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لمحة عن بعض الطرق المدروسة للتنبؤ بالكثافة المرورية
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ازدادت الحاجة لأنظمة التنبؤ المرورية وأصبحت حاجة ضرورية وملحة في أنظمة إدارة المرور المتقدمة، ذلك لأن توقع كثافة المرور يقلل الازدحام المروري ويسهل حركة السير. ومع وجود تنبؤ دقيق بحالة المرور سيكون بمقدورنا تطوير نظام إدارة مرورية متطور ونظام استعلامات متطور للمسافرين. التحدي الذي يواجه مشكلة نمذجة حالة المرور هو الخصائص المعقدة للعمليات المرورية العشوائية. معلومات التسلسل الزمني للكثافة المرورية، والسرعات، والتمركز المروري والتي يتم جمعها من مواقع مختلفة تمتلك خصائص مختلفة عن بعضها، وبذلك عملية التنبؤ بالكثافة المرورية المستقبلية ليست عملية بديهية، ويناقش هذا البحث عدة طرق قامت بتقديم حلول لهذه المشكلة.
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References used
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لتوصيل المعلومات بوضوح وكفاءة ، يستخدم تمثيل البيانات رسومات إحصائية ومعلومات وأدوات أخرى. قد يتم تشفير البيانات الرقمية باستخدام نقاط أو خطوط أو أشرطة لتوصيل المعلومات بصريًا. التمثيل الفعال يساعد المستخدمين على تحليل البيانات وتفسيرها، فهو يجعل الب
يانات المعقدة سهلة الوصول ومفهومة وقابلة للاستخدام. تُستخدم الجداول عمومًا حيث يبحث المستخدمون عن قياس محدد ، بينما تُستخدم المخططات ذات الأنواع المختلفة لإظهار أنماط أو علاقات في البيانات لمتغير واحد أو أكثر.
Traffic Conflict Technique TCT has a long history in traffic safety researches. Traffic accidents are now
well known to generate a serious problem that exhausts enormous resources of the national economy
either directly or indirectly. Because signa
lized intersections are potential black spot locations that may
cause too many accident this research uses the traffic conflict techniques for analyzing accidents at
signalized intersections.
In this research, TCT was used at four-leg signalized intersections in Damascus to evaluate safety at these
intersections. The relationship between conflicts and accidents was estimated and the results indicated
that the conflicts and the accidents are related to each other by a linear regression. Results also showed
that the correlation was unclear between conflicts and entry volumes at the intersections. Additionally,
priority ranking of these intersections was developed based on risk index that depends on injury level.
When learned without exploration, local models for structured prediction tasks are subject to exposure bias and cannot be trained without detailed guidance. Active Imitation Learning (AIL), also known in NLP as Dynamic Oracle Learning, is a general t
echnique for working around these issues by allowing the exploration of different outputs at training time. AIL requires oracle feedback: an oracle is any algorithm which can, given a partial candidate solution and gold annotation, find the correct (minimum loss) next output to produce. This paper describes a general finite state technique for deriving oracles. The technique describe is also efficient and will greatly expand the tasks for which AIL can be used.
While Yu and Poesio (2020) have recently demonstrated the superiority of their neural multi-task learning (MTL) model to rule-based approaches for bridging anaphora resolution, there is little understanding of (1) how it is better than the rule-based
approaches (e.g., are the two approaches making similar or complementary mistakes?) and (2) what should be improved. To shed light on these issues, we (1) propose a hybrid rule-based and MTL approach that would enable a better understanding of their comparative strengths and weaknesses; and (2) perform a manual analysis of the errors made by the MTL model.
This paper focuses on paraphrase generation,which is a widely studied natural language generation task in NLP. With the development of neural models, paraphrase generation research has exhibited a gradual shift to neural methods in the recent years.
This has provided architectures for contextualized representation of an input text and generating fluent, diverseand human-like paraphrases. This paper surveys various approaches to paraphrase generation with a main focus on neural methods.
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