, (2014).
6. Milton, J.C., Shankar, V.N. and Mannering, F.L. "Highway Accident Severities and the Mixed Logit Model: An Exploratory Empirical Analysis", Accident Analysis and Prevention, Vol.40, No. 1, pp. 260–266, (2008).
7. Ma, J., Kockelman, K.M. and Damien, P., "A Multivariate Poisson-lognormal Regression Model for Prediction of Crash Counts by Severity, Using Bayesian Methods", Accident Analysis and Prevention, Vol.40, No. 3, pp. 964-975, (2008).
8. Cafiso, S., La Cava, G., Leonardi, S., Montella, A., and Pappalardo, G, "Operative Procedures for Road Safety Inspections," Varsaw, Poland, (2012).
9. Anastasopoulos, C. and Mannering, F.L., "A Note on Modeling Vehicle Accident Frequencies with Random-parameters Count Models", Accident Analysis and Prevention, Vol. 41, No. 1, pp. 153–159, (2009).
10. Cafiso, S., Cava, G. L., & Montella, A., "Safety Index for Evaluation of Two-lane Rural Highways". Transportation Research Record: Journal of the Transportation Research Board, Vol. 2019, No.1, pp. 136-145, (2007).
11. Chang, L., "Analysis of Freeway Accident Frequencies: Negative Binomial Regression Versus Artificial Neural Network", Safety Science, Vol.43, No. 8, pp. 541–557, (2005).
12. Hermans, E., Van den Bossche, F., & Wets, G., "Combining Road Safety Information in A Performance Index", Accident Analysis & Prevention, Vol. 40, No. 4, pp. 1337-1344, (2008).
13. Zheng, L., and Meng, X., "An Approach to Predict Road Accident Frequencies: Application of Fuzzy Neural Network", 3rd International Conference on Road Safety and Simulation, September 14-16, Indianapolis, USA, (2011).
14. Mc Elhinney, C. P., Kumar, P., Cahalane, C., & McCarthy, T., "Initial Results from European Road Safety Inspection (EURSI) Mobile Mapping Project", In ISPRS Commission V Technical Symposium, pp. 440-445, (2010).
15. Zeng, Q., & Huang, H., "Bayesian Spatial Joint Modeling of Traffic Crashes on an Urban Road N-etwork", Accident Analysis & Prevention, Vol. 67, pp. 105-112, (2014).
CAPTCHA Image