عنوان

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

Number

TL49184

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

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

General Material Designation

[Thesis]

First Statement of Responsibility

Sababa Islam

Subsequent Statement of Responsibility

Chowdhury, Mashrur

Name of Publisher, Distributor, etc.

Clemson University

Date of Publication, Distribution, etc.

2016

Specific Material Designation and Extent of Item

135

Text of Note

Committee members: Luo, Feng; Sarasua, Wayne

Text of Note

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

Dissertation or thesis details and type of degree

M.Engr.

Discipline of degree

Civil Engineering

Body granting the degree

Clemson University

Text preceding or following the note

2016

Text of Note

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.

Civil engineering; Transportation planning

Subject Term

Social sciences;Applied sciences

Yuzhu, Ge

Chowdhury, Mashrur

Subdivision

Civil Engineering

Clemson University

Call Number

1864672903; 10247462

Electronic name

p

[Thesis]

276903

a

Y