Traffic Data Imputation Algorithm Based on Improved Low-Rank Matrix Decomposition
المؤلفون المشاركون
Luo, Xianglong
Meng, Xue
Gan, Wenjuan
Chen, Yonghong
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-07-01
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Traffic data plays a very important role in Intelligent Transportation Systems (ITS).
ITS requires complete traffic data in transportation control, management, guidance, and evaluation.
However, the traffic data collected from many different types of sensors often includes missing data due to sensor damage or data transmission error, which affects the effectiveness and reliability of ITS.
In order to ensure the quality and integrity of traffic flow data, it is very important to propose a satisfying data imputation method.
However, most of the existing imputation methods cannot fully consider the impact of sensor data with data missing and the spatiotemporal correlation characteristics of traffic flow on imputation results.
In this paper, a traffic data imputation method is proposed based on improved low-rank matrix decomposition (ILRMD), which fully considers the influence of missing data and effectively utilizes the spatiotemporal correlation characteristics among traffic data.
The proposed method uses not only the traffic data around the sensor including missing data, but also the sensor data with data missing.
The information of missing data is reflected into the coefficient matrix, and the spatiotemporal correlation characteristics are applied in order to obtain more accurate imputation results.
The real traffic data collected from the Caltrans Performance Measurement System (PeMS) are used to evaluate the imputation performance of the proposed method.
Experiment results show that the average imputation accuracy with proposed method can be improved 87.07% compared with the SVR, ARIMA, KNN, DBN-SVR, WNN, and traditional MC methods, and it is an effective method for data imputation.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Luo, Xianglong& Meng, Xue& Gan, Wenjuan& Chen, Yonghong. 2019. Traffic Data Imputation Algorithm Based on Improved Low-Rank Matrix Decomposition. Journal of Sensors،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1191482
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Luo, Xianglong…[et al.]. Traffic Data Imputation Algorithm Based on Improved Low-Rank Matrix Decomposition. Journal of Sensors No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1191482
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Luo, Xianglong& Meng, Xue& Gan, Wenjuan& Chen, Yonghong. Traffic Data Imputation Algorithm Based on Improved Low-Rank Matrix Decomposition. Journal of Sensors. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1191482
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1191482
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