![](/images/graphics-bg.png)
Vehicle Trajectory Reconstruction for Signalized Intersections with Low-Frequency Floating Car Data
المؤلفون المشاركون
Ochieng, Washington Y.
Wang, Hua
Gu, Changlong
المصدر
Journal of Advanced Transportation
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-14، 14ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-05-20
دولة النشر
مصر
عدد الصفحات
14
التخصصات الرئيسية
الملخص EN
Floating car data are beneficial in estimating traffic conditions in wide areas and are playing an increasing role in traffic surveillance.
However, widespread application is limited by low-sample frequency which makes it hard to get a complete picture of a vehicle’s motion.
An accurate and reliable reconstruction of a vehicle’s trajectory could effectively result in a higher sampling frequency enabling a more accurate estimation of road traffic parameters.
Existing methods require additional information such as nearby vehicles, signal timing strategies, and queue patterns which are not always available.
To address this problem, this paper presents a method used with low-sample frequency data to reconstruct vehicle trajectories through intersections, without the need for extra information.
Furthermore, the additional parameters for the speed-time curve distributions for deceleration rate and acceleration rate are generated.
A piecewise deceleration and acceleration model is developed to calculate the acceleration rate for different travel modes in the trajectory.
The distribution parameters of the acceleration data for each travel mode are then estimated using a new Expectation Maximization (EM) algorithm.
The acceleration statistics are then used to reconstruct the corresponding parts of the trajectory.
Compared to the reference trajectories (truth), the test results show that the method developed in this paper achieves improvement in accuracy ranging from 16 to 67% over the commonly used linear interpolation method.
In addition, the proposed method is not very sensitive to the sampling interval of the floating car data, unlike the linear interpolation method where the error grows rapidly with increasing sampling interval.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Hua& Gu, Changlong& Ochieng, Washington Y.. 2019. Vehicle Trajectory Reconstruction for Signalized Intersections with Low-Frequency Floating Car Data. Journal of Advanced Transportation،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1170329
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Hua…[et al.]. Vehicle Trajectory Reconstruction for Signalized Intersections with Low-Frequency Floating Car Data. Journal of Advanced Transportation No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1170329
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Hua& Gu, Changlong& Ochieng, Washington Y.. Vehicle Trajectory Reconstruction for Signalized Intersections with Low-Frequency Floating Car Data. Journal of Advanced Transportation. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1170329
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1170329
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
![](/images/ebook-kashef.png)
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
![](/images/kashef-image.png)