Predicting Critical Bicycle-Vehicle Conflicts at Signalized Intersections
المؤلفون المشاركون
Darzian Rostami, Alireza
Katthe, Anagha
Sohrabi, Aryan
Jahangiri, Arash
المصدر
Journal of Advanced Transportation
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-16، 16ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-12-03
دولة النشر
مصر
عدد الصفحات
16
التخصصات الرئيسية
الملخص EN
Continuous development of urban infrastructure with a focus on sustainable transportation has led to a proliferation of vulnerable road users (VRUs), such as bicyclists and pedestrians, at intersections.
Intersection safety evaluation has primarily relied on historical crash data.
However, due to several limitations, including rarity, unpredictability, and irregularity of crash occurrences, quantitative and qualitative analyses of crashes may not be accurate.
To transcend these limitations, intersection safety can be proactively evaluated by quantifying near-crashes using alternative measures known as surrogate safety measures (SSMs).
This study focuses on developing models to predict critical near-crashes between vehicles and bicycles at intersections based on SSMs and kinematic data.
Video data from ten signalized intersections in the city of San Diego were employed to train logistic regression (LR), support vector machine (SVM), and random forest (RF) models.
A variation of time-to-collision called T2 and postencroachment time (PET) were used to specify monitoring periods and to identify critical near-crashes, respectively.
Four scenarios were created using two thresholds of 5 and 3 s for both PET and T2.
In each scenario, five monitoring period lengths were examined.
The RF model was superior compared to other models in all different scenarios and across different monitoring period lengths.
The results also showed a small trade-off between model performance and monitoring period length, identifying models with monitoring period lengths of 10 and 20 frames performed slightly better than those with lower or higher lengths.
Sequential backward and forward feature selection methods were also applied that enhanced model performance.
The best RF model had recall values of 85% or higher across all scenarios.
Also, RF prediction models performed better when considering just the rear-end near-crashes with recalls of above 90%.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Darzian Rostami, Alireza& Katthe, Anagha& Sohrabi, Aryan& Jahangiri, Arash. 2020. Predicting Critical Bicycle-Vehicle Conflicts at Signalized Intersections. Journal of Advanced Transportation،Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1176197
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Darzian Rostami, Alireza…[et al.]. Predicting Critical Bicycle-Vehicle Conflicts at Signalized Intersections. Journal of Advanced Transportation No. 2020 (2020), pp.1-16.
https://search.emarefa.net/detail/BIM-1176197
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Darzian Rostami, Alireza& Katthe, Anagha& Sohrabi, Aryan& Jahangiri, Arash. Predicting Critical Bicycle-Vehicle Conflicts at Signalized Intersections. Journal of Advanced Transportation. 2020. Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1176197
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1176197
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر