توسعه مدل‌های منعطف کلان نگر پیش‌بینی فراوانی تصادفات با در نظرگیری وابستگی‌های فضایی و اثرات مشاهده نشده ناهمسان‌ساز: مطالعه موردی شهر مشهد

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی کارشناسی ارشد، گروه مهندسی عمران، دانشکده مهندسی، دانشگاه فردوسی مشهد، مشهد، ایران

2 استادیار، گروه مهندسی عمران، دانشکده مهندسی، دانشگاه فردوسی مشهد، مشهد ، ایران

3 استادیار، گروه ریاضی و آمار، دانشکده علوم پایه، دانشگاه نیشابور، نیشابور، ایران

چکیده

در راستای دستیابی به سیستم حمل‌ونقل ایمن و کاهش عواقب جبران‌ناپذیر ناشی از سوانح ترافیکی نیاز است تا موضوع ایمنی ترافیک در کنار سایر اهداف برنامه‌ریزی حمل‌ونقل مانند آلودگی هوا، اقتصادی-جمعیتی و غیره موردبررسی قرار گیرد. در سال‌های اخیر استفاده از مدل‌های آماری برای کمی سازی اثر پارامترهای برنامه‌ریزی حمل‌ونقل بر ایمنی ترافیک و به دنبال آن ایجاد ارتباط بین برنامه‌ریزی حمل‌ونقل و ایمنی ترافیک، موردتوجه برنامه­ریزان قرارگرفته است. هدف پژوهش حاضر، توسعه مدل‌های کلان نگر پیش‌بینی تصادفات است که در سطح کلان اثر طیفی از ویژگی‌های نواحی ترافیکی شهر مشهد را بر فراوانی تصادفات مدل می‌کند. بدین منظور، علاوه بر مدل پواسون که متداول‌ترین و پایه‌ای‌ترین مدل پیش‌بینی تصادفات است، مدل‌های پواسون-لگ‌نرمال  و اتورگرسیو شرطی نیز برای در نظر گرفتن اثر بیش پراکنشی اطلاعات و وابستگی‌های فضایی مورداستفاده قرارگرفته است. جهت مقایسه مدل‌های پیشنهادی از معیار اطلاع انحرافی (DIC) استفاده‌شده است. نتایج مقایسه مدل‌ها نشان می‌دهد که در نظر گرفتن اثرات مشاهده نشده ناهمسان‌ساز و وابستگی فضایی به ترتیب توسط مدل‌های پواسون-لگ‌نرمال  و اتورگرسیو شرطی به‌طور قابل‌توجهی عملکرد مدل‌ها را ارتقا می‌بخشد و مقدار معیار DIC را از 41/4623 در مدل پواسون به ترتیب به 82/2066 و 28/2055، کاهش می‌دهد. قابل‌ذکر است که مدل اتورگرسیو شرطی (BYM) بهترین عملکرد را دارا است (28/2055= DIC)، که اهمیت در نظر گرفتن وابستگی فضایی در مدل‌های پیش‌بینی تصادفات را در تصحیح تخمین و همچنین جایگزینی برای متغیرهای در نظر گرفته نشده، برجسته می‌سازد.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Development of flexible macro-level injury crash prediction model considering spatial correlation and unobserved heterogeneity: a case-study of Mashhad

نویسندگان [English]

  • Emad Soroori 1
  • Abolfazl Mohammadzadeh Moghadam 2
  • Mahdi Salehi 3
1 M.Sc. Student, Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Iran
2 Assistant Professor, Department of Civil Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashad, Iran
3 Assistant Professor, Department of Mathematics and Statistics, Faculty of Science, University of Neyshabur, Neyshabour, Iran
چکیده [English]

In order to achieve the objective of a safe transportation system and reducing the irreparable consequences of the road crashes, it is necessary to consider the traffic safety issues along with other transport planning factors such as the air pollution, socio-economic. Recently, statistical models to quantify the impact of transport planning factors on the traffic safety and, subsequently, to establish a link between the transportation planning and traffic safety have been considered by planners. The purpose of the current study is to develop the comprehensive macro-level crash prediction models by which the effect of a range of the characteristics of traffic analysis zones on the crash frequency could be considered. To do so, in addition to the Poisson model, which is the most common and basic crash prediction model, Poisson-Lognormal and conditional autoregressive models are also used to consider the unobserved heterogeneity and spatial correlations. The Deviance Information Criterion (DIC) has been used to compare the proposed models. The results of the comparison of the models indicate that considering the two factors of overdispersion and spatial correlations by the Poisson-Lognormal and conditional autoregressive models, respectively, significantly improves the performance of the models and reduces the value of DIC from 4623.41 in Poisson model to 2066.82 and 2055.28 in aforementioned models. It is also worth noting that the conditional autoregressive model has the best performance, which highlights the importance of considering the spatial dependence in the crash prediction models with rectification of the estimation and replacement of the omitted variables.

کلیدواژه‌ها [English]

  • Macro-level crash prediction model
  • Poisson-lognormal model
  • conditional autoregressive model (BYM)
  • unobserved heterogeneity
  • spatial correlation

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  • تاریخ دریافت: 07 اردیبهشت 1397
  • تاریخ بازنگری: 27 مرداد 1397
  • تاریخ پذیرش: 28 مرداد 1397

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