High-Speed Data-Driven Methodology for Real-Time Traffic Flow Predictions: Practical Applications of ITS
المؤلفون المشاركون
المصدر
Journal of Advanced Transportation
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-04-24
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Despite the achievements of academic research on data-driven k-nearest neighbour nonparametric regression (KNN-NPR), the low-speed computational capability of the KNN-NPR method, which can occur during searches involving enormous amounts of historical data, remains a major obstacle to improvements of real-system applications.
To overcome this critical issue successfully, a high-speed KNN-NPR framework, capable of generating short-term traffic volume predictions, is proposed in this study.
The proposed method is based on a two-step search algorithm, which has the two roles of building promising candidates for input data during nonprediction times and identifying decision-making input data for instantaneous predictions at the prediction point.
To prove the efficacy of the proposed model, an experimental test was conducted with large-size traffic volume data.
It was found that the performance of the model not only at least equals that of linear-search-based KNN-NPR in terms of prediction accuracy, but also shows a substantially reduced execution time in approximating real-time applications.
This result suggests that the proposed algorithm can be also effectively employed as a preprocess to select useful past cases for advanced learning-based forecasting models.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Chang, Hyun-ho& Yoon, Byoung-jo. 2018. High-Speed Data-Driven Methodology for Real-Time Traffic Flow Predictions: Practical Applications of ITS. Journal of Advanced Transportation،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1181434
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Chang, Hyun-ho& Yoon, Byoung-jo. High-Speed Data-Driven Methodology for Real-Time Traffic Flow Predictions: Practical Applications of ITS. Journal of Advanced Transportation No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1181434
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Chang, Hyun-ho& Yoon, Byoung-jo. High-Speed Data-Driven Methodology for Real-Time Traffic Flow Predictions: Practical Applications of ITS. Journal of Advanced Transportation. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1181434
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1181434
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