پژوهشنامه حمل و نقل

پژوهشنامه حمل و نقل

شناسایی عوامل موثر بر شدت تصادفات موتورسیکلت در راه‌های برون‌شهری با الگوریتم جنگل تصادفی (RF) و تحلیل SHAP

نوع مقاله : مقاله پژوهشی

نویسندگان
حامد سیفی 1
شاهین شعبانی 1
محمد کوهی 2
1 گروه مهندسی عمران، دانشگاه پیام نور، تهران، ایران
2 دانش آموخته کارشناسی ارشد، گروه مهندسی عمران‌، دانشگاه پیام نور، تهران، ایران
10.22034/tri.2026.567167.3419
چکیده
تصادفات موتورسیکلت به دلیل آسیب‌پذیری بالای این گروه از کاربران راه، سهم قابل توجهی از تلفات جاده‌ای را به خود اختصاص می‌دهند و شدت پیامدهای آن‌ها، به‌ویژه در راه‌های برون‌شهری، همواره یکی از چالش‌های اصلی ایمنی راه بوده است. هدف اصلی این پژوهش، شناسایی و تفسیر عوامل مؤثر بر شدت تصادفات موتورسیکلت در راه‌های برون‌شهری با استفاده از رویکردهای یادگیری ماشین تفسیرپذیر است. بدین منظور، از داده‌های تصادفات سه‌ساله (۱۳۹۶ تا ۱۳۹۸) موتورسیکلت در راه‌های برون‌شهری استان خراسان رضوی استفاده شد. متغیرهای مورد بررسی در قالب سیزده دسته شامل ویژگی‌های راه، وضعیت هندسی، نوع شانه، نقص مؤثر راه، عامل انسانی، نوع وسیله نقلیه مقصر، کاربری اراضی و سایر عوامل محیطی و ترافیکی تعریف شدند. در این پژوهش، سه الگوریتم یادگیری ماشین شامل جنگل تصادفی، XGBoost و LightGBM برای پیش‌بینی شدت تصادفات (خسارتی، جرحی و فوتی) به کار گرفته شدند. پس از پیش‌پردازش داده‌ها و متعادل‌سازی کلاس‌ها با استفاده از روش SMOTE، عملکرد مدل‌ها با معیارهای precision، recallو F1-score ارزیابی شد. نتایج نشان داد که مدل جنگل تصادفی با دقت کلی ۷۶ درصد، عملکرد بهتری نسبت به سایر مدل‌ها دارد و به‌عنوان مدل برتر انتخاب شد. به‌منظور تفسیر خروجی مدل منتخب، از روش SHAP استفاده گردید. نتایج تحلیل SHAP نشان داد که به ترتیب نوع شانه، نوع وسیله نقلیه مقصر، نقص مؤثر راه، عامل انسانی، کاربری اراضی، سرعت مجاز، نوع راه، وضعیت خط‌کشی و هندسه مسیر، مهم‌ترین عوامل مؤثر بر شدت تصادفات موتورسیکلت در راه‌های برون‌شهری هستند. یافته‌های این پژوهش نشان می‌دهد که شدت تصادفات موتورسیکلت حاصل برهم‌کنش پیچیده عوامل زیرساختی، رفتاری و وسیله نقلیه بوده و اتخاذ رویکردهای شدت‌محور در برنامه‌ریزی ایمنی راه می‌تواند نقش مؤثری در کاهش پیامدهای شدید تصادفات داشته باشد.
کلیدواژه‌ها
تصادفات موتورسیکلت
جنگل تصادفی (RF)
تحلیل SHAP
موضوعات

عنوان مقاله English

Identification of Factors Affecting Motorcycle Crash Severity on Intercity Roads Using the Random Forest (RF) Algorithm and SHAP Analysis

نویسندگان English

Hamed Saify 1
Shahin Shabani 1
Mohammad Koohi 2
1 Department of Civil Engineering‌, Payame Noor University (PNU), Tehran, Iran.
2 M.Sc., Grad., Department of Civil Engineering, Payame Noor University (PNU), Tehran, Iran.
چکیده English

Motorcycle crashes account for a substantial share of road traffic fatalities due to the high vulnerability of this group of road users, and the severity of their consequences—particularly on intercity roads—has consistently remained one of the major challenges in road safety. The primary objective of this study is to identify and interpret the factors influencing motorcycle crash severity on intercity roads using interpretable machine learning approaches. To this end, three-year motorcycle crash data (2017–2019) from intercity roads in Razavi Khorasan Province were utilized. The explanatory variables were classified into thirteen categories, including road characteristics, geometric conditions, shoulder type, contributing road defects, human factors, at-fault vehicle type, land use, and other environmental and traffic-related factors. In this study, three machine learning algorithms—Random Forest, XGBoost, and LightGBM—were employed to predict crash severity levels (property damage only, injury, and fatal). After data preprocessing and class balancing using the SMOTE method, model performance was evaluated using precision, recall, and F1-score metrics. The results indicated that the Random Forest model achieved superior performance compared to the other models, with an overall accuracy of 76%, and was therefore selected as the optimal model. To interpret the output of the selected model, the SHAP method was applied. The SHAP analysis revealed that shoulder type, at-fault vehicle type, contributing road defects, human factors, land use, posted speed limit, road type, pavement marking condition, and roadway geometry were, respectively, the most influential factors affecting motorcycle crash severity on intercity roads. The findings of this study demonstrate that motorcycle crash severity results from a complex interaction of infrastructural, behavioral, and vehicle-related factors, and that adopting severity-oriented approaches in road safety planning can play an effective role in reducing severe crash outcomes.

کلیدواژه‌ها English

Motorcycle Crashes
Random Forest (RF)
SHAP Analysis
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