عنوان

Estimation of Annual Average Daily Traffic (AADT) and missing hourly volume using artificial intelligence

شماره

TL49184

زبان متن نوشتاري يا گفتاري و مانند آن

انگلیسی

عنوان اصلي

Estimation of Annual Average Daily Traffic (AADT) and missing hourly volume using artificial intelligence

نام عام مواد

[Thesis]

نام نخستين پديدآور

Sababa Islam

نام ساير پديدآوران

Chowdhury, Mashrur

نام ناشر، پخش کننده و غيره

Clemson University

تاریخ نشرو بخش و غیره

2016

نام خاص و کميت اثر

135

متن يادداشت

Committee members: Luo, Feng; Sarasua, Wayne

متن يادداشت

Place of publication: United States, Ann Arbor; ISBN=978-1-369-55077-1

جزئيات پايان نامه و نوع درجه آن

M.Engr.

نظم درجات

Civil Engineering

کسي که مدرک را اعطا کرده

Clemson University

امتياز متن

2016

متن يادداشت

Annual Average Daily Traffic (AADT) is one of the most important traffic parameters used in transportation engineering analysis. Moreover, each state Department of Transportation (DOT) must report the AADT data to Federal Highway Administration (FHWA) annually as part of the Highway Performance Monitoring System (HPMS) requirements. For this reason, state DOTs continually collect AADT data via permanent count stations and short-term counts. In South Carolina, only interstates and primary routes are equipped with permanent count stations. For the majority of the secondary routes, AADT data are estimated based on short-term counts or are simply guesstimated based on their functional classifications. In this study the use of Artificial Neural Network (ANN) and Support Vector Regression (SVR) were applied to estimate AADT from short-term counts. The results were compared to the traditional factor method used by South Carolina Department of Transportation (SCDOT) and also to the Ordinary Least-square Regression method. The comparison between ANN and SVR revealed that SVR functions better than ANN in making AADT estimation for different functional classes. A second comparison was conducted between SVR and the traditional factor method. The comparative analysis revealed that SVR performed better that the traditional factor method. Similarly, the comparison between SVR and regression analysis for the principal arterials revealed no significant difference in the actual AADT and the AADT estimated through SVR. However, it did show a significant difference between the actual AADT and AADT estimated through regression analysis.

موضوع مستند نشده

Civil engineering; Transportation planning

اصطلاح موضوعی

Social sciences;Applied sciences

مستند نام اشخاص تاييد نشده

Yuzhu, Ge

مستند نام اشخاص تاييد نشده

Chowdhury, Mashrur

تقسيم فرعي

Civil Engineering

مستند نام تنالگان تاييد نشده

Clemson University

شماره بازیابی

1864672903; 10247462

نام الکترونيکي

فرمت انتشار

p

نوع ماده

[Thesis]

کد کاربرگه

276903

سطح دسترسي

a

تكميل شده

Y